Graphical statistical modeling of wavelet coefficients and its applications

We introduce a simple spatially adaptive statistical model for wavelet image coefficients and apply it to image denoising. Our model is inspired by a recent wavelet image compression algorithm, the estimation-quantization (EQ) coder. We model wavelet image coefficients as zero-mean Gaussian random variables with high local correlation. We assume a marginal prior distribution on wavelet coefficients variances and estimate them using an approximate maximum a posteriori probability rule. Then we apply an approximate minimum mean squared error estimation procedure to restore the noisy wavelet image coefficients. Despite the simplicity of our method, both in its concept and implementation, our denoising results are among the best reported in the literature.

The first quarter of 2007 started as an active season for the real-time image processing community. The annual SPIE Conference on Real-Time Image Processing, which was held in San Jose, CA, highlighted recent developments in this growing branch of image processing. As one of the few regular events with its focus matching that of JRTIP, one witnessed the growth in applications of real-time image and video processing. The conference served as an effective instrument to foster the dissemination of recent results and to leverage the cohesion of the real time image processing community. The preparations for the next SPIE Conference on Real-Time Image Processing in 2008 are already progressing and its call-for-papers is included at the end of this issue. In 2007, JRTIP is going to seek involvement of more scientists in the field of real-time image and video processing. After 1 year of operation, the editors-in-chief have started to recruit additional associate editors to enlarge the expertise base of the editorial board, to solicit manuscript submissions from potential authors, and to make the reviewing process more efficient. Interested colleagues should contact the editors-in-chief expressing their Associate Editor candidature in support of one or more fields within the focus of JRTIP. In addition, they wish to encourage researchers to act as guest editors for special issues on any real-time image or video processing related topics of interest to them. It is possible to schedule two special issues in 2007, provided that a draft call-for-papers is sent to the editors-in-chief as soon as possible. The third issue of JRTIP presents four articles, two are survey papers and two are original research papers. These articles address different real-time image processing perspectives ranging from algorithmic to hardware issues. The first survey paper deals with the real-time filtering in 2D and 3D using ordered statistics that are supported by a rich set of experimental results derived from several algorithmic variations. Performance measurements are used to demonstrate the effect of noise suppression and preservation of fine image structures and to show the DSP implementation of denoising filters. The second survey paper introduces a non-invasive analysis of dynamic processes in living cells using microscopic computer vision techniques for understanding the interactions between cellular and molecular functional units and for providing novel insights into the cell biology. The third article is an original research paper covering a machine vision application involving vision-based position sensing for aerial refueling of unmanned aerial vehicles in real-time. A full image processing chain is investigated and tested in a MATLAB/Simulink soft real-time environment as well as in a Linux/RTAI hard real-time environment. The feasibility of the real-time implementation is shown using off-the-shelf commercial hardware. The fourth article is another original research paper describing a hardware architecture for real-time fractal coding for image compression. The introduced parallel hardware architecture contains units for domain M. F. Carlsohn (&) Ingenieurberatung fur Computer Vision und Bildkommunikation, Am Heiddamm 36g, 28355 Bremen, Germany e-mail: matthias.carlsohn@t-online.de

Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing shows you how robust subspace learning and tracking by decomposition into low-rank and sparse matrices provide a suitable framework for computer vision applications. Incorporating both existing and new ideas, the book conveniently gives you one-stop access to a number of different decompositions, algorithms, implementations, and benchmarking techniques. Divided into five parts, the book begins with an overall introduction to robust principal component analysis (PCA) via decomposition into low-rank and sparse matrices. The second part addresses robust matrix factorization/completion problems while the third part focuses on robust online subspace estimation, learning, and tracking. Covering applications in image and video processing, the fourth part discusses image analysis, image denoising, motion saliency detection, video coding, key frame extraction, and hyperspectral video processing. The final part presents resources and applications in background/foreground separation for video surveillance. With contributions from leading teams around the world, this handbook provides a complete overview of the concepts, theories, algorithms, and applications related to robust low-rank and sparse matrix decompositions. It is designed for researchers, developers, and graduate students in computer vision, image and video processing, real-time architecture, machine learning, and data mining.

