Showing papers in "Journal of Electronic Imaging in 2011"

Color demosaicking by local directional interpolation and nonlocal adaptive thresholding

Abstract: Single sensor digital color cameras capture only one of the three primary colors at each pixel and a process called color demosaicking (CDM) is used to reconstruct the full color images. Most CDM algorithms assume the existence of high local spectral redundancy in estimating the missing color samples. However, for images with sharp color transitions and high color saturation, such an assumption may be invalid and visually unpleasant CDM errors will occur. In this paper, we exploit the image nonlocal redundancy to improve the local color reproduction result. First, multiple local direc- tional estimates of a missing color sample are computed and fused according to local gradients. Then, nonlocal pixels similar to the esti- mated pixel are searched to enhance the local estimate. An adaptive thresholding method rather than the commonly used nonlocal means filtering is proposed to improve the local estimate. This allows the final reconstruction to be performed at the structural level as op- posed to the pixel level. Experimental results demonstrate that the proposed local directional interpolation and nonlocal adaptive thresh- olding method outperforms many state-of-the-art CDM methods in reconstructing the edges and reducing color interpolation artifacts, leading to higher visual quality of reproduced color images. © 2011

Discrete Fourier transform-based watermarking method with an optimal implementation radius

Abstract: In this paper, we evaluate the degradation of an image due to the implementation of a watermark in the frequency domain of the image. As a result, a watermarking method, which minimizes the impact of the watermark implementation on the overall quality of an image, is developed. The watermark is embedded in magnitudes of the Fourier transform. A peak signal-to-noise ratio is used to evaluate quality degradation. The obtained results were used to develop a watermarking strategy that chooses the optimal radius of the implementation to minimize quality degradation. The robustness of the proposed method was evaluated on the dataset of 1000 images. Detection rates and receiver operating characteristic performance showed considerable robustness against the print-scan process, print-cam process, amplitude modulated, halftoning, and attacks from the StirMark benchmark software.

Theoretical and experimental comparison of different approaches for color texture classification

Abstract: Color texture classification has been an area of intensive research activity. From the very onset, approaches to combining color and texture have been the subject of much discussion, and in particular, whether they should be considered joint or separately. We present a comprehensive comparison of the most prominent approaches both from a theoretical and experimental standpoint. The main contributions of our work are: (i) the establishment of a generic and extensible framework to classify methods for color texture classification on a mathematical basis, and (ii) a theoretical and experimental comparison of the most salient existing methods. Starting from an extensive set of experiments based on the Outex dataset, we highlight those texture descriptors that provide good accuracy along with low dimensionality. The results suggest that separate color and texture processing is the best practice when one seeks for optimal compromise between accuracy and limited number of features. We believe that our work may serve as a guide for those who need to choose the appropriate method for a specific application, as well as a basis for the development of new methods.

