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International audience; This paper proposes a Tone Mapping (TM) approach converting a High Dynamic Range (HDR) image into a Low Dynamic Range (LDR) image while preserving as much information of the HDR image as possible to ensure a good LDR image visual quality. This approach is based on a separable near optimal lifting scheme using an adaptive powerful prediction step. The latter relies on a linear weighted combination depending on the neighboring coefficients extracting then the relevant finest details in the HDR image at each resolution level. Moreover the approximation and detail coefficients are modified according to the entropy of each subband. The pixel's distribution of the coarse reconstructed LDR image is then adjusted according to a perceptual quantizer with respect to the human visual system using a piecewise linear function. Simulation results provide good results, both in terms of visual quality and TMQI metric, compared to existing competitive TM approaches.

Problem statement: Digital images play an important role both in daily life applications as well as in areas of research and technology. Due to the increasing traffic caused by multimedia information and digitized form of representation of images; image compression has become a necessity. Approach: Wavelet transform has demonstrated excellent image compression performance. New algorithms based on Lifting style implementation of wavelet transforms have been presented in this study. Adaptively is introduced in lifting by choosing the prediction operator based on the local properties of the image. The prediction filters are chosen based on the edge detection and the relative local variance. In regions where the image is locally smooth, we use higher order predictors and near edges we reduce the order and thus the length of the predictor. Results: We have applied the adaptive prediction algorithms to test images. The original image is transformed using adaptive lifting based wavelet transform and it is compressed using Set Partitioning In Hierarchical Tree algorithm (SPIHT) and the performance is compared with the popular 9/7 wavelet transform. The performance metric Peak Signal to Noise Ratio (PSNR) for the reconstructed image is computed. Conclusion: The proposed adaptive algorithms give better performance than 9/7 wavelet, the most popular wavelet transforms. Lifting allows us to incorporate adaptivity and nonlinear operators into the transform. The proposed methods efficiently represent the edges and appear promising for image compression. The proposed adaptive methods reduce edge artifacts and ringing and give improved PSNR for edge dominated images.

This paper describes a new multi-resolution approach for the blind source separation of convolutive image mixtures in transform domain. The proposed method uses an Adaptive Quincunx Lifting Scheme based on wavelet decomposition and a Complex ICA unmixing algorithm. It proceeds in three steps: first, the mixed signals are decomposed by an adaptive lifting scheme. Then, the unmixing algorithm is applied to the more relevant component. The unmixed signals are, thereafter, reconstructed using an inverse transform. Experiments carried out on images from various origins showed that the proposed method yields better results than many widely used blind source separation algorithms.

The advances in 3D technologies and applications is leading to more research effort for developing and improving stereo image compression techniques. In this paper, we propose a specialized transform for stereo image residuals. The particularities of the residual image are considered and consequently an adaptive Lifting Scheme is used. The novelty of this work is the use of the edge or orientation information in the disparity map to adapt the proposed scheme. An edge/orientation map is generated from the disparity map, and for each pixel, the direction or the length of filtering are tuned by the edge/orientation map. The proposed schemes does not require any extra overhead information. Comparison between the Directional and the Hybrid Lifting Scheme is made compared to traditional 5/3 transform. Experimental results show an improvement of 0.13 bpp compared to the traditional 5/3 transform and 0.18 bpp to Haar transform.

The Lifting Scheme provides us a flexible and easy way for constructing wavelet, which enables us to construct wavelet according to application needs. Its flexibility allows us to introduce nonlinearity into wavelet and modify it based on signal processed. The Adaptive Lifting Scheme provided the way of adjusting the filters via updater U and predictor P in lifting stages according to signal characters. But the lackness of self-learning ability of existing Adaptive Lifting Scheme is a big shortcoming. In this paper, BP neural networks is introduced into lifting scheme. It is used to replace the updater U and predictor P respectively. Experiment shows that BP neural networks work well in lifting stages.

