Signal and image restoration; information-theoretic approaches. The used chaotic map is not fixed but changeable via the wheel-switch structure according to the controlling sequence. For that reason, this book is as experimentally reproducible as possible. An efficient fast parametric slant-Haar transform is introduced and its computational complexity is discussed. Recent research results are presented, regarding the formulation of the restoration problem as a convex programming problem, the implementation of restoration algorithms using artificial neural networks, the derivation of non stationary image models compound random fields and their application to image estimation and restoration, the development of algorithms for the simultaneous image and blur parameter identification and restoration, and the development of algorithms for restoring scanned photographic images. In many instances one has the feeling that the degradations in the image are such that relevant information is close to being recognizable, if only the image could be sharpened just a little. Responsibility: Joseph Noonan and Prabahan Basu.
To fill that need but avoid a rehash of the many fine existing books on this subject, this reference focuses on algorithms rather than theories or applications. Review of signal restoration techniques -- Chapter 4. A new nonlinear filter is proposed which outperforms which are very visible under a high compression rate. The proposed method performs the recognition by extracting the characters from the whole word, avoiding the segmentation process. The system inputs are single instances of low resolution text images which must be restored and transformed into data readable text. The error bound will help us decide if the number of moment constraints is adequate for modeling the uncertainty in the system under study. In this paper, we present a novel algorithm to estimate direction and length of motion blur, using Radon transform and fuzzy set concepts.
The slantlet wavelet has been successfully applied in compression and denoising. The estimated statistics and observed distribution from 50,000 experiments match the theoretical ones. In this paper, we extend the scope of super-resolution imaging to acquire depth estimates at high spatial resolution simultaneously. In this paper, we introduce a novel signal-dependent parameterization of Slant-Hadamard transform. Simulations and comparisons demonstrate that the proposed method outperforms several state-of-the-art methods.
A transmit signal is split into a plurality of streams for transmission in occupied channels according to power allocation and modulation scheme. The behavior of the parametric Haar slantlet transforms in signal and image denoising is presented. The principles of the presented scheme could be applied to provide security for a variety of systems including image, audio and video systems. It introduces new concepts and frameworks that demonstrate how neural networks, support vector machines, fuzzy logic, and evolutionary algorithms can be used to address new challenges in image processing, including low-level image processing, visual content analysis, feature extraction, and pattern recognition. The parametric slant-Haar transforms are compared to the Karhunen-Loeve transform.
The authors provide new approaches to this problem, as well as new interpretations for existing techniques. The restored image, which is constrained by the given low-resolution image, is generated by iteratively solving a nonlinear optimization problem. Sparsity of transform domain coefficients is a critical requirement for algorithms employing wavelet transforms to pre-process signals or images. As a result, matching a template becomes equivalent to the problem of filtering the wavelet transformed image using a set of band-pass filters. To the best of our knowledge, the idea of Radon domain restoration is new. This new subband coder significantly reduces the ringing- effect distortions inherent to the linear standard coders and the smearing-effect distortions associated with the nonlinear, morphological subband coders.
There is an ongoing need to develop image denoising approaches that suppress noise while maintaining edge information. Transform methods have played an important role in signal and image processing applications. Divided into five major parts, the book begins by introducing the concepts and definitions necessary to understand computer imaging. This paper introduces a new system for expanding the resolution of text images. Modern methods of image restoration are most often based on regularization theory. The initially restored image is generated by simple techniques, and is then improved by integrating a variety of features obtained through image analysis.
Practical implications — The so-called known moments problem addressed in this paper has a variety of applications in learning, blind equalization and neural networks. At low bit rates, the linear subband coders are susceptible to the ringing effect, which causes rippling and blurring around the edges in the images. This parametric class performs better in generalized Wiener filtering than existing Slant-Hadamard transforms for the first-order Markov and the generalized image correlation models. Contours of characters are then modified in terms of gradient magnitudes and curvatures along the contours. A general approach to solving a number of detection and estimation problems utilizing concepts from information theory is developed, and the theoretical development of the approach as well as important applications is given. We show that the new technique performs better than the slantlet wavelet transform in denoising for piecewise constant signals. In this paper, we investigate the use of a family of transform domains in embedding hidden data.
Accurate image expansion is important in many areas of image analysis. A partially signal dependent parametric slant-Haar transform is also introduced. The field of image restoration is concerned with the estimation of uncorrupted im ages from noisy, blurred ones. Although there are many textbooks addressing the image identification and restoration problem in a general image processing setting, there are hardly any texts which give an indepth treatment of the state-of-the-art in this field. This monograph discusses iterative procedures for identifying and restoring images which have been degraded by a linear spatially invari ant blur and additive white observation noise. Similar ideas can be applicable to other patchwise image denoising techniques.
Worldwide, there is no lack of conferences on image analysis. Experimental results demonstrate that text images created by this new algorithm improve optical character recognition accuracy more than images obtained by existing expansion methods. An image restoration subsystem generates a resolution-expanded image from the image segments and negates degradation effects of the low-resolution image by transforming the image segments from a first domain to a second domain and deconvolving the transformed image segments in the second domain to determine parameters of the low-resolution image. The discrete slantlet wavelet transform is carried out by an existing filterbank which lacks a tree structure and has a complexity problem. At low bit rates, the linear subband coders are susceptible to the ringing effect, which causes rippling and blurring around the edges in the images.