Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017)
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Updated
Oct 9, 2021 - MATLAB
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017)
A Deep Learning library for EEG Tasks (Signals) Classification, based on TensorFlow.
A tensorflow implement of the paper "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising"
Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition (https://arxiv.org/pdf/2006.11538.pdf)
Improved Residual Networks (https://arxiv.org/pdf/2004.04989.pdf)
Winner solution of mobile AI (CVPRW 2021).
Image denoising using deep CNN with batch renormalization(Neural Networks,2020)
Official Implementation of ResViT: Residual Vision Transformers for Multi-modal Medical Image Synthesis
Code repo for "Deep Generative Adversarial Residual Convolutional Networks for Real-World Super-Resolution" (CVPRW NTIRE2020).
LIDIA: Lightweight Learned Image Denoising with Instance Adaptation (NTIRE, 2020)
Source code of "Deep Pyramidal Residual Networks for Spectral–Spatial Hyperspectral Image Classification"
The Pytorch implementation for "Learning to Forecast and Refine Residual Motion for Image-to-Video Generation" (ECCV 2018).
DeepDTI Tutorial
Enhanced CNN for image denoising (CAAI Transactions on Intelligence Technology, 2019)
[ICCV W] Contextual Convolutional Neural Networks (https://arxiv.org/pdf/2108.07387.pdf)
Implementation of GoogLeNet series Algorithm
Classification between normal and pneumonia affected chest-X-ray images using deep residual learning along with separable convolutional network(CNN). This methodology involves efficient edge preservation and image contrast enhancement techniques for better classification of the X-ray images.
Code of MICRON, MIMIC data processing, Residual Learning
tensorflow implementation of dr2net
IDC prediction in breast cancer histopathology images using deep residual learning with an accuracy of 99.37% in a subset of images containing a total of 7,500 microscopic images.
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