This repository contains several hyperspectral image analysis algorithms, including unmixing, registration and fusion.

MRI denoising used RicianNet

REDAEP: Robust and Enhanced Denoising Autoencoding Prior for Sparse-View CT Reconstruction

Learning Multi-Denoising Autoencoding Priors for Image Super-Resolution

Denoising Auto-encoding Priors in Undecimated Wavelet Domain for MRI Reconstruction

Enhanced Denoising Auto-Encoding Priors for Reconstruction

High-dimensional Embedding Network Derived Prior for CS-MRI

Convolutional Sparse Coding in Gradient Domain for MRI Reconstruction

VST-Net: Variance-stabilizing Transformation Inspired Network for Poisson Denoising

Field-of-Experts Filters Guided Tensor Completion

Gradient correlation similarity for efficient contrast preserving decolorization

Iterative-scheme Inspired Network for Impulse Noise Removal

Multi-channels and Multi-models based Autoencoding Priors for Grayscale Image Restoration

Log-Euclidean Metrics for Contrast Preserving Decolorization

Deep Frequency-recurrent Priors for Inverse Imaging Reconstruction

Multi-filters guided low-rank tensor coding for image inpainting

Transformed Denoising Autoencoder Prior for Image Restoration

Synthesis-analysis deconvolutional network for compressed sensing

High-dimensional embedding denoising autoencoding prior for color Image restoration

Highly undersampled magnetic resonance image reconstruction using two-level Bregman method with dictionary updating

Semi-parametric decolorization with Laplacian-based perceptual quality metric

SGTD: Structure gradient and texture decorrelating regularization for image decomposition

Distribution-transformed Network for Impulse Noise Removal

predual dictionary learning (PDL) / augmented Lagrangian multi-scale dictionary learning(ALM-DL)

Sparse and dense hybrid representation via subspace modeling for dynamic MRI

Multi-Wavelet guided Deep Mean-Shift Prior for Image Restoration