This can be used to visualize the channel corresponding to a specific category in the feature map.

Remove noise from an image with pytorch autograd engine. Loss function defined below uses L1 norm then uses L2 norm..

A two-stream-network based steganalysis network: TSNet

RepVGG: Making VGG-style ConvNets Great Again

⏰ Collaboratively track deadlines of conferences recommended by CCF (Website, Python Cli, Wechat Applet) / If you find it useful, please star this project, thanks~

Take image in which only small fraction of the pixels are known and reconstruct/upsample the full image using fully convolutional neural nets and valve filters (Tensorflow implementation).

Sparse convolution in python

Submanifold sparse convolutional networks

A PyTorch implementation of " EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks."

Vision Transformer (ViT) in PyTorch

Feature Extraction is an integral step for Image Processing jobs. This repository contains the python codes for Traditional Feature Extraction Methods from an image dataset, namely Gabor, Haralick,…

Official SRFlow training code: Super-Resolution using Normalizing Flow in PyTorch

ReLoc: A Restoration-Assisted Framework for Robust Image Tampering Localization

Automatic Virtual Data Augmentation for Deep Image Steganalysis

Fast SwT-based Deep Steganalysis Network for Arbitrary-sized Images

Implementation of several Steganography and steganalysis techniques.

A variation on the Yedroudj-Net network for deep learning based steganalysis.

code for MSFNet: Multi-stream Fusion Network with Generalized Smooth L1 Loss for Single Image Dehazing

Learning a Single Model With a Wide Range of Quality Factors for JPEG Image Artifacts Removal (TIP 2020)

The edge-preserving JPEG compression artifact removal algorithms implementation. The algorithm is designed to remove the artifacts from H&E stained medical images.

Reference-Based JPEG Compression Artifacts Removal

BlockCNN: A Deep Network for Artifact Removal and Image Compression