As modern image and video processing applications handle increasingly higher image resolutions, the buffering requirements between communicating functional modules increase correspondingly. The performance and cost of these applications can change dramatically depending on the implementation methods for FIFO buffers and the data delivery methods between modules. This paper introduces a new FIFO hardware mapping algorithm based on pointer-based token delivery from dataflow semantics for image and video processing applications. This approach significantly improves the performance of dataflow based implementation of image and video processing systems, and allows effective prediction of changes in performance and buffer memory requirements associated with changes in image resolution. Our pointer-based token delivery method allows indirect token delivery between actors by pointers in conjunction with use of a shared memory. Each pointer references a data block stored in the shared memory. In pointer-based token delivery, a buffer can be configured to be implemented as the combination of a small, fast FIFO and a larger, relatively cheap shared memory while providing an attractive trade-off between performance and hardware cost. We present the complete semantics of our pointer-based modeling method, systematic techniques for mapping representations using these semantics into efficient implementations, and experimental results that demonstrate the performance of the proposed pointer-based techniques.

Synthetic aperture radar (SAR) images are inherently affected by a signal multiplicative noise known as speckle, which is due to the radar wave coherence. In this paper, we introduce a new method to reduce speckle noise in SAR images, based on statistical modeling of wavelet coefficients. We use two-dimensional generalized autoregressive conditional heteroscedasticity (GARCH) model for statistical modeling of images wavelet coefficients. By using this model, we are capable of taking into account important characteristics of wavelet coefficients, such as heavy tailed marginal distribution, and the dependencies between the coefficients. Furthermore, we use maximum a-posteriori (MAP) estimator for estimating the clean image wavelet coefficients. Finally, we compare our proposed method with some denoising methods, and quantify the achieved performance improvement.

Multidimensional Signal, Image, and Video Processing and Coding

The objective of this work is to design and implement an Image and Video Processing Platform (IVPP) on FGPAs using PICO based HLS. This hardware/software codesign platform has been implemented on a Xilinx Virtex-5 FPGA. The video interface blocks are done in RTL and the initialization phase is done using a MicroBlaze processor allowing the support of multiple video resolutions. This paper discusses the architectural building blocks showing the flexibility of the proposed platform. This flexibility is achieved by using a new design flow based on PICO. IVPP allows custom-processing blocks to be plugged-in to the platform architecture without modifying the front-end (capturing video data) and back-end (displaying processed output). This paper presents several examples of video processing applications, such as a Canny edge detector, motion detector and object tracking that have been realized using IVPP for real-time video processing.

This paper proposes a new method for SAR (Synthetic Aperture Radar) image despeckling based on statistical model of wavelet coefficients combined with modification to them according to maximum-modulus criterion. In the method, wavelet coefficients of logarithmic image are firstly modeled as mixture density of two Gaussian (MG) distributions with zero mean. Secondly, in order to incorporate the spatial dependencies into the despeckling procedure, Hidden Markov Tree model (HMT) is explored and Expectation Maximization (EM) algorithm is adopted to estimate model parameters. Bayes Minimum mean square error (Bayes MMSE) method is used to estimate the wavelet coefficients free of noise. The wavelet coefficients are updated according to a criterion whether the coefficient is a significant one or not. 2D inverse DWT and exponential transform are performed on the updated coefficients to get denoised SAR image. Experimental Results using real SAR images demonstrate that the method can not only reduce the speckle but also preserve edges and radiometric scatter points.

In this chapter applications of image and video processing to navigation of mobile robots are presented. During the last years some impressive real time applications have been showed to the world, such as the NASA missions to explore the surface of Mars with autonomous vehicles; in those missions, video and image processing played an important role to rule the vehicle. Algorithms based on the processing of video or images provided by CCD sensors or video cameras have been used in the solution of the navigation problem of autonomous vehicles. In one of those approaches, a velocity field is designed in order to guide the orientation and motion of the autonomous vehicle. A particular approach to the solution of the navigation problem of an autonomous vehicle is presented here. In the first section of this introduction a state of the art review is presented, after it, the proposed algorithm is summarized; the following sections present the procedure. Finally, some experimental results are shown at the end of the chapter.

As wireless video products evolve, they demand more sophisticated processing at higher resolutions and frame rates. Computational performance and energy efficiency have become critical design issues. This paper presents the Quantized Color Pack eXtension (QCPX) combined with a loop unrolling (LU) technique to improve execution performance and energy efficiency of color image and video processing applications. QCPX applied to a 32-bit datapath processor supports parallel operations on two packed 16-bit YCbCr (Y: luminance, Cr and Cb: chrominance) color pixels, providing greater subword-level parallelism by increasing the number of smaller color pixels packed into a word. Instruction-level parallelism can be further enhanced through loop unrolling. These techniques provide greater performance and efficiency for multimedia workloads on mobile systems. Experimental results on a set of media benchmark applications indicate that the LU plus QCPX-optimized version achieves a speedup ranging from 3.8 to 7.9 while reducing the energy consumption from 76% to 87% over the baseline version on identically configured, dynamically scheduled ILP superscalar processors. The LU plus QCPX-optimized version also outperforms the LU plus MDMX-like (MIPS’s multimedia extension) version.

