We derive a fundamental property of the posterior distribution in Gaussian denoising, and use it to propose a new way for uncertainty visualization, which requires no training or fine-tuning.

This repository contains Python code for various tasks related to medical image analysis and processing.

The detection of peaks and valleys in a 1d-vector or 2d-array (image)

This repository is dedicated to the collection of 10 laboratory reports from the "Scene Segmentation and Interpretation" course, a key component of the Master Degree in Vision and Robotics (VIBOT). Each lab focuses on a specific aspect of scene segmentation and interpretation, employing various techniques from edge detection to image restoration.

AI model to denoise images

Remove 8x8-pixel artifacts from JPEGs.

Software to generate 2D/3D/4D analytical phantoms and their Radon transforms (parallel beam) for image processing

Image denoising and smoothing python script

Implementation of the loopy belief propagation for denoising and inpainting images.

This repository is about denoising the images. In this case, MNIST dataset is taken. The model is built with pytorch with autoencoder architecture

[Pillow] Denoising Images retrieved from Confocal Calcium Imaging.

EPLL implementation using pytorch

PyTorch implementation code for the WiT single-image desnow network

Reducing/Removing different types of noises in medical images such as Gaussian noise , Impulse noise and Uniform noise.

Diffusion based method for impulse noise removal using residual feedback

Neural Ocean is a project that addresses the issue of growing underwater waste in oceans and seas. It offers three solutions: YoloV8 Algorithm-based underwater waste detection, a rule-based classifier for aquatic life habitat assessment, and a Machine Learning model for water classification as fit for drinking or irrigation or not fit.

Min-Max Average Pooling Filter for Impulse Noise Removal is an S&P image processing algorithm published in IEEE Signal Processing Letters.

Implementation of Denoising Diffusion Probabilistic Model from Scratch for Image Generation Task in PyTorch

Une série de notebooks qui expliquent en détail comment fonctionnent les modèles de diffusion

Baseline Autoencoder based Denoiser with symmetric skip connections