Fourier Methods in Imaging

Abstract: Series Editor s Preface. Preface. 1 Introduction. 1.1 Signals, Operators, and Imaging Systems. 1.2 The Three Imaging Tasks. 1.3 Examples of Optical Imaging. 1.4 ImagingTasks inMedical Imaging. 2 Operators and Functions. 2.1 Classes of Imaging Operators. 2.2 Continuous and Discrete Functions. Problems. 3 Vectors with Real-Valued Components. 3.1 Scalar Products. 3.2 Matrices. 3.3 Vector Spaces. Problems. 4 Complex Numbers and Functions. 4.1 Arithmetic of Complex Numbers. 4.2 Graphical Representation of Complex Numbers. 4.3 Complex Functions. 4.4 Generalized Spatial Frequency Negative Frequencies. 4.5 Argand Diagrams of Complex-Valued Functions. Problems. 5 Complex-Valued Matrices and Systems. 5.1 Vectors with Complex-Valued Components. 5.2 Matrix Analogues of Shift-Invariant Systems. 5.3 Matrix Formulation of ImagingTasks. 5.4 Continuous Analogues of Vector Operations. Problems. 6 1-D Special Functions. 6.1 Definitions of 1-D Special Functions. 6.2 1-D Dirac Delta Function. 6.3 1-D Complex-Valued Special Functions. 6.4 1-D Stochastic Functions Noise. 6.5 Appendix A: Area of SINC[x] and SINC2[x]. 6.6 Appendix B: Series Solutions for Bessel Functions J0[x] and J1[x]. Problems. 7 2-D Special Functions. 7.1 2-D Separable Functions. 7.2 Definitions of 2-D Special Functions. 7.3 2-D Dirac Delta Function and its Relatives. 7.4 2-D Functions with Circular Symmetry. 7.5 Complex-Valued 2-D Functions. 7.6 Special Functions of Three (orMore) Variables. Problems. 8 Linear Operators. 8.1 Linear Operators. 8.2 Shift-Invariant.Operators. 8.3 Linear Shift-Invariant (LSI) Operators. 8.4 Calculating Convolutions. 8.5 Properties of Convolutions. 8.6 Autocorrelation. 8.7 Crosscorrelation. 8.8 2-DLSIOperations. 8.9 Crosscorrelations of 2-D Functions. 8.10 Autocorrelations of 2-D.Functions. Problems. 9 Fourier Transforms of 1-D Functions. 9.1 Transforms of Continuous-Domain Functions. 9.2 Linear Combinations of Reference Functions. 9.3 Complex-Valued Reference Functions. 9.4 Transforms of Complex-Valued Functions. 9.5 Fourier Analysis of Dirac Delta Functions. 9.6 Inverse Fourier Transform. 9.7 Fourier Transforms of 1-D Special Functions. 9.8 Theorems of the Fourier Transform. 9.9 Appendix: Spectrum of Gaussian via Path Integral. Problems. 10 Multidimensional Fourier Transforms. 10.1 2-D Fourier Transforms. 10.2 Spectra of Separable 2-D Functions. 10.3 Theorems of 2-D Fourier Transforms. Problems. 11 Spectra of Circular Functions. 11.1 The Hankel Transform. 11.2 Inverse Hankel Transform. 11.3 Theorems of Hankel Transforms. 11.4 Hankel Transforms of Special Functions. 11.5 Appendix: Derivations of Equations (11.12) and (11.14). Problems. 12 The Radon Transform. 12.1 Line-Integral Projections onto Radial Axes. 12.2 Radon Transforms of Special Functions. 12.3 Theorems of the Radon Transform. 12.4 Inverse Radon Transform. 12.5 Central-Slice Transform. 12.6 Three Transforms of Four Functions. 12.7 Fourier and Radon Transforms of Images. Problems. 13 Approximations to Fourier Transforms. 13.1 Moment Theorem. 13.2 1-D Spectra via Method of Stationary Phase. 