qunlin-chen Goto Github PK

adaptivereconnet

Implementation of Adaptive ReconNet in Tensorflow.

bcnn

Bayesian Convolutional Neural Networks for Compressed Sensing Restoration

bcs

Bayesian Compressive Sensing and Multi-task Compressive Sensing

bgcs

Bayesian Group-sparse Compressed Sensing Toolbox (FG 2015)

ca_entropy_model

Repository of the paper "Context-adaptive Entropy Model for End-to-end Optimized Image Compression"

cbsd68-dataset

Color BSD68 dataset for image denoising benchmarks

ccsnet

code-of-learn-deep-learning-with-pytorch

This is code of book "Learn Deep Learning with PyTorch"

compressed-sensing

A couple of simple compressed sensing examples, based on examples from the notes of the excellent Computational Methods for Data Analysis course on Coursera taught by Nathan Kutz.

compressed-sensing-1

Compressed sensing recovery methods

compression

Data compression in TensorFlow

compressivesensingdictionarylearning

Compressive Sensing using Sparse Dictionary Learning

cset

CSET (Compressed Sensing Electron Tomography)-toolbox is a three-dimensional TV-based compressed sensing reconstruction toolbox that consists of algebraic iterative algorithms (SART and SIRT) with total variation (TV) based CS. In addition, it integrates a Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) that is an acceleration method to speed up the algorithm convergence.

csnet

Reimplementation of CSNet (Deep network for compressed image sensing, ICME17)

csnet-1

The deep Learning network based on Compressive Sensing

cvpr2019

cvpr2019 papers，极市团队整理

d-amp_toolbox

This package contains the code to run Learned D-AMP, D-AMP, D-VAMP, D-prGAMP, and DnCNN algorithms. It also includes code to train Learned D-AMP, DnCNN, and Deep Image Prior U-net using the SURE loss.

deep-compressed-sensing

Deep Learning/Deep neural network-based Image/Video (Quantized) Compressed/Compressive Sensing (Coding)

distributed-compressive-sensing-a-deep-learning-approach

Distributed Compressive Sensing: A Deep Learning Approach

dncnn

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017)

easy-rl

强化学习中文教程（蘑菇书），在线阅读地址：https://datawhalechina.github.io/easy-rl/

enhanced-3dtv

The code of enhanced 3DTV Regularization and Its Applications on Hyper-spectral Image Denoising and Compressed Sensing

foa_mc

Fast Optimization Algorithm for Matrix Completion

image-compression-and-video-coding

image-compression-benchmarking

Image compression codecs benchmark inspired by Google's "Full Resolution Image Compression with Recurrent Neural Networks"

image-denoising-benchmark

Collection of image denosing tools in an unification Matlab code

image-denoising-state-of-the-art

improved-efficiency-on-adaptive-arithmetic-coding-for-data-compression-using-range--adjusting-scheme

Context-based adaptive arithmetic coding (CAAC) has high coding efficiency and is adopted by the majority of advanced compression algorithms. In this paper, five new techniques are proposed to further improve the performance of CAAC. They make the frequency table (the table used to estimate the probability distribution of data according to the past input) of CAAC converge to the true probability distribution rapidly and hence improve the coding efficiency. Instead of varying only one entry of the frequency table, the proposed range-adjusting scheme adjusts the entries near to the current input value together. With the proposed mutual-learning scheme, the frequency tables of the contexts highly correlated to the current context are also adjusted. The proposed increasingly adjusting step scheme applies a greater adjusting step for recent data. The proposed adaptive initialization scheme uses a proper model to initialize the frequency table. Moreover, a local frequency table is generated according to local information. We perform several simulations on edge-directed predictionbased lossless image compression, coefficient encoding in JPEG, bit plane coding in JPEG 2000, and motion vector residue coding in video compression. All simulations confirm that the proposed techniques can reduce the bit rate and are beneficial for data compression.

ista-net

ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing (Tensorflow Code)

kcs-gsr

Group Sparse Representation for Kronecker Compressive Sensing, Image Process. Image Under. (IPIU) 2016