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Sparse representations of signals have drawn considerable interest in recent years. The assumption that natural signals, such as images, admit a sparse decomposition over a redundant dictionary leads to efficient algorithms for handling such sources of data. In particular, the design of well adapted dictionaries for images has been a major challenge. The K-SVD has been recently proposed for this task [1] and shown to perform very well for various grayscale image processing tasks. In this paper, we address the problem of learning dictionaries for color images and extend the K-SVD-based grayscale image denoising algorithm that appears in [21. This work puts forward ways for handling nonhomogeneous noise and missing information, paving the way to state-of-the-art results in applications such as color image denoising, demosaicing, and inpainting, as demonstrated in this paper. [ABSTRACT FROM AUTHOR]

It is well known that the performance of context-based image processing systems can be improved by allowing the processor (e.g., an encoder or a denoiser) a delay of several samples before making a processing decision. Often, however, for such systems, traditional delayed-decision algorithms can become computationally prohibitive due to the growth in the size of the space of possible solutions. In this paper, we propose a reduced-complexity, one-pass, delayed-decision algorithm that systematically reduces the size of the search space, while also preserving its structure. In particular, we apply the proposed algorithm to two examples of adaptive context-based image processing systems, an image coding system that employs a context-based entropy coder, and a spatially adaptive image-denoising system. For these two types of widely used systems, we show that the proposed delayed-decision search algorithm outperforms instantaneous-decision algorithms with only a small increase in complexity. We also show that the performance of the proposed algorithm is better than that of other, higher complexity, delayed-decision algorithms. [ABSTRACT FROM AUTHOR]

Centroidal Voronoi tessellations (CVTs) are special Voronoi tessellations whose generators are also the centers of mass (centroids) of the Voronoi regions with respect to a given density function and CVT-based methodologies have been proven to be very useful in many diverse applications in science and engineering. In the context of image processing and its simplest form, CVT-based algorithms reduce to the well-known k-means clustering and are easy to implement. In this paper, we develop edge-weighted centroidal Voronoi tessellation (EWCVT) model for image segmentation and propose some efficient algorithms for its construction. Our EWCVT model can overcome some deficiencies possessed by the basic CVT model; in particular, the new model appropriately combines the image intensity information together with the length of cluster boundaries, and can handle very sophisticated situations. We demonstrate through extensive examples the efficiency, effectiveness, robustness, and flexibility of the proposed method [ABSTRACT FROM AUTHOR]

Abstract-In this paper, we propose an algorithm for lossless conversion of data between Cartesian and polar coordinates, when the data is sampled from a 2-D real-valued function (a mapping: R² ↦ R) expressed as a particular kind of truncated expansion. We use Laguerre functions and the Fourier basis for the polar coordinate expression. Hermite functions are used for the Cartesian coordinate expression. A finite number of coefficients for the truncated expansion specifies the function in each coordinate system. We derive the relationship between the coefficients for the two coordinate systems. Based on this relationship, we propose an algorithm for lossless conversion between the two coordinate systems. Resampling can be used to evaluate a truncated expansion on the complementary coordinate system without computing a new set of coefficients. The resampled data is used to compute the new set of coefficients to avoid the numerical instability associated with direct conversion of the coefficients. In order to apply our algorithm to discrete image data, we propose a method to optimally fit a truncated expression to a given image. We also quantify the error that this filtering process can produce. Finally the algorithm is applied to solve the polar-Cartesian interpolation problem. [ABSTRACT FROM AUTHOR]

This paper presents a novel gradient-based image completion algorithm for removing significant objects from natural images or photographs. Our method reconstructs the region of removal in two phases. Firstly, the gradient maps in the removed area are completed through a patch-based filling algorithm. After that, the image is reconstructed from the gradient maps by solving a Poisson equation. A new patch-matching criterion is developed in our approach to govern the completion of gradient maps. Both the gradient and the color information are incorporated in this new criterion, so a better image completion result is obtained. Several examples and comparisons are given at the end of the paper to demonstrate the performance of our gradient-based image completion approach. [Copyright &y& Elsevier]

Gradient-based motion estimation methods (GMs) are considered to be in the heart of state-of-the-art registration algorithms, being able to account for both pixel and subpixel registration and to handle various motion models (translation, rotation, affine, and projective). These methods estimate the motion between two images based on the local changes in the image intensities while assuming image smoothness. This paper offers two main contributions. The first is enhancement of the GM technique by introducing two new bidirectional formulations of the GM. These improve the convergence properties for large motions. The second is that we present an analytical convergence analysis of the GM and its properties. Experimental results demonstrate the applicability of these algorithms to real images. [ABSTRACT FROM AUTHOR]

This paper examines measures for evaluating the performance of algorithms for single instruction stream-multiple data stream (SIMD) machines. The SIMD mode of parallelism involves using a large number of processors synchronized together. All processors execute the same instruction at the same time: however, each processor operates on a different data item. The complexity of parallel algorithms is, in general, a function of the machine size (number of processors), problem size, and type of interconnection network used to provide communications among the processors. Measures which quantity the effect of changing the machine-size/problem-size/network-type relationships are therefore needed. A number of such measures are presented and are applied to an example SIMD algorithm from the image processing problem domain. The measures discussed and compared include execution time, speed, parallel efficiency, overhead ratio, processor utilization, redundancy, cost effectiveness, speed-up of the parallel algorithm over the corresponding serial algorithm, and an additive measure called "price" which assigns a weighted value to computations and processors. [ABSTRACT FROM AUTHOR]

The article discusses the study which aims to develop an advance heterogeneous parallel algorithm for hyperspectral image processing through HeteroMPI, an extension of message passing interface (MPI) for programming high-performance computations on heterogeneous networks of computers (HNOCs). It describes hyperspectralimaging algorithm, which performs joint spatial and spectral analysis of the data in combined fashion, and further develops HeteroMPI-based parallel implementations of the algorithm. The study concludes through citing the effectiveness of the HeteroMPI-based implementation.