This paper proposes an image enhancement method which is based on space-adaptive lifting scheme. The space-adaptive lifting scheme is designed with update-first strategy and the spatial domain adaptive Wiener filter is used for subband image enhancement. The space-adaptive lifting scheme is superior to the scale-adaptive one in that, the prediction stage is adapted to the signal structure pixel-by-pixel (not scale-by-scale) which results in a better signal representation and better image enhancement. A novel adaptive prediction method is proposed in the 2-D lifting scheme. The method adaptively chooses the best prediction filter (predictor), among a set of predictors, based on minimizing the prediction error (or detail coefficient). The experimental results and objective quality test results prove the accomplishment of the proposed image enhancement method.

For ECG QRS complexes detection, an Adaptive Lifting Scheme (ALS) has been developed and successfully implemented in Field Programmable Gate Array (FPGA). Realized under XUP Virtex-II Pro environment, the system consists of Top Configuration Module, Detection module and ALS module. Tested by the samples generated from the MIT-BIH ECG Database, its detection accuracy is higher than 99.681%, fulfilling ECG signal processing requirements. Comparing with the same algorithm realized with C codes under TI's TMS320VC5509A DSP system, more than 20% of processing time is saved.

The signals that are encountered in practice are not smooth signals and classical wavelet transforms cannot deal with the discontinuities encountered in the signals. Such singularities tend to give rise to large coefficients in their proximity, which is undesirable for signal compression. To overcome such problems one can consider the local variance during the decomposition of the signal. There are various ways to build adaptivity into the decomposition of a signal. The best algorithm, selects a wavelet basis by minimizing a concave cost function such as the entropy. In such an approach, the filter coefficients are fixed for entire block of data as the optimization criterion is global. Here, the decompositions are considered where the filter coefficients vary locally, taking into account of local signal variations. The approach taken by Chan and Zhou [1] suggests that instead of changing the filter coefficients, the input signal is changed in the proximity of discontinuities through an extrapolation procedure. By registering these changes, the original signal can be recovered at synthesis level. By extending the approach of Chan and Zhou in the present work, the SPIHT, Classical Lifting scheme and Adaptive Lifting schemes are analyzed for achieving better compression ratio and PSNR for satellite Rural and Urban imageries. The results are presented in the paper.

Image edge is because the gradation is the result of not continuously, is image's information basic characteristic, is also one of hot topics in image processing. This paper analyzes traditional arithmetic of image edge detection and existing problem, uses adaptive lifting wavelet analysis, adaptive adjusts the predict filter and the update filter according to information's partial characteristic, thus realizes the processing information accurate match; at the same time, improves the wavelet edge detection operator, realizes one kind to be suitable for the adaptive lifting scheme image edge detection's algorithm, and applies this method in the medicine image edge detection. The experiment results show that this paper's algorithm is better than the traditional algorithm effect.

This paper presents an adaptive lifting scheme, which performs integer-to-integer wavelet transform, for lossless image compression. We optimize the coefficients of the predict filter in the lifting scheme to minimize the entropy of the detail coefficients. The optimized coefficients depend on the auto-correlation structure of the image. The presented lifting scheme adapts to both horizontal and vertical directions of the image. We obtain experimental results using different types of images. These results show that the presented adaptive transform is a competitive wavelet transform for lossless image compression.

International audience; Many existing works related to lossy-to-lossless image compressionare based on the lifting concept. In this paper, we present a sparse optimization technique based on recent convex algorithms and appliedto the prediction filters of a two-dimensional non separable liftingstructure. The idea consists of designing these filters, at each resolution level, by minimizing the sum of the ℓ1-norm of the three detailsubbands. Extending this optimization method in order to performa global minimization over all resolution levels leads to a new optimization criterion taking into account linear dependencies betweenthe generated coefficients. Simulations carried out on still imagesshow the benefits which can be drawn from the proposed optimization techniques.

International audience; Lifting schemes (LS) were found to be efficient tools for image coding purposes. Since LS-based decompositions depend on the choice of the prediction/update operators, many research efforts have been devoted to the design of adaptive structures. The most commonly used approaches optimize the prediction filters by minimizing the variance of the detail coefficients. In this article, we investigate techniques for optimizing sparsity criteria by focusing on the use of an a"" (1) criterion instead of an a"" (2) one. Since the output of a prediction filter may be used as an input for the other prediction filters, we then propose to optimize such a filter by minimizing a weighted a"" (1) criterion related to the global rate-distortion performance. More specifically, it will be shown that the optimization of the diagonal prediction filter depends on the optimization of the other prediction filters and vice-versa. Related to this fact, we propose to jointly optimize the prediction filters by using an algorithm that alternates between the optimization of the filters and the computation of the weights. Experimental results show the benefits which can be drawn from the proposed optimization of the lifting operators.

