Showing papers on "Channel (digital image) published in 2013"

Multi-channel Correlation Filters

Abstract: Modern descriptors like HOG and SIFT are now commonly used in vision for pattern detection within image and video. From a signal processing perspective, this detection process can be efficiently posed as a correlation/ convolution between a multi-channel image and a multi-channel detector/filter which results in a single channel response map indicating where the pattern (e.g. object) has occurred. In this paper, we propose a novel framework for learning a multi-channel detector/filter efficiently in the frequency domain, both in terms of training time and memory footprint, which we refer to as a multichannel correlation filter. To demonstrate the effectiveness of our strategy, we evaluate it across a number of visual detection/ localization tasks where we: (i) exhibit superior performance to current state of the art correlation filters, and (ii) superior computational and memory efficiencies compared to state of the art spatial detectors.

PiCam: an ultra-thin high performance monolithic camera array

Abstract: We present PiCam (Pelican Imaging Camera-Array), an ultra-thin high performance monolithic camera array, that captures light fields and synthesizes high resolution images along with a range image (scene depth) through integrated parallax detection and superresolution. The camera is passive, supporting both stills and video, low light capable, and small enough to be included in the next generation of mobile devices including smartphones. Prior works [Rander et al. 1997; Yang et al. 2002; Zhang and Chen 2004; Tanida et al. 2001; Tanida et al. 2003; Duparre et al. 2004] in camera arrays have explored multiple facets of light field capture - from viewpoint synthesis, synthetic refocus, computing range images, high speed video, and micro-optical aspects of system miniaturization. However, none of these have addressed the modifications needed to achieve the strict form factor and image quality required to make array cameras practical for mobile devices. In our approach, we customize many aspects of the camera array including lenses, pixels, sensors, and software algorithms to achieve imaging performance and form factor comparable to existing mobile phone cameras.Our contributions to the post-processing of images from camera arrays include a cost function for parallax detection that integrates across multiple color channels, and a regularized image restoration (superresolution) process that takes into account all the system degradations and adapts to a range of practical imaging conditions. The registration uncertainty from the parallax detection process is integrated into a Maximum-a-Posteriori formulation that synthesizes an estimate of the high resolution image and scene depth. We conclude with some examples of our array capabilities such as postcapture (still) refocus, video refocus, view synthesis to demonstrate motion parallax, 3D range images, and briefly address future work.

Color retinal image enhancement using CLAHE

Abstract: Common method in image enhancement that's often use is histogram equalization, due to this method is simple and has low computation load. In this research, we use Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance the color retinal image. To reduce this noise effect in color retinal image due to the acquisition process, we need to enhance this image. Color retinal image has unique characteristic than other image, that is, this image has important in green (G) channel. Image enhancement has important contribution in ophthalmology. In this paper, we propose new enhancement method using CLAHE in G channel to improve the color retinal image quality. The enhancement process conduct in G channel is appropriate to enhance the color retinal image quality.

Fast single image fog removal using the adaptive Wiener filter

Abstract: We present in this paper a fast single image defogging method that uses a novel approach to refining the estimate of amount of fog in an image with the Locally Adaptive Wiener Filter. We provide a solution for estimating noise parameters for the filter when the observation and noise are correlated by decorrelating with a naively estimated defogged image. We demonstrate our method is 50 to 100 times faster than existing fast single image defogging methods and that our proposed method subjectively performs as well as the Spectral Matting smoothed Dark Channel Prior method.

Specular Reflection Separation Using Dark Channel Prior

Abstract: We present a novel method to separate specular reflection from a single image. Separating an image into diffuse and specular components is an ill-posed problem due to lack of observations. Existing methods rely on a specular-free image to detect and estimate specularity, which however may confuse diffuse pixels with the same hue but a different saturation value as specular pixels. Our method is based on a novel observation that for most natural images the dark channel can provide an approximate specular-free image. We also propose a maximum a posteriori formulation which robustly recovers the specular reflection and chromaticity despite of the hue-saturation ambiguity. We demonstrate the effectiveness of the proposed algorithm on real and synthetic examples. Experimental results show that our method significantly outperforms the state-of-the-art methods in separating specular reflection.

