Showing papers on "Image scaling published in 2006"

An edge-guided image interpolation algorithm via directional filtering and data fusion

Abstract: Preserving edge structures is a challenge to image interpolation algorithms that reconstruct a high-resolution image from a low-resolution counterpart. We propose a new edge-guided nonlinear interpolation technique through directional filtering and data fusion. For a pixel to be interpolated, two observation sets are defined in two orthogonal directions, and each set produces an estimate of the pixel value. These directional estimates, modeled as different noisy measurements of the missing pixel are fused by the linear minimum mean square-error estimation (LMMSE) technique into a more robust estimate, using the statistics of the two observation sets. We also present a simplified version of the LMMSE-based interpolation algorithm to reduce computational cost without sacrificing much the interpolation performance. Experiments show that the new interpolation techniques can preserve edge sharpness and reduce ringing artifacts

Stochastic methods for joint registration, restoration, and interpolation of multiple undersampled images

Abstract: Using a stochastic framework, we propose two algorithms for the problem of obtaining a single high-resolution image from multiple noisy, blurred, and undersampled images. The first is based on a Bayesian formulation that is implemented via the expectation maximization algorithm. The second is based on a maximum a posteriori formulation. In both of our formulations, the registration, noise, and image statistics are treated as unknown parameters. These unknown parameters and the high-resolution image are estimated jointly based on the available observations. We present an efficient implementation of these algorithms in the frequency domain that allows their application to large images. Simulations are presented that test and compare the proposed algorithms.

Image interpolation in 4D CT using a BSpline deformable registration model.

Abstract: Purpose: To develop a method for deriving the phase-binned four-dimensional computed tomography (4D CT) image sets through interpolation of the images acquired at some known phases. Methods and Materials: Four-dimensional computed tomography data sets for 3 patients were acquired. For each patient, the correlation between inhale and exhale phases was studied and quantified using a BSpline deformable model. Images at an arbitrary phase were deduced by an interpolation of the deformation coefficients. The accuracy of the proposed scheme was assessed by comparing marker trajectories and by checkerboard/difference display of the interpolated and acquired images. Results: The images at intermediate phases could be derived by an interpolation of the deformation field. An analysis of marker movements indicated that 3 mm accuracy is achievable by the interpolation. The subtraction of image analysis indicated a similar level of success. The proposed technique was useful also for automatically mapping the organ contours in a known phase to other phases, and for designing patient-specific margins in the presence of respiratory motion. Finally, the technique led to a 90% reduction in the acquired data, because in the BSpline model, a lattice of only a few thousand values is sufficient to describe a CT data set of 25 million pixels. Conclusions: Organ deformation can be well modeled by using a BSpline model. The proposed technique may offer useful means for radiation dose reduction, binning artifacts removal, and disk storage improvement in 4D imaging.

On Resampling Detection and its Application to Detect Image Tampering

Abstract: Usually digital image forgeries are created by copy-pasting a portion of an image onto some other image. While doing so, it is often necessary to resize the pasted portion of the image to suit the sampling grid of the host image. The resampling operation changes certain characteristics of the pasted portion, which when detected serves as a clue of tampering. In this paper, we present deterministic techniques to detect resampling, and localize the portion of the image that has been tampered with. Two of the techniques are in pixel domain and two others in frequency domain. We study the efficacy of our techniques against JPEG compression and subsequent resampling of the entire tampered image.

