Data hiding is usually achieved by alternating some nonessential information in the host message. A more challenging problem is to hide data in a two - color binary image. Hiding is difficult for the binary image sinc...
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Data hiding is usually achieved by alternating some nonessential information in the host message. A more challenging problem is to hide data in a two - color binary image. Hiding is difficult for the binary image since each of its black or white pixels requires only one bit representation. So that, changing a pixel can be easily detected. In this paper, we propose a new data hiding scheme using the parity of blocks. The original image is partitioned into mxn blocks. The new scheme ensures that for any bit that is modified in the host image, the bit must be adjacent to another bit that has the same value as the former's new value. Thus, the existence of secret information in the host image is difficult to detect. The invisible effect will be achieved by sacrificing some data hiding space, but the new scheme still offers a good data hiding ratio. Specifically, for each m x n block of host image, we will hide one bit of secret data by changing either one bit or without changing any bits in the block.
The proceedings contains 42 papers from the conference of SPIE - wavelets: applications in signals and imageprocessingx - Part two. The topics discussed include: a new local transform without overlaps: a combination...
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The proceedings contains 42 papers from the conference of SPIE - wavelets: applications in signals and imageprocessingx - Part two. The topics discussed include: a new local transform without overlaps: a combination of computational harmonic analysis and PDE;geometric methods for wavelet-based image compression;high-bitrate approximation;new design of orthogonal filter banks using the cayley transform;image registration using threefold orthogonal wavelet;a nonseperable multiwavelet for edge detection;wavelet-based pavement distress detection and evaluation.
The discrete wavelet transform has taken its place at the forefront of research for the development of signal and imageprocessingapplications. These wavelet-based approaches have outperformed existing strategies in ...
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The discrete wavelet transform has taken its place at the forefront of research for the development of signal and imageprocessingapplications. These wavelet-based approaches have outperformed existing strategies in many areas including telecommunication, numerical analysis and, most notably, image/video compression. The authors present an investigation into the design and implementation of 1-D and 2-D discrete biorthogonal wavelet transforms (DBWTs) using a field programmable gate array (FPGA)-based rapid prototyping environment. The proposed architectures for DBWTs are scalable, modular and have less area and time complexity when compared with existing structures. FPGA implementation results based on a xilinx Virtex-2000E device have shown that the proposed system provides an efficient solution for the processing of DBWTs in real-time.
Diffusion wavelets can be constructed on manifolds, graphs and allow an efficient multiscale representation of powers of the diffusion operator that generates them. In many applications it is necessary to have time-fr...
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Diffusion wavelets can be constructed on manifolds, graphs and allow an efficient multiscale representation of powers of the diffusion operator that generates them. In many applications it is necessary to have time-frequency bases that are more versatile than wavelets, for example for the analysis, denoising and compression of a signal. In the Euclidean setting, wavelet packets have been very successful in many applications, ranging from image denoising, 2- and 3-dimensional compression of data (e.g., images, seismic data, hyper-spectral data) and in discrimination tasks as well. Till now these tools for signalprocessing have been available mainly in Euclidean settings and in low dimensions. Building upon the recent construction of diffusion wavelets, we show how to construct diffusion wavelet packets, generalizing the classical construction of wavelet packets, and allowing the same algorithms existing in the Euclidean setting to be lifted to rather general geometric and anisotropic settings, in higher dimension, on manifolds, graphs and even more general spaces. We show that efficient algorithms exists for computations of diffusion wavelet packets and discuss some applications and examples. (C) 2006 Elsevier Inc. All rights reserved.
A wavelet-domain image resolution enhancement algorithm which is based on the estimation of detail wavelet coefficients at high resolution scales is proposed. The method exploits wavelet coefficient correlation in a l...
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A wavelet-domain image resolution enhancement algorithm which is based on the estimation of detail wavelet coefficients at high resolution scales is proposed. The method exploits wavelet coefficient correlation in a local neighbourhood sense and employs linear least-squares regression to estimate the unknown detail coefficients. Results show that the proposed method is considerably superior to conventional image interpolation techniques, both in objective and subjective terms, while it also compares favourably with competing methods operating in the wavelet domain.
Classical linear wavelet representations of images have the drawback that they are not optimally suited to represent edge information. To overcome this problem, nonlinear multiresolution decompositions have been desig...
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Classical linear wavelet representations of images have the drawback that they are not optimally suited to represent edge information. To overcome this problem, nonlinear multiresolution decompositions have been designed to take into account the characteristics of the input signal/image. In our previous work(20,22,23) we have introduced an adaptive lifting framework, that does not require bookkeeping but has the property that it processes edges and homogeneous image regions in a different fashion. The current paper discusses the effects of quantization in such an adaptive wavelet decomposition. We provide conditions for recovering the original decisions at the synthesis and for relating the reconstruction error to the quantization error. Such an analysis is essential for the application of these adaptive decompositions in image compression.
Low bit rate image coding is an important problem regarding applications such as storage on low memory devices or streaming data on the internet. The state of the art in image compression is to use two-dimensional (2-...
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Low bit rate image coding is an important problem regarding applications such as storage on low memory devices or streaming data on the internet. The state of the art in image compression is to use two-dimensional (2-D) wavelets. The advantages of wavelet bases lie in their multiscale nature and in their ability to sparsely represent functions that are piecewise smooth. Their main problem on the other hand, is that in 2-D wavelets are not able to deal with the natural geometry of images, i.e. they cannot sparsely represent objects that are smooth away from regular submanifolds. In this paper we propose an approach based on building a sparse representation of the edge part of images in a redundant geometrically inspired library of functions, followed by suitable coding techniques. Best N-terms non-linear approximations in general dictionaries is, in most cases, a NP-hard problem and sub-optimal approaches have to be followed. In this work we use a greedy strategy, also known as Matching Pursuit to compute the expansion. The residual, that we suppose to be the smooth and texture part, is then coded using wavelets. A rate distortion optimization procedure chooses the number of functions from the redundant dictionary and the wavelet basis. (C) 2005 Published by Elsevier B.V.
In computer vision and imageprocessing, edge detection concerns the localization of significant variations of the grey level image and the identification of the physical phenomena that originated them. It is difficul...
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In computer vision and imageprocessing, edge detection concerns the localization of significant variations of the grey level image and the identification of the physical phenomena that originated them. It is difficult to design a general edge detection algorithm which performs well in many contexts and captures the requirements of subsequent processing stages. Consequently, over the history of digital imageprocessing, a variety of edge detectors have been devised which differ in their mathematical and algorithmic properties. In this paper, we briefly describe some existing edge detectors. We investigate the wavelet transform with direction which is related to the Canny and Marr-Hildreth's edge detectors. This wavelet transform has a remarkable feature that plays an important role in imageprocessingapplications. We discuss its applications to edge and target detections. The direction effect enables the wavelets to analyze the directional features of images. Algorithms and experiments for edge and target detections have been developed based on the theory. And, very promising results have been shown in imageprocessing.
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