Image-Based Coral Reef Classification and Thematic Mapping

Abstract: This paper presents a novel image classification scheme for benthic coral reef images that can be applied to both single image and composite mosaic datasets. The proposed method can be configured to the characteristics (e.g., the size of the dataset, number of classes, resolution of the samples, color information availability, class types, etc.) of individual datasets. The proposed method uses completed local binary pattern (CLBP), grey level co-occurrence matrix (GLCM), Gabor filter response, and opponent angle and hue channel color histograms as feature descriptors. For classification, either k- nearest neighbor (KNN), neural network (NN), support vector machine (SVM) or probability density weighted mean distance (PDWMD) is used. The combination of features and classifiers that attains the best results is presented together with the guidelines for selection. The accuracy and efficiency of our proposed method are compared with other state-of-the-art techniques using three benthic and three texture datasets. The proposed method achieves the highest overall classification accuracy of any of the tested methods and has moderate execution time. Finally, the proposed classification scheme is applied to a large-scale image mosaic of the Red Sea to create a completely classified thematic map of the reef benthos.

Single image haze removal considering sensor blur and noise

Abstract: Images of outdoor scenes are usually degraded under bad weather conditions, which results in a hazy image. To date, most haze removal methods based on a single image have ignored the effects of sensor blur and noise. Therefore, in this paper, a three-stage algorithm for haze removal, considering sensor blur and noise, is proposed. In the first stage, we preprocess the degraded image and eliminate the blur/noise interference to estimate the hazy image. In the second stage, we estimate the transmission and atmospheric light by the dark channel prior method. In the third stage, a regularized method is proposed to recover the underlying image. Experimental results with both simulated and real data demonstrate that the proposed algorithm is effective, based on both the visual effect and quantitative assessment.

Method and apparatus for processing image

Abstract: An image processing apparatus includes an adaptive interpolation device which converts a MFA pattern image into a quincuncial pattern image based on difference values, and interpolates color channels and an NIR channel, based on difference values of the converted quincuncial pattern image in vertical and horizontal pixel directions; a frequency compensation device which obtains a high-resolution MFA image using high-frequency and medium-frequency components of a high-resolution base image, based on linear regression analysis and compared energy levels of MFA channel images to an energy level of a base image; and a channel interference suppression device which removes color distortion generated between each channel of the high-resolution MFA image, and another channel of the high-resolution MFA image and a base channel using a weighted average of pixel value differences between each channel of the high-resolution MFA image, and the other channel of the high-resolution MFA image and the base channel.

Per-Colorant-Channel Color Barcodes for Mobile Applications: An Interference Cancellation Framework

Abstract: We propose a color barcode framework for mobile phone applications by exploiting the spectral diversity afforded by the cyan (C), magenta (M), and yellow (Y) print colorant channels commonly used for color printing and the complementary red (R), green (G), and blue (B) channels, respectively, used for capturing color images. Specifically, we exploit this spectral diversity to realize a three-fold increase in the data rate by encoding independent data in the C, M, and Y print colorant channels and decoding the data from the complementary R, G, and B channels captured via a mobile phone camera. To mitigate the effect of cross-channel interference among the print-colorant and capture color channels, we develop an algorithm for interference cancellation based on a physically-motivated mathematical model for the print and capture processes. To estimate the model parameters required for cross-channel interference cancellation, we propose two alternative methodologies: a pilot block approach that uses suitable selections of colors for the synchronization blocks and an expectation maximization approach that estimates the parameters from regions encoding the data itself. We evaluate the performance of the proposed framework using specific implementations of the framework for two of the most commonly used barcodes in mobile applications, QR and Aztec codes. Experimental results show that the proposed framework successfully overcomes the impact of the color interference, providing a low bit error rate and a high decoding rate for each of the colorant channels when used with a corresponding error correction scheme.