Image interpolation by two-dimensional parametric cubic convolution

Abstract: Cubic convolution is a popular method for image interpolation. Traditionally, the piecewise-cubic kernel has been derived in one dimension with one parameter and applied to two-dimensional (2-D) images in a separable fashion. However, images typically are statistically nonseparable, which motivates this investigation of nonseparable cubic convolution. This paper derives two new nonseparable, 2-D cubic-convolution kernels. The first kernel, with three parameters (designated 2D-3PCC), is the most general 2-D, piecewise-cubic interpolator defined on [-2,2]/spl times/[-2,2] with constraints for biaxial symmetry, diagonal (or 90/spl deg/ rotational) symmetry, continuity, and smoothness. The second kernel, with five parameters (designated 2D-5PCC), relaxes the constraint of diagonal symmetry, based on the observation that many images have rotationally asymmetric statistical properties. This paper also develops a closed-form solution for determining the optimal parameter values for parametric cubic-convolution kernels with respect to ensembles of scenes characterized by autocorrelation (or power spectrum). This solution establishes a practical foundation for adaptive interpolation based on local autocorrelation estimates. Quantitative fidelity analyses and visual experiments indicate that these new methods can outperform several popular interpolation methods. An analysis of the error budgets for reconstruction error associated with blurring and aliasing illustrates that the methods improve interpolation fidelity for images with aliased components. For images with little or no aliasing, the methods yield results similar to other popular methods. Both 2D-3PCC and 2D-5PCC are low-order polynomials with small spatial support and so are easy to implement and efficient to apply.

Locally adaptive wavelet-based image interpolation

Abstract: We describe a spatially adaptive algorithm for image interpolation. The algorithm uses a wavelet transform to extract information about sharp variations in the low-resolution image and then implicitly applies interpolation which adapts to the image local smoothness/singularity characteristics. The proposed algorithm yields images that are sharper compared to several other methods that we have considered in this paper. Better performance comes at the expense of higher complexity.

Method and apparatus for automatically capturing multiple images of motor vehicles and other items for sale or auction

Abstract: An automatic 'photo booth' for capturing images of objects including but not limited to motor vehicles provides consistent and rapid image capture from multiple viewpoints. Ultrasonic sensors or other positional sensing devices are used to both position the object with the photo booth enclosure and calculate field of view parameters controlling digital cameras to provide appropriate image scaling/cropping at time of image capture. The enclosure provides automatic entry/exit door opening/closure and a controlled interior space to provide a controlled environment for image capture. Captured images may be rapidly uploaded to a server for electronic distribution over the World Wide Web or other appropriate network.

Cross Dissolve Without Cross Fade: Preserving Contrast, Color and Salience in Image Compositing

Abstract: Linear interpolation is the standard image blending method used in image compositing. By averaging in the dynamic range, it reduces contrast and visibly degrades the quality of composite imagery. We demonstrate how to correct linear interpolation to resolve this longstanding problem. To provide visually meaningful, high level control over the compositing process, we introduce three novel image blending operators that are designed to preserve key visual characteristics of their inputs. Our contrast preserving method applies a linear color mapping to recover the contrast lost due to linear interpolation. Our salience preserving method retains the most informative regions of the input images by balancing their relative opacity with their relative saliency. Our color preserving method extends homomorphic image processing by establishing an isomorphism between the image colors and the real numbers, allowing any mathematical operation defined on real numbers to be applied to colors without losing its algebraic properties or mapping colors out of gamut. These approaches to image blending have artistic uses in image editing and video production as well as technical applications such as image morphing and mipmapping. Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Picture/Image Generation

Edge-forming methods for color image zooming

Abstract: This paper introduces edge-forming schemes for image zooming of color images by general magnification factors. In order to remove/reduce artifacts arising in image interpolation, such as image blur and the checkerboard effect, an edge-forming method is suggested to be applied as a postprocess of standard interpolation methods. The method is based on nonconvex nonlinear partial differential equations. The equations are carefully discretized, incorporating numerical schemes of anisotropic diffusion, to be able to form reliable edges satisfactorily. The alternating direction implicit (ADI) method is employed for an efficient simulation of the model. It has been numerically verified that the resulting algorithm can form clear edges in 2 to 3 ADI iterations. Various results are given to show th effectiveness and reliability of the algorithm.

Distortion correction of images using hybrid interpolation technique

Abstract: The specification and drawings present a new method, system, apparatus and software product for correcting a geometrical distortion of an image using a hybrid interpolation technique by a digital image processor. After calculating corrected coordinates of pixels in the image, the interpolation of color components of the pixels can be performed by using the corrected coordinates, wherein at least one color component is interpolated using a quality interpolator which is different from quality interpolators used for other color components.

