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This paper presents a novel algorithm for detecting user-selected objects in given test images based on a new adaptive lifting scheme transform. Given an object as a template, we first select a set of coefficients as object features in the wavelet transform domain and then build an adaptive transform based on the selected features. The goal of the new adaptive transform is to vanish the selected features in the transform domain. After applying both non-adaptive and adaptive transforms to a given test image, the corresponding transform domain coefficients are compared for detecting the object of interest. In addition, the proposed detection algorithm is combined with the proper log-polar mapping model in the parametric template space to attain rotation/scale invariance property. Finally, we have verified the properties of our proposed algorithm with experimental results.

This paper proposes an image enhancement method based on space-adaptive, 2-D lifting scheme. In the space-adaptive update-first lifting scheme, the prediction stage is adapted to the signal structure point-by-point which results in a better signal representation and enhancement result. In this paper, a novel edge-sensitive adaptive prediction method is introduced in the 2-D lifting framework. The method adaptively chooses the best predictor among a set of predictors minimizing the prediction error. The proposed prediction method is sensitive to both even and odd indexed edge pixels in the 2-D lifting context. The bivariate shrinkage which assumes the dependence of the subband wavelet coefficients is used for subband image enhancement. As an objective quality measure, the peak signal-to-noise ratio test is applied to the results of the proposed image enhancement algorithm. Results of the proposed algorithm are compared with those of the VisuShrink, BayesShrink, and NorShrink. Experimental and objective quality test results prove the superior performance of the proposed image enhancement method.

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.

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.

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.

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.

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.

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 the subband coding of image, direction of image features has already emphasized to want a function. In this paper, we propose a adaptive lifting transform (ALT) that adapts the direction filter to image based on the lifting scheme. In addition, We design a lossless and loss compression parallel system, which realized the energy of high frequency subbands as few as possible. Experimental results report outstanding to the conventional LWT for typical test images.

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.

Wavelet transform has demonstrated excellent compression performance with natural images. The framework of lifting offers the flexibility for developing adaptive wavelet transforms. In this paper, adaptive prediction and update techniques based on the local properties of the images are proposed. A non-linear wavelet transform that chooses the number of vanishing and preserving moments in the prediction and update lifting steps adaptively according to the underlying local signal characteristics is presented. The spatial adaptivity criterion is based on the optimum interpolating function and Minimum Mean Square Error (MMSE). This adaptive lifting transform appears promising for image compression.

This paper presents a new adaptive lifting scheme transform for detecting user-selected objects in a sequence of images. In our algorithm, we first select a set of object features in the wavelet transform domain and then build an adaptive transform by using the selected features. The adaptive transform is constructed based on adaptive prediction in a lifting scheme procedure. Adaptive prediction is performed such that, the large coefficients in the high-pass component of the non-adaptive transform vanishes in the high-pass component of the adaptive transform. Finally, both the non-adaptive and adaptive transforms are applied to a given test image and the transform domain coefficients are compared for detecting the object of interest. It is shown that the presented algorithm is robust to the noisy environments with reasonable signal-to-noise ratio. We have verified our claims with experimental results on noisy 1-D signals and images.

In this paper, we propose novel adaptive wavelet filter bank structures based on the lifting scheme. The filter banks are nonseparable, based on quincunx sampling, with their properties being pixel-wise adapted according to the local image features. Despite being adaptive, the filter banks retain a desirable number of primal and dual vanishing moments. The adaptation is introduced in the predict stage of the filter bank with an adaptation region chosen independently for each pixel, based on the intersection of confidence intervals (ICI) rule. The image denoising results are presented for both synthetic and real-world images. It is shown that the obtained wavelet decompositions perform well, especially for synthetic images that contain periodic patterns, for which the proposed method outperforms the state of the art in image denoising.

An adaptive lifting scheme is proposed, in which the intersection of confidence intervals (ICI) rule is used to determine the wavelet support. The adaptation is performed on a point-by-point basis and the ICI rule is used to prevent the support from spanning across the edges in a signal, in which the edge is considered to be any sudden change in the signal statistics. It is shown that the proposed method preserves the edges well, and the overall denoising performance is much better than in the other considered methods.

Wavelet transforms found widespread application in signal denoising. Many adaptive algorithms were proposed to improve their performance, especially about edges in a signal. In this study, the authors propose a novel denoising method based on adaptive edge-preserving lifting scheme - intersection of confidence intervals-edge preserving lifting scheme (ICI-EPL). By incorporating the statistical method of intersection of confidence intervals rule into the lifting scheme, the authors are able to select the most appropriate wavelet on a point-by-point basis. The resulting transform adapts very well to local signal properties and significantly improves denoising performance. Simulations on various signal classes show that the ICI-EPL in most cases easily outperforms other considered transforms, with the greatest improvement being about edges in a signal. Achieved results bring confidence that the ICI-EPL can be used to improve performance in a variety of denoising applications.