Abstract: In this paper, we define the three-dimensional topological map, a model which represents both the topological and geometrical information of a three-dimensional labeled image. Since this model describes the image’s topology in a minimal way, we can use it to define efficient image processing algorithms. The topological map is the last level of map hierarchy. Each level represents the region boundaries of the image and is defined from the previous level in the hierarchy, thus giving a simple constructive definition. This model is an extension of the similar model defined for 2D images. Progressive definition based on successive map levels allows us to extend this model to higher dimension. Moreover, with progressive definition, we can study each level separately. This simplifies the study of disconnection cases and the proofs of topological map properties. Finally, we provide an incremental extraction algorithm which extracts any map of the hierarchy in a single image scan. Moreover, we show that this algorithm is very efficient by giving the results of our experiments made on artificial images. [Copyright &y& Elsevier]

An orthographic projections-based algorithm was investigated for the traffic monitoring application of tracking a truck moving across a bridge through a sequence of video image frames. The two-dimensional position of a specific marker on the truck for each video frame is found using color thresholding-based segmentation. A coordinate transformation process provides the real-world, three-dimensional location of the marker position for tracking the truck. The procedure is experimentally illustrated with a field bridge. This paper presents the experimental bridge setup, the marker identification technique, the orthographic coordinate transformation algorithm, and web-accessible laboratory exercises developed for image processing and related curricula. [ABSTRACT FROM AUTHOR]

This paper describes various forms of line drawing representation, compares different schemes of quantization, and reviews the manner in which a line drawing can be extracted from a tracing or a photographic image. The subjective aspects of a line drawing are examined. Different encoding schemes are compared, with emphasis on the so-called chain code which is convenient for highly irregular line drawings. The properties of chain-coded line drawings are derived, and algorithms are developed for analyzing line drawings to determine various geometric features. Procedures are described for rotating, expanding, and smoothing line structures, and for establishing the degree of similarity between two contours by a correlation technique. Three applications are described in detail: automatic assembly of jigsaw puzzles, map matching, and optimum two-dimensional template layout. [ABSTRACT FROM AUTHOR]

Noise is ubiquitous in real life and changes image acquisition, communication, and processing characteristics in an uncontrolled manner. Gaussian noise and Salt and Pepper noise, in particular, are prevalent in noisy communication channels, camera and scanner sensors, and medical MRI images. It is not unusual for highly sophisticated image processing algorithms developed for clean images to malfunction when used on noisy images. For example, hidden Markov Gauss mixture models (HMGMM) have been shown to perform well in image segmentation applications, but they are quite sensitive to image noise. We propose a modified HMGMM procedure specifically designed to improve performance in the presence of noise. The key feature of the proposed procedure is the adjustment of covariance matrices in Gauss mixture vector quantizer codebooks to minimize an overall minimum discrimination information distortion (MDI). In adjusting covariance matrices, we expand or shrink their elements based on the noisy image. While most results reported in the literature assume a particular noise type, we propose a framework without assuming particular noise characteristics. Without denoising the corrupted source, we apply our method directly to the segmentation of noisy sources. We apply the proposed procedure to the segmentation of aerial images with Salt and Pepper noise and with independent Gaussian noise, and we compare our results with those of the median filter restoration method and the blind deconvolution-based method, respectively. We show that our procedure has better performance than image restoration-based techniques and closely matches to the performance of HMGMM for clean images in terms of both visual segmentation results and error rate. [ABSTRACT FROM AUTHOR]

The median filter is one of the basic building blocks in many image processing situations. However, its use has long been hampered by its algorithmic complexity of O(r) in the kernel radius. With the trend toward larger images and proportionally larger filter kernels, the need for a more efficient median filtering algorithm becomes pressing. In this correspondence, a new, simple, yet much faster, algorithm exhibiting O(1) runtime complexity is described and analyzed. It is compared and bench- marked against previous algorithms. Extensions to higher dimensional or higher precision data and an approximation to a circular kernel are presented, as well. [ABSTRACT FROM AUTHOR]

A method for enhancing the local contrast of a noisy image using sliding discrete transforms is proposed. A minimum mean-square error estimator in the domain of a sliding transform for noise removal is derived. This estimator is based on a fast inverse sliding transform. Local contrast enhancement is performed by a nonlinear modification of denoised local spectrum coefficients. To provide image processing in real time, a fast recursive algorithm for computing the sliding transform is utilized. The algorithm is based on a recursive relationship between three subsequent local spectra. Computer simulation results using a real image are provided and discussed. [ABSTRACT FROM AUTHOR]