Fine directional interpolation for spatial error concealment

Abstract: This paper presents a block loss recovery technique for the image block data corrupted by transmission losses through the employment of fine directional interpolation (FDI). The proposed algorithm introduces a spatial direction vector (SDV). The SDVs are extracted from the edge information of the neighboring image data. Subsequently, the SDVs are adaptively applied to interpolate lost pixels on a pixel-by-pixel basis. This approach improves the capability to more reliably recover high-detailed contents in the corrupted block. Experimental results demonstrate that the FDI method performs better as compared to previous techniques

Finger sensor apparatus using image resampling and associated methods

Abstract: A finger sensor may include a plurality of finger image sensing arrays for generating a respective plurality of finger image data sets based upon sliding finger movement over the finger image sensing arrays, and a processor cooperating with the finger image sensing arrays. The processor may determine finger movement based upon the finger image data sets, and generate a resampled finger image data set by resampling the finger image data sets based upon the determined finger movement. The processor may further deskew the finger image data sets when generating the resampled finger image data set.

Image Interpolation by Super-Resolution

Abstract: Term “super-resolution” is typically used for a high-resolution image produced from several low-resolution noisy observations. In this paper, we consider the problem of high-quality interpolation of a single noise-free image. Several aspects of the corresponding super-resolution algorithm are investigated: choice of regularization term, dependence of the result on initial approximation, convergence speed, and heuristics to facilitate convergence and improve the visual quality of the resulting image.

Adaptive classification scheme for CFA image interpolation

Abstract: A first image is received and enlarged to create a second image. The second image includes a plurality of unknown pixel values, wherein each unknown pixel value has a plurality of neighboring known pixel values. The unknown pixel values are interpolated from the known pixel values in view of pixel interpolation weights. Interpolation of the unknown pixel values involves determining the needed interpolation weights by: classifying an area of the image into one of a plurality of types based on known pixel values, and obtaining at least one certain interpolation weight based on the classification type of the image area for use in interpolating at least one unknown pixel value.

Adaptive Interpolation Algorithm for Real-time Image Resizing

Abstract: In this paper, an adaptive interpolation algorithm is presented based on the Newton Polynomial to improve the limitation of the traditional algorithm for image resizing The second-order difference of adjacent pixels gray values shows the relativity among the pixels Accordingly, the adaptive function for image interpolation is deduced according to both this relativity and the classical Newton polynomial Then the efficiency of our method is compared with that of the traditional algorithm for image resizing in Matlab Furthermore, the implementation circuit architecture is devised by three stage paralleling pipelines for the adaptive image resizing algorithm and is verified in FPGA (field programmable gate array) The experimental results show that our proposed algorithm excels the bicubic interpolation in visual effect, and has a lower complexity Therefore, the algorithm adapts to real-time image resizing

Classification-based hybrid filters for image processing

Abstract: The paper proposes a new type of nonlinear filters, classification-based hybrid filters, which jointly utilize spatial, rank order and structural information in image processing. The proposed hybrid filters use a vector containing the observation samples in both spatial and rank order. The filter coefficients depend on the local structure of the image content, which can be classified based on the luminance pattern in the filter window. The optimal coefficients for each class are obtained by the Least Mean Square optimization. We show that the proposed classification-based hybrid filters exhibit improved performance over linear filters and order statistic filters in several applications, image de-blocking, impulsive noise reduction and image interpolation. Both quantitative and qualitative comparison have also been presented in the paper.