The lifting scheme is well known to be an efficient tool for constructing second generation wavelets and is often used to design a class of bi-orthogonal wavelet filter banks. This paper presents the implementation of a class of Infinite Impulse Response (IIR) orthogonal wavelet filter banks by using the adaptive lifting scheme with two lifting steps. A class of II R orthogonal wavelet filter banks can be realized by using all pass filters in the lifting steps. The adaptive lifting scheme is based on the statistical characteristics of the image, the order of the prediction and updation filters are chosen by considering the statistical energy of the image. The designed IIR orthogonal wavelet filter banks have approximately linear phase responses. Finally, the proposed IIR orthogonal wavelet filter banks are applied to the image compression and the coding performance of the proposed IIR filter banks are analysed.

The real advantage of using a wavelet transform for image data compression is the power of adapting to local statistics of the pixels. In hyperspectral data, many but not all spectral planes are well correlated. In each spectral plane, the spatial data is composed of patches of relatively smooth areas segmented by edges. The smooth areas can be well compressed by a relatively long wavelet transform with a large number of vanishing moments. However, for the regions around edges, shorter wavelet transforms are preferable. Despite the fact that the local statistics of both the spectral and spatial data change from pixel to pixel, almost all known image data compression algorithms use only one wavelet transform for the entire dataset. For example, the current international still image data compression standard, JPEG2000, has adopted the 5/3 wavelet transform as the default standard for lossless image data compression for all images. There is not a single wavelet filter that performs uniformly better than the others. Thus, it would be beneficial to use many types of wavelet filters based on local activities of the image. The selected wavelet transform can thus be best adapted to the content of the image locally. In this paper, we have derived a fast adaptive lifting scheme that can easily switch wavelet filters from one to the other. The adaptation is performed on a pixel-by-pixel basis, and it does not need any bookkeeping overhead. It is known that the lifting scheme is a fast and powerful tool to implement all wavelet transforms. Especially for integer-to-integer wavelet transforms, the lifting scheme is an indispensable tool for lossless image compression. Taking advantage of our newly developed lossless lifting scheme, the fast adaptive lifting algorithm presented in this paper not only saves two lifting steps but also improves accuracy compared to the conventional lifting scheme for lossless data compression. Moreover, our simulation results for ten two-dimensional images have shown that the fast adaptive lifting scheme outperforms both of the lossless wavelet tranforms used in JPEG2000 and the S+P transform in lossless SPIHT algorithm.

The lifting scheme is an effective method that provides flexible solutions for designing new perfect reconstruction filter bands. However, most of the existing applications of lifting are based upon stationary assumptions. As such, existing schemes tend to fit the data with a single, pre- determined model. These methods do not exploit the full flexibility provided by lifting. By exploiting the temporal interpretation of lifting, we incorporate adaptive filtering with the lifting scheme to cope with signals whose characteristics vary with time. In this paper, we study the proposed adaptive lifting scheme and its ability to decorrelate subbands. The decorrelation behavior is related proposed adaptive lifting scheme and its ability to decorrelate subbands. The decorrelation behavior is related to the coherence between the subbands, and simulations indicate improved decorrelation when compared with deterministic lifting. Our adaptive filterbank may be used in a thresholding scheme that can yield improved noise reduction capabilities compared to conventional wavelet thresholding schemes. We present a condition under which the proposed adaptive lifting denoising scheme can outperform a similar wavelet thresholding. Simulations are presented that indicate there is an SNR value at which the performance of adaptive lifting denoising surpasses wavelet denoising.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

This paper presents a novel algorithm for matching image interest points. Potential interest points are identified by searching for local peaks in Difference-of-Gaussian (DoG) images. We refine and assign rotation, scale and location for each keypoint by using the SIFT algorithm . Pseudo log-polar sampling grid is then applied to properly scaled image patches around each keypoint, and a weighted adaptive lifting scheme transform is designed for each ring of the log-polar grid. The designed adaptive transform for a ring in the reference keypoint and the general non-adaptive transform are applied to the corresponding ring in a test keypoint. Similarity measure is calculated by comparing the corresponding transform domain coefficients of the adaptive and non-adaptive transforms. We refer to the proposed versatile system of Rotation And Scale Invariant Matching as RASIM. Our experiments show that the accuracy of RASIM is more than SIFT, which is the most widely used interest point matching algorithm in the literature. RASIM is also more robust to image deformations while its computation time is comparable to SIFT.

Wavelet transform has been widely used for vibration-based machine fault diagnosis. However, it is a difficult task to choose or design appropriate wavelet or wavelets for a given application. In this paper, a new signal-adapted lifting scheme for rotating machinery fault diagnosis is proposed, which allows us to construct a wavelet directly from the statistics of a given signal. The prediction operator based on genetic algorithms is designed to maximize the kurtosis of detail signal produced by the lifting scheme, and the update operator is designed to minimize a reconstruction error. The signal-adapted lifting scheme is applied to analyze bearing and gearbox vibration signals. The conventional diagnosis techniques and non-adaptive lifting scheme are also used to analyze the same signals for comparison. The results demonstrate that the signal-adapted lifting scheme is more effective in extracting inherent fault features from complex vibration signals.

