Pytorch Implementation of "Deep Iterative Down-Up CNN for Image Denoising".

Deep CNN for learning image restoration without clean data!

This is Matlab implementation of modulation and demodulation of QPSK signals with added white Gaussian noise

Official implementation for "Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images" https://arxiv.org/abs/2112.08810

LDPC MATLAB simulation using BPSK + AWGN modulation decoded using Sum Product and Min Sum Algorithm

Non Local Means (NLM) python implementation.

This repository is related to all about Computer Vision - an A-Z guide to the world of Computer Vision. This supplement contains the implementation of algorithms, statistical methods, and techniques (in Python)

Digital Image Processing filters developed by python using ipywidgets.

Video Denoising using Low Rank Matrix completion

In this project, low-pass filters and Kalman filters with different window function designs are used to denoise speech signals polluted in the full frequency band of Gaussian white noise

Program for Harris Corner Detection with non-maximum Suppression, HOG Feature Extraction, Feature Comparison, Gaussian Noise and Smoothing.

Learning-to-Augment Strategy Using Noisy and Denoised Data: An Algorithm to Improve Generalization of Deep CNN

Signal and image denoising using quantum adaptive transformation.

Add Gaussian noise to point cloud

Denoising by Quantum Interactive Patches

National Taiwan Normal University 2020 Autumn - 1091 Advanced Image Processing Course Homework.

Using CNN to de noise images.

An Autoencoder Model to Create New Data Using Noisy and Denoised Images Corrupted by the Speckle, Gaussian, Poisson, and impulse Noise.

Develop a simulation platform1 for a BPSK, 4QAM, 8PSK and 16QAM communication system transmitting information over an additive white Gaussian noise (AWGN) channel.

Vocal Tract Segmentation project from the course Neuroengineering @ Politecnico di Milano