13.3 Central-Limit Theorem. 13.4 Width Metrics and Uncertainty Relations. Problems. 14 Discrete Systems, Sampling, and Quantization. 14.1 Ideal Sampling. 14.2 Ideal Sampling of Special Functions. 14.3 Interpolation of Sampled Functions. 14.4 Whittaker Shannon Sampling Theorem. 14.5 Aliasingand Interpolation. 14.6 Prefiltering to Prevent Aliasing. 14.7 Realistic Sampling. 14.8 Realistic Interpolation. 14.9 Quantization. 14.10 Discrete Convolution. Problems. 15 Discrete Fourier Transforms. 15.1 Inverse of the Infinite-Support DFT. 15.2 DFT over Finite Interval. 15.3 Fourier Series Derived from Fourier Transform. 15.4 Efficient Evaluation of the Finite DFT. 15.5 Practical Considerations for DFT and FFT. 15.6 FFTs of 2-D Arrays. 15.7 Discrete Cosine Transform. Problems. 16 Magnitude Filtering. 16.1 Classes of Filters. 16.2 Eigenfunctions of Convolution. 16.3 Power Transmission of Filters. 16.4 Lowpass Filters. 16.5 Highpass Filters. 16.6 Bandpass Filters. 16.7 Fourier Transform as a Bandpass Filter. 16.8 Bandboost and Bandstop Filters. 16.9 Wavelet Transform. Problems. 17 Allpass (Phase) Filters. 17.1 Power-Series Expansion for Allpass Filters. 17.2 Constant-Phase Allpass Filter. 17.3 Linear-Phase Allpass Filter. 17.4 Quadratic-Phase Filter. 17.5 Allpass Filters with Higher-Order Phase. 17.6 Allpass Random-Phase Filter. 17.7 Relative Importance of Magnitude and Phase. 17.8 Imaging of Phase Objects. 17.9 Chirp Fourier Transform. Problems. 18 Magnitude Phase Filters. 18.1 Transfer Functions of Three Operations. 18.2 Fourier Transform of Ramp Function. 18.3 Causal Filters. 18.4 Damped Harmonic Oscillator. 18.5 Mixed Filters with Linear or Random Phase. 18.6 Mixed Filter with Quadratic Phase. Problems. 19 Applications of Linear Filters. 19.1 Linear Filters for the Imaging Tasks. 19.2 Deconvolution Inverse Filtering . 19.3 Optimum Estimators for Signals in Noise. 19.4 Detection of Known Signals Matched Filter. 19.5 Analogies of Inverse and Matched Filters. 19.6 Approximations to Reciprocal Filters. 19.7 Inverse Filtering of Shift-Variant Blur. Problems. 20 Filtering in Discrete Systems. 20.1 Translation, Leakage, and Interpolation. 20.2 Averaging Operators Lowpass Filters. 20.3 Differencing Operators Highpass Filters. 20.4 Discrete Sharpening Operators. 20.5 2-DGradient. 20.6 Pattern Matching. 20.7 Approximate Discrete Reciprocal Filters. Problems. 21 Optical Imaging in Monochromatic Light. 21.1 Imaging Systems Based on Ray Optics Model. 21.2 Mathematical Model of Light Propagation. 21.3 Fraunhofer Diffraction. 21.4 Imaging System based on Fraunhofer Diffraction. 21.5 Transmissive Optical Elements. 21.6 Monochromatic Optical Systems. 21.7 Shift-Variant Imaging Systems. Problems. 22 Incoherent Optical Imaging Systems. 22.1 Coherence. 22.2 Polychromatic Source Temporal Coherence. 22.3 Imaging in Incoherent Light. 22.4 System Function in Incoherent Light. Problems. 23 Holography. 23.1 Fraunhofer Holography. 23.2 Holography in Fresnel Diffraction Region. 23.3 Computer-Generated Holography. 23.4 Matched Filtering with Cell-Type CGH. 23.5 Synthetic-Aperture Radar (SAR). Problems. References. Index.