An Empirical Bayes Em-Wavelet Unification for Simultaneous Denoising, Interpolation, and/Or Demosaicing

Abstract: We present a unified framework for coupling the EM algorithm with the Bayesian hierarchical modeling of neighboring wavelet coefficients of image signals Within this framework, problems with missing pixels or pixel components, and hence unobservable wavelet co-efficients, are handled simultaneously with denoising The hyper-parameters of the model are estimated via the marginal likelihood by the EM algorithm, and a part of the output of its E-step automatically provide optimal estimates, given the specified Bayesian model, of the noise-free image This unified empirical-Bayes based framework, therefore, offers a statistically principled and extremely flexible approach to a wide range of pixel estimation problems including image denoising, image interpolation, demosaicing, or any combinations of them

Method and system for digital image scaling with sharpness enhancement and transient improvement

Abstract: An image scaling system includes a single set of line buffers that receive and store input image pixel data in an input video frame. The scaling system also includes a linear two-dimensional sharpness enhancement unit configured to receive input pixel data from the line buffers and to generate sharpened pixel data by enhancing high frequency components of the input pixel data at an input image resolution, a linear two-dimensional image scaling unit configured to receive the sharpened pixel data and to convert the sharpened pixel data into scaled sharpened pixel data at an output image resolution, and a transient improvement unit configured to receive the input pixel data from the line buffers, sharpened pixel data and scaled sharpened pixel data to improve transient responses at edges in an output image, and to generate output image pixel data at the output image resolution.

Image interpolation using mathematical morphology

Abstract: We present a new method for interpolating binary images that outperforms existing techniques. Bitmapped images have a specific horizontal and vertical resolution. When we magnify such an image, we want the resolution to be increased, allowing more details in the image. However, these extra details are not present in the original image. A blowup of the image using simple interpolation will introduce jagged edges, also called "jaggies". We present a new interpolation technique "mmINT", which avoids these errors. It is based on mathematical morphology, a theoretical framework to alter an image while preserving the image objects' geometry. The algorithm detects jaggies in the blown up image and removes them, making the edges smoother. This is done by replacing specific black pixels with white pixels, and vice versa. The results show that mmINT is a superior technique for the interpolation of binary images, like logos, diagrams, cartoons and maps.

Non-Local Image Interpolation

Abstract: In this paper we present a novel method for interpolating images and we introduce the concept of non-local interpolation. Unlike other conventional interpolation methods, the estimation of the unknown pixel values is not only based on its local surrounding neighbourhood, but on the whole image (non-locally). In particularly, we exploit the repetitive character of the image. A great advantage of our proposed approach is that we have more information at our disposal, which leads to better estimates of the unknown pixel values. Results show the effectiveness of non-local interpolation and its superiority at very large magnifications to other interpolation methods.

Image interpolation apparatus and method thereof

Abstract: An image interpolation apparatus includes: a frequency component detecting part detecting a frequency component in the unit of pixel data included in an input image signal; a coefficient storing part storing a plurality of interpolation coefficients corresponding to a plurality of frequency component sections; a coefficient controlling part selecting a certain interpolation coefficient corresponding to the frequency component detected in the unit of pixel data at the coefficient storing part; and an interpolation filtering part filtering the pixel data with the selected interpolation coefficient and outputting the interpolated pixel data. Accordingly, an interpolation adaptive to images in high and low frequency areas are performed to output enhanced picture quality.

Wavelet fusion: a tool to break the limits on lmmse image super-resolution

Abstract: This paper presents a wavelet-based computationally efficient implementation of the Linear Minimum Mean Square Error (LMMSE) algorithm in image super-resolution. The image super-resolution reconstruction problem is well-known to be an ill-posed inverse problem of large dimensions. The LMMSE estimator to be implemented in the image super-resolution reconstruction problem requires an inversion of a very large dimension matrix, which is practically impossible. Our suggested implementation is based on breaking the problem into four consecutive steps, a registration step, a multi-channel LMMSE restoration step, a wavelet-based image fusion step and an LMMSE image interpolation step. The objective of the wavelet fusion step is to integrate the data obtained from each observation into a single image, which is then interpolated to give a high-resolution image. The paper explains the implementation of each step. The proposed implementation has succeeded in obtaining a high-resolution image from multiple degraded observations with a high PSNR. The computation time of the suggested implementation is small when compared to traditional iterative image super-resolution algorithms.

