Wavelet/shearlet hybridized neural networks for biomedical image restoration

Recently, new programming paradigms have emerged that combine parallelism and numerical computations with algorithmic differentiation. This approach allows for the hybridization of neural network techniques for inverse imaging problems with more traditional methods such as wavelet-based sparsity modelling techniques. The benefits are twofold: on the one hand traditional methods with well-known properties can be integrated in neural networks, either as separate layers or tightly integrated in the network, on the other hand, parameters in traditional methods can be trained end-to-end from datasets in a neural network "fashion" (e.g., using Adagrad or Adam optimizers). In this paper, we explore these hybrid neural networks in the context of shearlet-based regularization for the purpose of biomedical image restoration. Due to the reduced number of parameters, this approach seems a promising strategy especially when dealing with small training data sets

Pipelined implementation of Jpeg image compression using Hdl

This thesis presents the architecture and design of a JPEG compressor for color images using VHDL. The system consists of major parts like color space converter, down sampler, 2-D DCT module, quantization, zigzag scanning and entropy coDing The color space conversion transforms the RGB colors to YCbCr color coDing The down sampling operation reduces the sampling rate of the color information (Cb and Cr). The 2-D DCT transform the pixel data from the spatial domain to the frequency domain. The quantization operation eliminates the high frequency components and the small amplitude coefficients of the co-sine expansion. Finally, the entropy coding uses run-length encoding (RLE), Huffman, variable length coding (VLC) and differential coding to decrease the number of bits used to represent the image. The JPEG compression is a lossy compression, since downsampling and quantization operations are irreversible. But the losses can be controlled in order to keep the necessary image quality; Architectures for these parts were designed and described in VHDL. The results were observed using Active-HDL simulator and the code being synthesized using xilinx ise for vertex-4 FPGA. This pipelined architecture has a minimum latency of 187 clock cycles

Discrete Wavelet Transforms

The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications

Subband Image Coding with Jointly Optimized Quantizers

An iterative design algorithm for the joint design of complexity- and entropy-constrained subband quantizers and associated entropy coders is proposed. Unlike conventional subband design algorithms, the proposed algorithm does not require the use of various bit allocation algorithms. Multistage residual quantizers are employed here because they provide greater control of the complexity-performance tradeoffs, and also because they allow efficient and effective high-order statistical modeling. The resulting subband coder exploits statistical dependencies within subbands, across subbands, and across stages, mainly through complexity-constrained high-order entropy coding. Experimental results demonstrate that the complexity-rate-distortion performance of the new subband coder is exceptional

The 1993 Space and Earth Science Data Compression Workshop

The Earth Observing System Data and Information System (EOSDIS) is described in terms of its data volume, data rate, and data distribution requirements. Opportunities for data compression in EOSDIS are discussed

Image up-sampling using the discrete wavelet transform

Image up-sampling is an effective technique, useful in today\u27s digital image processing applications and rendering devices. In image up-sampling, an image is enhanced from a lower resolution to a higher resolution with the degree of enhancement depending upon application requirements. It is known that the traditional interpolation based approaches for up-sampling, such as the Bilinear or Bicubic interpolations, blur the resultant images along edges and image features. Furthermore, in color imagery, these interpolation-based up-sampling methods may have color infringing artifacts in the areas where the images contain sharp edges and fine textures. We present an interesting up-sampling algorithm based on the Discrete Wavelet Transform (DWT). The proposed method preserves much of the sharp edge features in the image, and lessens the amount of color artifacts. Effectiveness of the proposed algorithm has been demonstrated based on comparison of PSNR and Δ E * ab quality metrics between the original and reconstructed images

Learned Lossless Image Compression Through Interpolation With Low Complexity

With the increasing popularity of deep learning in image processing, many learned lossless image compression methods have been proposed recently. One group of algorithms that have shown good performance are based on learned pixel-based auto-regressive models, however, their sequential nature prevents easily parallelized computations and leads to long decoding times. Another popular group of algorithms are based on scale-based auto-regressive models and can provide competitive compression performance while also enabling simple parallelization and much shorter decoding times. However, their major drawback are the used large neural networks and high computational complexity. This paper presents an interpolation based learned lossless image compression method which falls in the scale-based auto-regressive models group. The method achieves better than or on par compression performance with the recent scale-based auto-regressive models, yet requires more than 10x less neural network parameters and encoding/decoding computation complexity. These achievements are due to the contributions/findings in the overall system and neural network architecture design, such as sharing interpolator neural networks across different scales, using separate neural networks for different parameters of the probability distribution model and performing the processing in the YCoCg-R color space instead of the RGB color space.Comment: 8 pages, 4 figures, 2 table

Fast and Interpretable Nonlocal Neural Networks for Image Denoising via Group-Sparse Convolutional Dictionary Learning

Nonlocal self-similarity within natural images has become an increasingly popular prior in deep-learning models. Despite their successful image restoration performance, such models remain largely uninterpretable due to their black-box construction. Our previous studies have shown that interpretable construction of a fully convolutional denoiser (CDLNet), with performance on par with state-of-the-art black-box counterparts, is achievable by unrolling a dictionary learning algorithm. In this manuscript, we seek an interpretable construction of a convolutional network with a nonlocal self-similarity prior that performs on par with black-box nonlocal models. We show that such an architecture can be effectively achieved by upgrading the $\ell 1$ sparsity prior of CDLNet to a weighted group-sparsity prior. From this formulation, we propose a novel sliding-window nonlocal operation, enabled by sparse array arithmetic. In addition to competitive performance with black-box nonlocal DNNs, we demonstrate the proposed sliding-window sparse attention enables inference speeds greater than an order of magnitude faster than its competitors.Comment: 11 pages, 8 figures, 6 table