Colour spaces effects on improved discrete wavelet transform-based digital image watermarking using Arnold transform map

Abstract: The most digital image watermarking algorithms have nearly always been realised in red, green and blue (RGB) colour space. In this study, a secure, robust and imperceptible CDMA image watermarking scheme which uses discrete wavelet transform is proposed and tested in eight colour spaces RGB, YCbCr, JPEG-YCbCr, YIQ, YUV, hue, saturation, intensity, hue, saturation, value and CIELab to determine which colour space is more effective in watermarking algorithms based on correlation techniques and provides a result which does not differ immeasurably from the original with respect to imperceptibility and robustness. In the proposed scheme, a scrambled binary image by Arnold transform map, after encryption, is embedded into sub-images of the first channel wavelet decomposition of intended colour space using block processing technique. The experimental results show that the proposed approach provides extra imperceptibility, security and robustness against JPEG compression and different noise attacks compared to the similar proposed methods in earlier works.

SHIRC: A simultaneous sparsity model for histopathological image representation and classification

Abstract: Automated classification of histopathological images is an important research problem in medical imaging. Digital histopathology exhibits two principally distinct characteristics: 1) invariably histopathological images are multi-channel (color) with key geometric information spread across the color channels instead of being captured by luminance alone, and 2) the richness of geometric structures in such tissue imagery makes feature extraction for classification very demanding. Inspired by recent work in the use of sparsity for single channel image classification, we propose a new simultaneous Sparsity model for multi-channel Histopathological Image Representation and Classification (SHIRC). Essentially, we represent a multi-channel histopathological image as a sparse linear combination of training examples under suitable channel-wise constraints and classification is performed by solving a newly formulated simultaneous sparsity-based optimization problem. Experiments on two challenging real-world image databases: 1) provided by pathologists of the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 2) histopathological images corresponding to intraductal breast lesions [1], reveal the merits of the proposed SHIRC model over state of the art alternatives.

Decolorization: is rgb2gray() out?

Abstract: Decolorization problems originate from the fact that the luminance channel may fail to represent iso-luminant regions in the original color image. Currently all the existing methods suffer from the same weakness -- robustness: failure cases can be easily found for each of the methods. This prevents all these methods from being practical for real-world applications. In fact, the luminance conversion (i.e, rgb2gray() function in Matlab) performs rather well in practice only with exceptions for failure cases like the iso-luminant regions. Thus a thought-provoking question is naturally raised: can we reach a robust solution by simply modifying the rgb2gray() to avoid failures in iso-luminant regions? Instead of assigning fixed channel weights for all images, a more flexible strategy would be choosing channel weights depending on specific images to avoid indiscrimination in iso-luminant regions. Following this strategy, by considering multi-scale contrast preservation, we design an algorithm that can consistently produce "good" results for each color image, among which the "best" one preferred by users can be selected by further involving perceptual contrasts preferences. The results are verified through user study.

Strategy for the Development of a Smart NDVI Camera System for Outdoor Plant Detection and Agricultural Embedded Systems

Abstract: The application of (smart) cameras for process control, mapping, and advanced imaging in agriculture has become an element of precision farming that facilitates the conservation of fertilizer, pesticides, and machine time. This technique additionally reduces the amount of energy required in terms of fuel. Although research activities have increased in this field, high camera prices reflect low adaptation to applications in all fields of agriculture. Smart, low-cost cameras adapted for agricultural applications can overcome this drawback. The normalized difference vegetation index (NDVI) for each image pixel is an applicable algorithm to discriminate plant information from the soil background enabled by a large difference in the reflectance between the near infrared (NIR) and the red channel optical frequency band. Two aligned charge coupled device (CCD) chips for the red and NIR channel are typically used, but they are expensive because of the precise optical alignment required. Therefore, much attention has been given to the development of alternative camera designs. In this study, the advantage of a smart one-chip camera design with NDVI image performance is demonstrated in terms of low cost and simplified design. The required assembly and pixel modifications are described, and new algorithms for establishing an enhanced NDVI image quality for data processing are discussed.

A variational framework for single low light image enhancement using bright channel prior

Abstract: Low light image enhancement is prerequisite in many fields, such as surveillance systems, road safety and waterway transport. In this paper, a new variational framework using bright channel prior is proposed to address the low light image enhancement problem within a single image. An alternating direction optimization method is employed to solve the variational problem. Experiment results show that the new method can better eliminate the black halo and suppressing the issues of over-enhancement and color distortion when compared with other existing methods.