This paper investigates an adaptive lifting scheme for wavelet-based image compression, which performs update before prediction, and then we proposes an improved adaptive update and prediction scheme to better exploit the correlation among neighboring pixels. Simulation results show that the proposed scheme generates in the transformed domain lower coefficients' entropy, lower high-frequency coefficients' energy and more zero values, which make it useful for image compression applications.

High SNR (signal to noise ratio) image is deeply needed in the precise analysis of IC wafer micro-image; current denoising algorithms cant reach the analytic precision in some level. In tins paper, one image denoising algorithms is putted forward based on adaptive lifting scheme, the construction of Haar wavelet and CDF (2,2) is given, the signal is decomposed by wavelet base Haar or wavelet base CDF (2,2) adaptively along four directions (horizon, verticality, 45 degree and 135 degree) in the step of predicting, the wavelet coefficients are calculated separately at each direction, all the thresholds are gained using wavelet soft-thresholding principle, the optimal thresholds minimize the error of the result as compared to these the signal is decomposed along horizon and verticality. Finally the definition of the image is appraised with gray gradient judging function, the experimental data shows that the focus error is no more 4 um, the display definition of the image is improved.

International audience; Many existing works related to lossy-to-lossless image compression are based on the lifting concept. However, it has been observed that the separable lifting scheme structure presents some limitations because of the separable processing performed along the image lines and columns. In this paper, we propose to use a 2D non separable lifting scheme decomposition that enables progressive reconstruction and exact decoding of images. More precisely, we focus on the optimization of all the involved decomposition operators. In this respect, we design the prediction filters by minimizing the variance of the detail signals. Concerning the update filters, we propose a new optimization criterion which aims at reducing the inherent aliasing artefacts. Simulations carried out on still and stereo images show the benefits which can be drawn from the proposed optimization of the lifting operators.

This paper proposes a new adaptive scheme for stereo image coding . The original left and right images are jointly coded in order to exploit the high correlation between the two images for a lossless coding. Our goal is to design an efficient transform that reduces the existing redundancy in the stereo pair. This approach is inspired by the Lifting Scheme (LS). First, illumination change compensation has been made. Secondly, the prediction filtering is locally adapted and is based on local horizontal and vertical gradient information. In the sense that small gradient evokes predictability in the same direction. The coefficients of the predictor are then optimized. Experimental results show improvement in term of performance and complexity compared to other proposed methods.

In this paper, an improved adaptive update and adaptive prediction algorithm is put forward. The method makes full use of the correlation of neighboring pixels, which the approximation band and the detail bands obtained by a polyphase decomposition of an original image perform the proposed adaptive update step one by one, then the detail bands perform the adaptive prediction algorithm. The advantage of this method is its discrimination ability to use gradient operator for deciding the smooth areas and the discontinued areas, and choosing the different prediction filter according to the local characteristics. Simulation results show that the proposed lifting scheme algorithm generates lower entropy of high-frequency coefficients, lower energy of high-frequency coefficients and more zero values in wavelet transform domain, which makes it useful for digital image compression.

In this paper, a new adaptive lifting wavelet transform scheme is proposed. The main idea of the scheme is that the choice of predicting operator is triggered by local gradient information of the signal. Such an adaptive scheme can adapt itself well to the analyzed signal, and both the smooth properties and the sharp transition characteristics of the signal can be reserved perfectly by using this adaptive lifting wavelet transform. Simulation examples illustrate that the new scheme is effective.

One of the methods introduced for establishing hidden communication is steganography. One of the problems in these methods is the security of transferring password key used for steganography between sender and receiver of secure data. In this paper a new method is proposed for solving this problem using CAPTCHA method. In this method the password key is drawing in the image and the data are hidden in the image by this password key. So the human receiver can recognize the password from the image and extract the data from the image using this password, but a computer program cannot do above operation. By this method the user does not need to memorize any password for extracting the data.