Lossless image compression by LMS adaptive filter banks

Cataloged from PDF version of article.A lossless image compression algorithm based on adaptive subband decomposition is proposed. The subband decomposition is achieved by a two-channel LMS adaptive filter bank. The resulting coefficients are lossy coded first, and then the residual error between the lossy and error-free coefficients is compressed. The locations and the magnitudes of the nonzero coefficients are encoded separately by an hierarchical enumerative coding method. The locations of the nonzero coefficients in children bands are predicted from those in the parent band. The proposed compression algorithm, on the average, provides higher compression ratios than the state-of-the-art methods. (C) 2001 Elsevier Science B.V. All rights reserved

Lossless image compression by LMS adaptive filter banks

A lossless image compression algorithm based on adaptive subband decomposition is proposed. The subband decomposition is achieved by a two-channel LMS adaptive filter bank. The resulting coefficients are lossy coded first, and then the residual error between the lossy and error-free coefficients is compressed. The locations and the magnitudes of the nonzero coefficients are encoded separately by an hierarchical enumerative coding method. The locations of the nonzero coefficients in children bands are predicted from those in the parent band. The proposed compression algorithm, on the average, provides higher compression ratios than the state-of-the-art methods

CNN-Based Real-Time Parameter Tuning for Optimizing Denoising Filter Performance

We propose a novel direction to improve the denoising quality of filtering-based denoising algorithms in real time by predicting the best filter parameter value using a Convolutional Neural Network (CNN). We take the use case of BM3D, the state-of-the-art filtering-based denoising algorithm, to demonstrate and validate our approach. We propose and train a simple, shallow CNN to predict in real time, the optimum filter parameter value, given the input noisy image. Each training example consists of a noisy input image (training data) and the filter parameter value that produces the best output (training label). Both qualitative and quantitative results using the widely used PSNR and SSIM metrics on the popular BSD68 dataset show that the CNN-guided BM3D outperforms the original, unguided BM3D across different noise levels. Thus, our proposed method is a CNN-based improvement on the original BM3D which uses a fixed, default parameter value for all images.Comment: 2019 International Conference on Image Analysis and Recognitio

A Monte Carlo framework for denoising and missing wedge reconstruction in cryo-electron tomography

International audienceWe propose a statistical method to address an important issue in cryo electron to-mography image analysis: reduction of a high amount of noise and artifacts due to the presence of a missing wedge (MW) in the spectral domain. The method takes as an input a 3D tomogram derived from limited-angle tomography, and gives as an output a 3D denoised and artifact compensated tomogram. The artifact compensation is achieved by filling up the MW with meaningful information. The method can be used to enhance visualization or as a pre-processing step for image analysis, including segmentation and classification. Results are presented for both synthetic and experimental data