Validation of various adaptive threshold methods of segmentation applied to follicular lymphoma digital images stained with 3,3’-Diaminobenzidine&Haematoxylin

Abstract: The comparative study of the results of various segmentation methods for the digital images of the follicular lymphoma cancer tissue section is described in this paper. The sensitivity and specificity and some other parameters of the following adaptive threshold methods of segmentation: the Niblack method, the Sauvola method, the White method, the Bernsen method, the Yasuda method and the Palumbo method, are calculated. Methods are applied to three types of images constructed by extraction of the brown colour information from the artificial images synthesized based on counterpart experimentally captured images. This paper presents usefulness of the microscopic image synthesis method in evaluation as well as comparison of the image processing results. The results of thoughtful analysis of broad range of adaptive threshold methods applied to: (1) the blue channel of RGB, (2) the brown colour extracted by deconvolution and (3) the ’brown component’ extracted from RGB allows to select some pairs: method and type of image for which this method is most efficient considering various criteria e.g. accuracy and precision in area detection or accuracy in number of objects detection and so on. The comparison shows that the White, the Bernsen and the Sauvola methods results are better than the results of the rest of the methods for all types of monochromatic images. All three methods segments the immunopositive nuclei with the mean accuracy of 0.9952, 0.9942 and 0.9944 respectively, when treated totally. However the best results are achieved for monochromatic image in which intensity shows brown colour map constructed by colour deconvolution algorithm. The specificity in the cases of the Bernsen and the White methods is 1 and sensitivities are: 0.74 for White and 0.91 for Bernsen methods while the Sauvola method achieves sensitivity value of 0.74 and the specificity value of 0.99. According to Bland-Altman plot the Sauvola method selected objects are segmented without undercutting the area for true positive objects but with extra false positive objects. The Sauvola and the Bernsen methods gives complementary results what will be exploited when the new method of virtual tissue slides segmentation be develop.

Efficient dark channel based image dehazing using quadtrees

Abstract: Using dark channel prior—a kind of statistics of the haze-free outdoor images—to remove haze from a single image input is simple and effective. However, due to the use of soft matting algorithm, the method suffers from massive consumption of both memory and time, which largely limits its scalability for large images. In this paper, we present a hierarchical approach to accelerate dark channel based image dehazing. The core of our approach is a novel, efficient scheme for solving the soft matting problem involved in image dehazing, using adaptively subdivided quadtrees built in image space. Acceleration is achieved by transforming the problem of solving a N-variable linear system required in soft matting, to a problem of solving a much smaller m-variable linear system, where N is the number of pixels and m is the number of the corners in the quadtree. Our approach significantly reduces both space and time cost while still maintains visual fidelity, and largely extends the practicability of dark channel based image dehazing to handle large images.

A novel compressive sensing approach to simultaneously acquire color and near-infrared images on a single sensor

Abstract: Sensors of most digital cameras are made of silicon that is inherently sensitive to both the visible and near-infrared parts of the electromagnetic spectrum. In this paper, we address the problem of color and NIR joint acquisition. We propose a framework for the joint acquisition that uses only a single silicon sensor and a slightly modified version of the Bayer color-filter array that is already mounted in most color cameras. Implementing such a design for an RGB and NIR joint acquisition system requires minor changes to the hardware of commercial color cameras. One of the important differences between this design and the conventional color camera is the post-processing applied to the captured values to reconstruct full resolution images. By using a CFA similar to Bayer, the sensor records a mixture of NIR and one color channel in each pixel. In this case, separating NIR and color channels in different pixels is equivalent to solving an under-determined system of linear equations. To solve this problem, we propose a novel algorithm that uses the tools developed in the field of compressive sensing. Our method results in high-quality RGB and NIR images (the average PSNR of more than 30 dB for the reconstructed images) and shows a promising path towards RGB and NIR cameras.