Robust wavelet-based video watermarking scheme for copyright protection using the human visual system

Abstract: The development of the information technology and computer networks facilitates easy duplication, manipulation, and distribution of digital data. Digital watermarking is one of the proposed solutions for effectively safeguarding the rightful ownership of digital images and video. We propose a public digital watermarking technique for video copyright protection in the discrete wavelet transform domain. The scheme uses binary images as watermarks. These are embedded in the detail wavelet coefficients of the middle wavelet subbands. The method is a combination of spread spectrum and quantization-based watermarking. Every bit of the watermark is spread over a number of wavelet coefficients with the use of a secret key by means of quantization. The selected wavelet detail coefficients from different subbands are quantized using an optimal quantization model, based on the characteristics of the human visual system (HVS). Our HVS-based scheme is compared to a non-HVS approach. The resilience of the watermarking algorithm is tested against a series of different spatial, temporal, and compression attacks. To improve the robustness of the algorithm, we use error correction codes and embed the watermark with spatial and temporal redundancy. The proposed method achieves a good perceptual quality and high resistance to a large spectrum of attacks.

Image encryption by using gyrator transform and Arnold transform

Abstract: We propose an optical image encryption algorithm based on Arnold transform and gyrator transform. The amplitude and phase, which are the outputs of gyrator transform, are separated into several sub-images. Arnold transform is introduced for scrambling the data of the sub-images. The random spectrum composed of the scrambled sub-images is transformed by gyrator transform. An iterative structure of the algorithm is designed for enhancing the security of the encryption algorithm. The parameters of gyrator transforms and separating scheme serve as the key of the encryption method. The encryption process can be implemented by an electro-optical setup. Some numerical simulations have been given to demonstrate the security and validity of this algorithm.

Single-image super-resolution based on Markov random field and contourlet transform

Abstract: Learning-based methods are well adopted in image super-resolution. In this paper, we propose a new learning-based approach using contourlet transform and Markov random field. The proposed algorithm employs contourlet transform rather than the conventional wavelet to represent image features and takes into account the correlation between adjacent pixels or image patches through the Markov random field (MRF) model. The input low-resolution (LR) image is decomposed with the contourlet transform and fed to the MRF model together with the contourlet transform coefficients from the low- and high-resolution image pairs in the training set. The unknown high-frequency components/coefficients for the input low-resolution image are inferred by a belief propagation algorithm. Finally, the inverse contourlet transform converts the LR input and the inferred high-frequency coefficients into the super-resolved image. The effectiveness of the proposed method is demonstrated with the experiments on facial, vehicle plate, and real scene images. A better visual quality is achieved in terms of peak signal to noise ratio and the image structural similarity measurement.

Refinement of Depth Maps by Fusion of Multiple Estimates

Abstract: A method, system, and computer-readable storage medium are disclosed for generating fused depth maps. A plurality of initial depth maps are generated for a first image of a stereo pair, using at least two separate and distinct techniques for depth estimation. A plurality of initial depth maps are generated for a second image of the stereo pair, using at least two separate and distinct techniques for depth estimation. A fused depth map is generated for the first image based on the plurality of initial depth maps for the first image and the plurality of initial depth maps for the second image. A fused depth map is generated for the second image based on the plurality of initial depth maps for the second image and the plurality of initial depth maps for the first image.

Automatic visual inspection and defect detection on variable data prints

Abstract: We present a system for automatic, on-line visual inspection and print defect detection for variable data printing (VDP). This system can be used to automatically stop the printing process and alert the operator to problems. We lay out the components required for constructing a vision-based inspection system and show that our approach is novel for the high-speed detection of defects on variable data. When implemented in a high-speed digital printing press, the system allows a single skilled operator to monitor and maintain several presses, reducing the number of operators required to run a shop floor of presses as well as reduce wasted consumables when a defect goes undetected.

Toward a digital camera to rival the human eye

Abstract: All things considered, electronic imaging systems do not ri- val the human visual system despite notable progress over 40 years since the invention of the CCD. This work presents a method that allows design engineers to evaluate the performance gap between a digital camera and the human eye. The method identifies limit- ing factors of the electronic systems by benchmarking against the human system. It considers power consumption, visual field, spatial resolution, temporal resolution, and properties related to signal and noise power. A figure of merit is defined as the performance gap of the weakest parameter. Experimental work done with observers and cadavers is reviewed to assess the parameters of the human eye, and assessment techniques are also covered for digital cam- eras. The method is applied to 24 modern image sensors of various types, where an ideal lens is assumed to complete a digital camera. Results indicate that dynamic range and dark limit are the most lim- iting factors. The substantial functional gap, from 1.6 to 4.5 orders of magnitude, between the human eye and digital cameras may arise from architectural differences between the human retina, arranged in a multiple-layer structure, and image sensors, mostly fabricated in planar technologies. Functionality of image sensors may be signifi- cantly improved by exploiting technologies that allow vertical stacking of active tiers. © 2011 SPIE and IS&T. (DOI: 10.1117/1.3611015)

Moving object detection in the presence of dynamic backgrounds using intensity and textural features

Abstract: Moving object detection in the presence of dynamic backgrounds remains a challenging problem in video surveillance. Earlier work established that the background subtraction technique based on a covariance matrix descriptor is effective and robust for dynamic backgrounds. The work proposed herein extends this concept further, using the covariance-matrix descriptor derived from local textural properties, instead of directly computing from the local image features. The proposed approach models each pixel with a covariance matrix and a mean feature vector and the model is dynamically updated. We made extensive studies with the proposed technique to demonstrate the effectiveness of statistics on local textural properties.