We propose to use adaptive wavelet lifting for image retrieval systems that are based on shape detection and multiresolution structures of objects in a database against a background of texture. To measure the performance of our approach, feature vectors are computed based on moment invariants of detail coefficients produced by the adaptive lifting scheme and retrieval rates are obtained by measuring distances between these vectors. Retrieval rates are compared with the rates obtained when using non-adaptive wavelet filtering as a preprocessing step. A synthetic database is created for this simulation.

In the high bit-rate data compression, the filterbanks that match the statistical properties of inputs have been widely researched. For orthonormal uniform filterbanks and biorthogonal filterbanks without order constraints, the design problems have been solved on the whole. However, for biorthogonal FIR filterbanks, an efficient method associated with an algorithm for the design still needs to be developed. In this paper, two-channel adaptive biorthogonal filterbanks via lifting scheme are investigated. Starting from an arbitrary filterbank (orthonormal or biorthogonal), two-channel biorthogonal filterbanks are designed by the adaptive lifting scheme, which is a general form of our previous work. Two adaptive lifting algorithms are developed, namely, the sequential adaptive lifting and the joint adaptive lifting. Further, it is proved that starting from a linear-phase filterbank, the optimal filterbank by the sequential adaptive lifting preserves the linear-phase property. Experimental results show that the subband coding gains can be markedly improved when an appropriate initial filterbank is chosen.

In this paper, we study the problem of image denoising by using an adaptive lifting scheme. Such a scheme can adapt itself well to the analyzed signal, which allows to keep important information for denoising applications. However, it results in a non-isometric transform which can be an important limitation as most of the denoising approaches rely on the estimation of the noise energy in the subbands. In a previous work, it has been shown how to evaluate the subband energies of an uncorrelated signal, in the wavelet domain when using such an adaptive scheme. Based on this previous work, we propose in this paper an estimation of the noise energies in the subband and use it to perform image denoising. Experimental results illustrate that this approach is more effective, in image denoising, than the classical non adaptive lifting schemes both considering perceptual and non perceptual image quality measures.

Because of the better temporal and frequency properties, the wavelet linear approximation can approximate smooth signals very efficiently with the constant size filter. However, standard wavelet linear approximation techniques cannot achieve similar results for signal which are not smooth. Based on the adaptive lifting scheme with perfect reconstruction presented by G. Piella and the author’s previous research, this paper proposed the improved double adaptive wavelet transform which can achieve the better linear approximation for the piecewise continuous signals.

De-noising by the traditional wavelet transform, the result is affected by the choosing of wavelet base. Because the wavelet base is fixed in the traditional wavelet transform, either the smoothness or singularity of the signal can’t be fitted quite well. To overcome the limitation, a new self-adaptive lifting scheme based on modulus maximum analysis is presented. Modulus maximum sequence of the large scale wavelet coefficients can locate the point of the signal with big singularity precisely. According to the position of the point with big singularity, proper neighborhood is fixed, and prediction operator can be chosen self-adaptively. In this way, the prediction operator is fitted to the local feature of the signal. The simulation and engineering application showed that the proposed method could overcome the de-noising disadvantage of traditional wavelet transform. It not only can filter noise from original signal effectively but also can hold local characteristics of original signal in the de-noised signals?

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 predictor's error variance. The optimized coefficients depend on the autocorrelation structure of the image. The presented lifting scheme adapts not only to every component of the color image, but also to its horizontal and vertical directions. We implement this lifting scheme on the fixed-point TMS320C6416 DSK evaluation board. We obtain experimental results using different types of images, as well as using images captured by camera in a real-time application. These results show that the presented method is competitive to few well-known methods for lossless image compression.

In this paper, we propose a new adaptive lifting scheme that not only locally adapts the filtering directions to the orientations of image features, but also adapts the lifting filters to the statistic properties of image signal. The proposed approach refines previous adaptive directional lifting-based wavelet transform (ADL) by combining directional lifting and adaptive lifting filters to form a unified framework. The image signal is first segmented into regions of textures and edges with close directional features. The lifting filters are then effectively designed. The prediction step is designed to minimize the prediction error of the image signal, and the update step is designed to minimize the reconstruction error. Significant improvements on objective and subjective quality over conventional 2-D wavelet transform and previous ADL transform are achieved.

Multiwavelets and the lifting scheme are two important developments of wavelet theory. Multiwavelets outperform scalar wavelets in many applications due to their better properties. The lifting scheme is a method to construct a new wavelet with prescribed properties. In this paper, multiwavelets are integrated with the lifting scheme, synthesizing their advantages. Due to multiple wavelet bases, the lifting scheme of multiwavelets is more flexible than that of scalar wavelets. With supplement of a symmetric condition, a novel adaptive symmetric lifting scheme of multiwavelets is presented. Kurtosis is chosen to be the performance measurement of lifting coefficients, and the genetic algorithm is used to optimize the free parameters in the lifting scheme. The proposed method, constructing a new multiwavelet via an adaptive lifting scheme, is applied to analyze the simulation of a rolling bearing and gearbox vibration signals. The results demonstrate that the adaptive symmetric lifting of multiwavelets is more effective in extracting fault features of rotating machinery than conventional diagnosis techniques with scalar wavelets and non-adaptive multiwavelets.