This research explores the design and implementa- tion of an image and video processing platform using recon- figurable FPGA technology. With the increasing demand for real- time, high-performance multimedia processing in modern applications, a flexible and efficient solution is essential. The project utilizes an FPGA-based platform to handle image and video data processing through edge detection, image scaling, and real-time motion detection techniques. By integrating custom- built hardware components, this study aims to provide a low- power, high-throughput solution that meets the requirements of both image and video processing tasks. Materials and methods involved using Xilinx Virtex-II Pro FPGA for hardware imple- mentation, alongside embedded memory processors and multi- functional design elements such as zoom-in/out utilities. The system was evaluated through several experimental procedures, using various image and video datasets, to measure performance in terms of speed, resource utilization, and power consumption. Results indicated that the system achieved notable improvements in terms of efficiency, utilizing 25% of logic resources, 55% of on- chip memory, and 180mW of power. The discussion focuses on the adaptability of FPGA-based systems in meeting the diverse needs of multimedia processing, emphasizing scalability and low power consumption. In conclusion, the reconfigurable FPGA platform provides a versatile and efficient environment for image and video processing. The system’s resource efficiency, scalability, and potential for further enhancements make it a promising candidate for future multimedia applications. Keywords-Reconfigurable FPGA Architecture, Video and Image Processing, Edge Detection, Image Rescaling.

Wavelet packets provide a flexible representation of data, which has been proved to be very useful in a lot of applications in signal, image and video processing. In particular, in image and video coding, their ability to best capture the input content features can be exploited by designing appropriate optimization criteria. In this paper, we introduce joint wavelet packets for groups of frames, allowing to provide a unique best basis representation for several frames and not one basis per frame, as classically done. Two main advantages are expected from this joint representation. On the one hand, bitrate is spared, since a single tree description is sent instead of 31 per group of frames (GOP) - when a GOP contains, for example, 32 frames. On the other hand, this common description can characterize the spatio-temporal features of the given video GOP and this way can be exploited as a valuable feature for video classification and video database searching. A second contribution of the paper is to provide insight into the modifications necessary in the best basis algorithm (BBA) in order to cope with biorthogonal decompositions. A computationally efficient algorithm is deduced for an entropy-based criterion.

Many modern applications in important application domains, as communication, image and video processing, multimedia, etc. involve much data-level parallelism (DLP). Therefore, adequate exploitation of DLP is highly relevant. This paper focuses on effective and efficient exploitation of DLP for the synthesis of vector VLIW ASIP processors. We propose analytical energy models in order to rapidly estimate the energy consumption of a nested loop executed on a VLIW ASIP with respect to different vector widths. The models perform a rapid and relatively accurate energy consumption estimation through combining the relevant information on the application and implementation technology. The analytical energy models are experimentally validated and the validation results are discussed.

In this paper, we propose an efficient memory partitioning algorithm for parallel data access via data reuse. We found that for most of the applications in image and video processing, a large amount of data can be reused among different iterations in a loop nest. Motivated by this observation, we propose to cache these reusable data by on-chip registers. The on-chip registers used to cache the re-fetched data can be organized as chains of registers. The non-reusable data are then partitioned into several memory banks by a memory partition algorithm. We revise the existing padding method to cover cases occurring frequently in our method that some components of partition vector are zeros. Experimental results have demonstrated that compared with the state-of-the-art algorithms the proposed method can reduce the required number of memory banks by 59.8% on average. The corresponding resources for bank mapping is also significantly reduced. The number of LUTs is reduced by 78.6%. The number of Flip-Flops is reduced by 66.8%. The number of DSP48Es is reduced by 41.7%. Moreover, the storage overheads of the proposed method are zeros for most of the widely used access patterns in image filtering.

This paper presents the derivation of the Duality Property for the DCT-IV, which is a finite-length discrete transform having applications in Digital Signal, Image and Video Processing. This property can be applied for finding the temporal domain signal from its frequency domain counterpart or mutatis mutandis if an existing DCT-IV pair is known to exist, thus receding considerable computational gruntwork and disbursement substantially.