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Two-channel wavelet filter-banks (FBs) are universally used in many applications of signal processing. In this work, design of a bi-orthogonal wavelet FB with low complexity is suggested. The proposed design is based on canonical signed digit (CSD) implementation of the bi-orthogonal wavelet FB. CSD technique has been applied to the FB to eliminate the power consuming multipliers and to reduce the number of shifters and adders in the implementation of bi-orthogonal FB. The suggested FB design is suitable for the application of high speed signal processing applications. In addition, the proposed bi-orthogonal wavelet FB is implemented on hardware using Field Programmable Gate Array (FPGA) board Kintex-7 from Xilinx. It is found that the suggested bi-orthogonal wavelet FB attained low digital hardware requirement as compared to well-known rationalized existing bi-orthogonal wavelet FBs. The efficacy of the suggested wavelet FB is demonstrated on medical image retrieval application. NEMA, OASIS, and EXACT09, three publicly accessible medical image datasets, are utilized to measure the efficacy of image retrieval. It is shown that the suggested bi-orthogonal wavelet FB performs better than the bi-orthogonal wavelet FBs existing that are being used.

With the increasingly mature medical imaging technology, super-resolution imaging and other systems have been widely applied to pathological medical image and tele-medical image transmission system has also attracted extensive heed and application. While pathological diagnosis is expedited, the transmission and storage of pathological medical image have become a problem demanding prompt solution. Due to its excellent spatial resolution and frequency resolution, wavelet analysis is particularly applicable to the analysis of non-stationary signals. As natural image has this non-stationary characteristic, it can be seen as a linear combination of signals with concentration of energy space (image edges and details) and frequency (the part of image with gradual changes). Because conventional wavelet transform requires rounding operation of wavelet coefficients, it cannot reconstruct the original lossless image. So, this paper introduces bi-orthogonal filter bank into wavelet transform, provides the conditions for accurate reconstruction of bi-orthogonal filter bank suited for wavelet transform as well as the multi-resolution analysis under bi-orthogonal condition and the construction method for bi-orthogonal wavelet, achieves the three-level orthogonal wavelet transform in the Lifting Scheme by means of symmetric and periodic extension, and concentrates the energy into a few wavelet coefficients. With the final experiment method, this paper analyzes the distribution characteristics of image wavelet coefficients, laying a foundation for selecting proper compressed encoding scheme. And the experiment result proves that the image compression conducted via bi-orthogonal wavelet transform in this paper can avoid the rounding error of computer and avails the lossless compression of medical image so as to improve image compression in both the efficiency and the accuracy. [ABSTRACT FROM AUTHOR]

Medical imaging technology has become an important tool to conduct non-invasive detection on human body in medical diagnosis. Therefore, image quality is particularly significant. As wavelet transform has excellent local time-frequency properties as well as features of multi-scale and multi-resolution, it holds an enormous advantage in digital image processing. After wavelet transform, edge detail information of the image is distributed in different decomposition scales and for different decomposition scales, edge information has different features. All traditional image enhancement algorithms based on wavelet obtain detail information by decomposing low-frequency sub-image and there is still plenty of detail information in high-frequency subimages. On this basis, this paper makes improvements to the traditional image decomposition methods; raises the method to get more detail information by further high-frequency sub-image and a new image enhancement algorithm for edge detection based on bi-orthogonal wavelet; and uses this algorithm in medical image enhancement. In another word, it firstly performs n-level wavelet decomposition on the image and gets the approximate low-frequency information and high-frequency information of the image. Then, it adopts different methods to handle the wavelet coefficients obtained, namely it processes the high-frequency wavelet coefficients or all wavelet coefficients with different enhancement algorithms. Finally, it reconstructs the wavelet coefficients processed to obtain the enhanced medical image. [ABSTRACT FROM AUTHOR]

An outline is presented for construction of wavelet filters with compact support. Our approach does not require any extensive simulations for obtaining the values of design variables like other methods. A unified framework is proposed for designing halfband polynomials with varying vanishing moments. Optimum filter pairs can then be generated by factorization of the halfband polynomial. Although these optimum wavelets have characteristics close to that of CDF 9/7 (Cohen-Daubechies-Feauveau), a compact support may not be guaranteed. Subsequently, we show that by proper choice of design parameters finite wordlength wavelet construction can be achieved. These hardware friendly wavelets are analyzed for their possible applications in image compression and feature extraction. Simulation results show that the designed wavelets give better performances as compared to standard wavelets. Moreover, the designed wavelets can be implemented with significantly reduced hardware as compared to the existing wavelets. [ABSTRACT FROM AUTHOR]

Abstract: Synthetic Aperture Radar (SAR) images are inherently affected by multiplicative speckle noise, which is due to the coherent nature of scattering phenomena. This paper proposes a novel filtering method for removing such speckle noise from Synthetic Aperture Radar image, that combines the Stationary Wavelet Transform (SWT) with a mean based smoothing operation, which adapts to variation in both signal and the noise. Experimental results on several test images by using the proposed method show that, the proposed method yields significantly superior image quality and better Peak Signal to Noise Ratio (PSNR). In order to illustrate the effectiveness of the proposed algorithm this method is compared with other wavelet based thresholding techniques and conventional statistical filters. [Copyright &y& Elsevier]