Enhancement of the asymmetry-based overlapping analysis through features extraction

Abstract: In this paper, an enhanced algorithm is proposed to detect foot inflammation and, hence, predict ulcers before they can develop. This algorithm is based on an asymmetry analysis combined with a segmentation technique with a genetic algorithm to achieve higher efficiency in the detection of inflammation. The analysis involves several steps: segmentation, geometry transformation, overlapping, and abnormality identification. To enhance the results of this analysis, an additional step, features extraction, is performed. In this step, low and high order statistics are computed for each foot. Preliminary results show that the proposed algorithm combined with features extraction can be reliable and efficient to predict potential ulceration.

New framework for using image contents in blind steganalysis systems

Abstract: Although current blind image steganalysis systems utilize a wide variety of features and classifiers, a common shortcoming in all of them is that they almost have similar processes for all images and they do not take advantage of the content diversity of different images. In this paper, a new framework is proposed that enables us to employ the content of images in these systems. All blind image steganalysis methods can be adapted to the proposed framework. In the training phase of our framework, the input images are first divided into classes according to an image content evaluation criterion and then the training process is specialized for each class. In the testing phase, a fuzzy approach is used to include different classes in the decision making process. Experimental results demonstrate that the proposed framework significantly enhance the detection accuracy of these systems.

Calibrating light sources by using a planar mirror

Abstract: Light source calibration is an important issue in many com- puter vision fields such as photometric stereo and shape from shad- ing. Spheres, with either diffuse or specular reflections, are frequently deployed as calibration objects to recover the direction and intensity of light source from images. We present a novel method for light source calibration by using a planar mirror with a chessboard pattern and a diffuse region. The light direction can be accurately estimated from one mirror orientation by recovering the normal direction of the mirror plane. The location and intensity of light source can be further estimated if two mirror orientations are used. Experimental results show that the calibration accuracy of the proposed method is much higher than traditional sphere-based techniques and can of- fer improved three-dimensional reconstruction in photometric stereo. © 2011 SPIE and IS&T. (DOI: 10.1117/1.3533326)

High capacity reversible watermarking using differential histogram shifting and predicted error compensation

Abstract: Reversible watermarking inserts watermarks into digital media in such a way that visual transparency is preserved, which enables the restoration of the original media from the watermarked one without any loss of media quality. It has various applications where high capacity and high visual quality are major requirements for reversible watermarking. This work presents a new reversible watermarking algorithm that embeds message bits by modifying the differential histogram of adjacent pixels. To satisfy both high embedding capacity and visual quality, the proposed technique exploits the fact that the adjacent pixels are highly correlated. Also, we prevent overflow and underflow problems by designing a predicted error compensation scheme. Through experiments using multiple kinds of test images, we prove that the presented algorithm provides 100% reversibility, higher capacity, and higher visual quality than any previous method, while maintaining low induced distortion.

Depth adjustment for stereoscopic images and subjective preference evaluation

Abstract: Visual fatigue is a common problem when viewing stereoscopic images. We propose a depth adjustment method that controls the amount of disparity in stereoscopic images using visual fatigue prediction and conducted subjective preference evaluation based on individual fusional response characteristics. Visual fatigue level is predicted by examining the characteristics of horizontal disparity. Viewers are classified into two groups (those with normal susceptibility to visual fatigue and those with high susceptibility to visual fatigue) according to individual fusional limit and speed of fusion, which are determined using a random dot stereogram test. Subjective preferences for the amount of depth adjustment are investigated based on the degree of fusion ability. Our experimental results support the suitability of the proposed depth adjustment method for reducing visual fatigue.

