SJTU CS386 Digital Image Processing

Inter-Subject Correlations (ISC) technique for analyzing neural data (EEG and MEG)

ISC method for M/EEG data

Implementation of Popular Digital Image Processing Filtering Operations

Image Processing Algorithms Implemented in Matlab

Code I wrote for the imgproc course at school

Video processing exercises in C using FFmpeg library 📁

Image restoration in spatial domain

This repository contains experimentation on selectivity estimation when dealing with spatial filters using optimizer feedback. Explore our experiments and methodologies in selectivity estimation for spatial filters.

Sharpening Spatial filtering using Laplacian Filter

Simulates EEG data representing sensorimotor rhythms. Develops and compares spatial filters for neural source reconstruction.

Demonstration of both spatial and frequency domain DoG filters work the same

Matlab Codes for Image Manipulation

Development of a MATLAB-based Toolbox (Graphical User Interfaces) for Elastogram Image and RF Ultrasound Signal Processing

My solutions (and my partner) for the practical work of the Image Processing (Traitement d'image TI) module in my 3rd year of state engineering studies at ENSTTIC

Harris Corner Detection and Spatial Filtering algorithm in C language using optimization techniques based on hardware organization.

Matlab R2019b program using mean and Gaussian filtering for images with Gaussian, uniform multiplicative and impulsive (salt and pepper) noise, plus a connectivity mask 4 is proposed to compare with the filters.

Framework for spatial selectivity estimation using machine learning and optimizer feedback. Addresses both RCC filters and distance-based filters by transforming estimation into regression task. Compares neural networks, tree-based models and instance-based approaches against traditional RTree and histogram methods across 14 spatial datasets.

Interactive Colab dashboard for spatial filtering in image processing. Implements smoothing filters (Mean, Median, Mode) and sharpening filters (Sobel, Laplacian, Sobel+Laplacian). Demonstrates noise reduction, edge detection, and detail enhancement with visual comparisons for real-world computer vision tasks.