Image Up-Sampling Using Discrete Wavelet Transform

Abstract: Image up-sampling is found to be a very effective technique useful in today’s digital image processing applications or rendering devices. In image upsampling, an image is enhanced from a lower resolution to a higher resolution with the degree of enhancement depending upon application requirements. It is known that the traditional interpolation based approaches for up-sampling, such as Bilinear or Bicubic interpolation, blur the resultant images [1, 2]. Furthermore; in color imagery, these interpolation based up-sampling methods may have color infringing artifacts in the areas where the images contain sharp edges and fine textures. In this paper, we present an interesting up-sampling algorithm using 9/7 bi-orthogonal Spline filters based Discrete Wavelet Transform (DWT). The proposed method preserves much of the sharp edge features in the image, and lessens the amount of color artifacts. Effectiveness of the proposed algorithm has been demonstrated based on evaluation of PSNR and * ab E ' quality metrics of the original image and the reconstructed image.

Method for small detail restoration in digital images

Abstract: A small detail restoration (SDR) system implements an SDR method for processing digital images, especially in DTV applications prior to image resampling process in a scaler unit. The SDR system performs three image processing functions: detail extraction, smoothness checking, and small detail detection on an input image. The results of the three processing functions are combined, amplified and added back to original image to obtain the final resulting output image. The SDR method is applied in the texture or small detail area of image while the strong edge area is not enhanced. For example, in DTV applications, the SDR method is suitable for enhancing the digital image prior to image resampling process in the scaler unit.

Metal artifacts reduction in CT images through Euler's elastica and curvature based sinogram inpainting

Abstract: Metal artifacts arise in CT images when X-rays traverse the high attenuating objects such as metal bodies. Portions of projection data become unavailable. In this paper, we present an Euler's elastica and curvature based sinogram inpainting (EECSI) algorithm for metal artifacts reduction, where "inpainting" is a synonym for "image interpolation". In EECSI, the unavailable data are regarded as occlusion and can be inpainted inside the inpainting domain based on elastica interpolants. Numerical simulations demonstrate that, compared to conventional interpolation methods, the algorithm proposed connects the unavailable projection region more smoothly and accurately, thus better reduces metal artifacts and more accurately reveals cross section structures, especially in the immediate neighborhood of the metallic objects.

An Edge Preserving Locally Adaptive Anti-aliasing Zooming Algorithm with Diffused Interpolation

Abstract: In this paper the problem of producing an enlarged image from a given digital image is addressed (zooming). Different image interpolation techniques are used for image enlargement. During interpolation, preserving details and smoothing data at the same time for not introducing spurious artifacts (i.e. Aliasing) is difficult. A complete and a definitive solution to this problem is still an open issue. Although there are some well known methods in the market Parket [14], Sakamote [16], the paper proposes a method that considers discontinuities and luminance variations in a sequence of non linear iterations steps. All the pixels present near the edges are diffused into the edge in a way that aliasing is reduced to a greater extent. Hence the proposed method is completed in limited computational resources. The proposed method preserves edges and brings smoothness and at the same time controls the aliasing effect.

Image Manifold Interpolation using Free-Form Deformations

Abstract: An important class of image data sets depict an object undergoing deformation When there are only a few underlying causes of the deformation, these images have a natural low-dimensional structure which can be parameterized using manifold learning This paper presents a method to solve for the deformation field as a function of the manifold coordinates ? implicitly optimizing the deformation between all pairs of images simultaneously Additionally, we provide a mechanism to create images for arbitrary coordinates of the manifold, addressing an important limitation of manifold learning algorithms for the case of images related through deformations We give quantitative results in an artificial image morphing example and illustrate the method by finding the deformations relating all images of a cardiopulmonary MR image sequence

An image interpolation scheme for repetitive structures

Abstract: In this paper we present a novel method for interpolating images with repetitive structures. Unlike other conventional interpolation methods, the unknown pixel value is not estimated based on its local surrounding neighbourhood, but on the whole image. In particularly, we exploit the repetitive character of the image. A great advantage of our proposed approach is that we have more information at our disposal, which leads to a better reconstruction of the interpolated image. Results show the effectiveness of our proposed method and its superiority at very large magnifications to other traditional interpolation methods.