Main subject detection via adaptive feature refinement

Abstract: Main subject detection (MSD) refers to the task of determining which spatial regions in an image correspond to the most visually relevant or scene-defining object(s) for general viewing purposes. This task, while trivial for a human, remains extremely challenging for a computer. Here, we present an algorithm for MSD which operates by adaptively refining low-level features. The algorithm computes, in a block-based fashion, five feature maps corresponding to lightness distance, color distance, contrast, local sharpness, and edge strength. These feature maps are adaptively combined and gradually refined via three stages. The final combination of the refined feature maps produces an estimate of the main subject's location. We tested the proposed algorithm on two extensive image databases. Our results show that relatively simple, low-level features, when used in an adaptive and iterative fashion, can be very effective at MSD.

Using image entropy maximum for auto exposure

Abstract: To achieve auto exposure in digital cameras, image brightness is widely used because of its direct relationship with exposure value. To use image entropy as an alternative statistic to image brightness, it is required to establish how image entropy changes as exposure value is varied. This paper presents a mathematical proof along with experimental verification results to show that image entropy reaches a maximum value as exposure value is varied by changing shutter speed or aperture size.

Adaptive level-set evolution without initial contours for image segmentation

Abstract: We propose a novel variational formulation (external energy) for implicit active contours that forces the level-set function to have the opposite sign along the edges at convergence. This external energy is then incorporated into a variational level-set formulation with two extra regularization terms (internal energy). The resulting evolution of the level-set function is the gradient flow that minimizes the overall energy functional. Because of the external energy, the level-set function can be initialized to any bounded function (e.g., a constant function), which completely eliminates the need of initial contours. This implies that the new formulation is robust to initialization or even free of manual initialization. The proposed model is been applied to both real and synthetic images with promising results. Besides, our model is especially appropriate for segmentation of real images with a complex and /or nonuniform background.

Compute-unified device architecture implementation of a block-matching algorithm for multiple graphical processing unit cards

Abstract: We describe and evaluate a fast implementation of a classical block-matching motion estimation algorithm for multiple graphical processing units (GPUs) using the compute unified device architecture computing engine. The implemented block-matching algorithm uses summed absolute difference error criterion and full grid search (FS) for finding optimal block displacement. In this evaluation, we compared the execution time of a GPU and CPU implementation for images of various sizes, using integer and noninteger search grids. The results show that use of a GPU card can shorten computation time by a factor of 200 times for integer and 1000 times for a noninteger search grid. The additional speedup for a noninteger search grid comes from the fact that GPU has built-in hardware for image interpolation. Further, when using multiple GPU cards, the presented evaluation shows the importance of the data splitting method across multiple cards, but an almost linear speedup with a number of cards is achievable. In addition, we compared the execution time of the proposed FS GPU implementation with two existing, highly optimized nonfull grid search CPU-based motion estimations methods, namely implementation of the Pyramidal Lucas Kanade Optical flow algorithm in OpenCV and simplified unsymmetrical multi-hexagon search in H.264/AVC standard. In these comparisons, FS GPU implementation still showed modest improvement even though the computational complexity of FS GPU implementation is substantially higher than non-FS CPU implementation. We also demonstrated that for an image sequence of 720 × 480 pixels in resolution commonly used in video surveillance, the proposed GPU implementation is sufficiently fast for real-time motion estimation at 30 frames-per-second using two NVIDIA C1060 Tesla GPU cards.