Image fusion scheme based on modified dual pulse coupled neural network

Abstract: Image fusion combines information from multiple images of the same scene to obtain a composite image which is more suitable for further image processing tasks. This study presented an image fusion scheme based on the modified dual pulse coupled neural network (PCNN) in non-subsampled contourlet transform (NSCT) domain. NSCT can overcome the lack of shift invariance in contourlet transform. Original images were decomposed to obtain the coefficients of low-frequency subbands and high-frequency subbands. In this fusion scheme, a new sum-modified Laplacian of the low-frequency subband image, which represents the edge-feature of the low-frequency subband image in NSCT domain, is presented and input to motivate modified dual PCNN. For fusion of high-frequency subband coefficients, spatial frequency will be used as the gradient features of images to motivate dual channel PCNN and to overcome Gibbs phenomena. Experimental results show that the proposed scheme can significantly improve image fusion performance, performs very well in fusion and outperforms conventional methods such as traditional discrete wavelet transform, dual tree complex wavelet and PCNN in terms of objective criteria and visual appearance.

Oblique aerial images and their use in cultural heritage documentation

Abstract: Oblique images enable three-dimensional (3d) modelling of objects with vertical dimensions. Such imagery is nowadays systematically taken of cities and may easily become available. The documentation of cultural heritage can take advantage of these sources of information. Two new oblique camera systems are presented and characteristics of such images are summarized. A first example uses images of a new multi-camera system for the derivation of orthoimages, facade plots with photo texture, 3d scatter plots, and dynamic 3d models of a historic church. The applied methodology is based on automatically derived point clouds of high density. Each point will be supplemented with colour and other attributes. The problems experienced in these processes and the solutions to these problems are presented. The applied tools are a combination of professional tools, free software, and of own software developments. Special attention is given to the quality of input images. Investigations are carried out on edges in the images. The combination of oblique and nadir images enables new possibilities in the processing. The use of the near-infrared channel besides the red, green, and blue channel of the applied multispectral imagery is also of advantage. Vegetation close to the object of interest can easily be removed. A second example describes the modelling of a monument by means of a non-metric camera and a standard software package. The presented results regard achieved geometric accuracy and image quality. It is concluded that the use of oblique aerial images together with image-based processing methods yield new possibilities of economic and accurate documentation of tall monuments.

Method for correcting image data from an image sensor having image pixels and non-image pixels, and image sensor implementing same

Abstract: A method for correcting image data, in particular for colour correction and cross-talk reduction of image data obtained by an image sensor comprising image pixels (R, G, B) and non-image pixels (F) with a different spectral sensitivity, comprises obtaining uncorrected full colour data for the image pixels (R,G,B); correcting the full colour data of non-direct neighbours of the non-image pixels (F) by means of a first colour correction matrix (CCM1); and correcting the full colour data (rcorr, gcorr, bcorr) of direct neighbours of the non-image pixels by means of a second colour correction matrix (CCM2) different from the first colour correction matrix. An image sensor (10) and an auto-focus camera using same are also disclosed.

Local color transfer between images using dominant colors

Abstract: A color-transfer method that can transfer colors of an image to another for the local regions using their dominant colors is proposed. In order to naturally transfer the colors and moods of a target image to a source image, we need to find the local regions of colors that need to be modified in the image. Since the dominant colors of each image can be used for the estimation of color regions, we develop a grid-based mode detection, which can efficiently estimate dominant colors of an image. Based on these dominant colors, our proposed method performs a consistent segmentation of source and target images by using the cost-volume filtering. Through the segmentation procedure, we can estimate complex color characteristics and transfer colors to the local regions of an image. For an intuitive and natural color transfer, region matching is also crucial. Therefore, we use the visually prominent colors in the image to meaningfully connect each segmented region in which modified color-transfer method is applied to balance the overall luminance of the final result. In the Experimental Result section, various convincing results are demonstrated through the proposed method.

Image colorization using sparse representation

Abstract: Image colorization is the task to color a grayscale image with limited color cues. In this work, we present a novel method to perform image colorization using sparse representation. Our method first trains an over-complete dictionary in YUV color space. Then taking a grayscale image and a small subset of color pixels as inputs, our method colorizes overlapping image patches via sparse representation; it is achieved by seeking sparse representations of patches that are consistent with both the grayscale image and the color pixels. After that, we aggregate the colorized patches with weights to get an intermediate result. This process iterates until the image is properly colorized. Experimental results show that our method leads to high-quality colorizations with small number of given color pixels. To demonstrate one of the applications of the proposed method, we apply it to transfer the color of one image onto another to obtain a visually pleasing image.