Lossless and lossy coding for multispectral image based on sRGB standard and residual components

Abstract: In this paper, we present a multispectral image (MSI) compression method using a lossless and lossy coding scheme, which focuses on the seamless coding of the RGB bit stream to enhance the usability of the MSI. The proposed method divides the MSI data into two components: RGB and residual. The RGB component is extracted from the MSI by using the XYZ color matching functions, a color conversion matrix, and a gamma curve. The original MSI is estimated by an RGB data encoder and the difference between the original and the estimated MSI, which is referred to as the residual component in this paper. Next, the RGB and residual components are encoded by using JPEG2000, and progressive decoding is achieved from the losslessly encoded code stream. Experimental results show that a high-quality RGB image can be obtained at a low bit rate with primary encoding of the RGB component. In addition, by using the proposed method, the quality of a spectrum can be improved by decoding the residual data, and the quality is comparable to that obtained by using JPEG2000. The lossless compression ratio obtained by using this method is also similar to that obtained by using JPEG2000 with the integer Karhunen-Loeve transform.

Photometric approach to surface reconstruction of artist paintings

Abstract: We propose a method for surface reconstruction of artist paintings. In order to reproduce the appearance of a painting, including color, surface texture, and glossiness, it is essential to acquire the pixel-wise light reflection property and orientation of the surface and render an image under an arbitrary lighting condition. A photometric approach is used to estimate bidirectional reflectance distribution functions (BRDFs) and surface normals from a set of images photographed by a fixed camera with sparsely distributed point light sources. A robust and computationally less expensive nonlinear optimization algorithm is proposed that optimizes the small number of parameters to simultaneously determine all of the specular BRDF, diffuse albedo, and surface normal. The proposed method can be applied to moderately glossy surfaces without separating captured images into diffuse and specular reflections beforehand. Experiments were conducted using oil paintings with different surface glossiness. The effectiveness of the proposed method is validated by comparing captured and rendered images.

Color harmonization for images

Abstract: Color harmonization is an artistic technique to adjust a set of colors in order to enhance their visual harmony so that they are aesthetically pleasing in terms of human visual perception. We present a new color harmonization method that treats the harmonization as a function optimization. For a given image, we derive a cost function based on the observation that pixels in a small window that have similar unharmonic hues should be harmonized with similar harmonic hues. By minimizing the cost function, we get a harmonized image in which the spatial coherence is preserved. A new matching function is proposed to select the best matching harmonic schemes, and a new component-based preharmonization strategy is proposed to preserve the hue distribution of the harmonized images. Our approach overcomes several shortcomings of the existing color harmonization methods. We test our algorithm with a variety of images to demonstrate the effectiveness of our approach.

Real-time video watermarking scheme resistant to geometric distortions

Abstract: A real-time video watermarking scheme against geometric distortions is proposed for DCT-encoded compressed video data. The full DCT coefficients have proven to be invariant to scaling and local geometric attacks. Therefore, watermarks are embedded into watermark minimal sequences (WMS) by modulating the low-frequency full DCT coefficients. To meet the requirement of real-time performance, a fast intertransformation is employed to construct full DCT directly from block DCTs. The modified differences of full DCT coefficients are inversely transformed to the differences of block DCT coefficients that are subsequently added to the original block DCTs to generate the WMS. To resist various video format conversions, the watermark detection is performed in spatial domain. This does not affect the real-time performance because the watermark detection need not re-encode the frames into compressed data, and compressed video can be easily decoded fast by employing some codec or decoding chips. Furthermore, the proposed scheme can resist rotation attacks by employing a rotation compensation strategy. Experimental results demonstrate that the proposed scheme is real-time, transparent, and robust to signal distortions, geometric distortions including rotation, scaling, aspect ratio change, linear geometric transforms, cropping and combination attacks, frame dropping and swapping, file format conversion, as well as camcorder recording.

Dark current in an active pixel complementary metal-oxide-semiconductor sensor

Abstract: We present an analysis of dark current from a comple- mentary metal-oxide-semiconductor (CMOS) active pixels sensor with global shutter. The presence of two sources of dark current, one within the collection area of the pixel and another within the sense node, present complications to correction of the dark current. The two sources are shown to generate unique and characteristic dark current behavior with respect to varying exposure time, temperature, and/or frame rate. In particular, a pixel with storage time in the sense node will show a dark current dependence on frame rate and the appearance of being a "stuck pixel" with values independent of expo- sure time. On the other hand, a pixel with an impurity located within the collection area will show no frame rate dependence, but rather a linear dependence on exposure time. A method of computing dark frames based on past dark current behavior of the sensor is pre- sented and shown to intrinsically compensate for the two different and unique sources. In addition, dark frames requiring subtraction of negative values, arising from the option to modify the bias offset, are shown to be appropriate and possible using the computational method. © 2011 SPIE and IS&T. (DOI: 10.1117/1.3533328)

Higher order singular value decomposition of tensors for fusion of registered images

Abstract: This paper describes a computational method using tensor math for higher order singular value decomposition (HOSVD) of registered images. Tensor decomposition is a rigorous way to expose structure embedded in multidimensional datasets. Given a dataset of registered 2-D images, the dataset is represented in tensor format and HOSVD of the tensor is computed to obtain a set of 2-D basis images. The basis images constitute a linear decomposition of the original dataset. HOSVD is data-driven and does not require the user to select parameters or assign thresholds. A specific application uses the basis images for pixel-level fusion of registered images into a single image for visualization. The fusion is optimized with respect to a measure of mean squared error. HOSVD and image fusion are illustrated empirically with four real datasets: (1) visible and infrared data of a natural scene, (2) MRI and x ray CT brain images, and in nondestructive testing (3) x ray, ultrasound, and eddy current images, and (4) x ray, ultrasound, and shearography images.

Image denoising based on adaptive nonlinear diffusion in wavelet domain

Abstract: In this paper, we propose a context adaptive nonlinear diffusion method for image denoising in wavelet domain which we call context based diffusion in stationary wavelet domain (SWCD). In diffusing detail coefficients, the method adapts to the local context such that strong edges are preserved and smooth regions are diffused in a greater extent. The local context which is derived directly from the transform energies at scales 1 and 2 of two-level stationary wavelet transform (SWT) controls the diffusion. The shift invariance of SWT contributes to the performance of the method. The experiment is conducted on a number of benchmark images and compared to recently developed denoising methods which explore the adaptation concept for wavelet shrinkage and diffusion. A comparison is performed also to a method of diffusing both approximation and detail coefficients. The proposed SWCD method outperforms recently proposed adaptive shrinkage and adaptive diffusion, particularly at high noise levels. The method is computationally efficient due to the Haar wavelet and fast convergence attained due to exploiting the context information.

Improved lossless coding algorithm in H.264/AVC based on hierarchical intraprediction and coding-mode selection

Abstract: In this paper, an improved lossless intracoding algorithm based on H.264/AVC framework is proposed. In the proposed algorithm, two contributions have been made. One is that samples in a macroblock (MB)/block are hierarchically predicted instead of using block-based prediction as a whole. More specifically, four groups are extracted from the samples in an macroblock/block. Samples in the first group are first predicted based on directional intraprediction method, and then the samples in other groups are predicted using the samples in the first group as the references. As a result, the information left in the residual block can be reduced since the samples can be accurately predicted by using nearer references. The other contribution is that two coding modes are designed to efficiently encode the resulting residual block. A better coding mode can be selected based on the rate optimization. Experimental results show that compared with other methods in the literature the proposed algorithm gives a much better compression performance.

Spatiotemporal attention operator using isotropic contrast and regional homogeneity

Abstract: A multiscale operator for spatiotemporal isotropic attention is proposed to reliably extract attention points during image sequence analysis. Its consecutive local maxima indicate attention points as the centers of image fragments of variable size with high intensity contrast, region homogeneity, regional shape saliency, and temporal change presence. The scale-adaptive estimation of temporal change (motion) and its aggregation with the regional shape saliency contribute to the accurate determination of attention points in image sequences. Multilocation descriptors of an image sequence are extracted at the attention points in the form of a set of multidimensional descriptor vectors. A fast recursive implementation is also proposed to make the operator's computational complexity independent from the spatial scale size, which is the window size in the spatial averaging filter. Experiments on the accuracy of attention-point detection have proved the operator consistency and its high potential for multiscale feature extraction from image sequences.