A deep-learning library for N2V and friends

🔧🧠 MEEGkit: MEG & EEG processing toolkit in Python

ICASSP 2023-2024 Papers: A complete collection of influential and exciting research papers from the ICASSP 2023-24 conferences. Explore the latest advancements in acoustics, speech and signal processing. Code included. Star the repository to support the advancement of audio and signal processing!

Open source Python library for building bioimage analysis pipelines

Processing and visualization tools for quantitative MRI data

DeepVID v2: Self-Supervised Denoising with Decoupled Spatiotemporal Enhancement for Low-Photon Voltage Imaging

DIPY is the paragon 3D/4D+ imaging library in Python. Contains generic methods for spatial normalization, signal processing, machine learning, statistical analysis and visualization of medical images. Additionally, it contains specialized methods for computational anatomy including diffusion, perfusion and structural imaging.

ShabbyPages is a state-of-the-art corpus of born-digital document images with both ground truth and distorted versions appropriate for use in training models to reverse distortions and recover to original denoised documents.

AI model to denoise images

A passion projects that has been going on for awhile, finally at a place where I feel comfortable tentatively uploading it to Github for others

Hybrid GI solution, based on DDGI ( include Ray-Tracing and SDF-Tracing )

Self-supervised deep learning for denoising and missing wedge reconstruction of cryo-ET tomograms

SwinIR: Image Restoration Using Swin Transformer (official repository)

Cluster Analysis of Trimmed Spectrograms: framework for detection and denoising of sparse signals in time-frequency domain.

Simple PyTorch Denoisers for Waveform Audio

Discover, install, and share napari plugins

Main repository gathering all content of the EMBO-DL4MIA 2024 course.

[WACV 2024] INCODE: Implicit Neural Conditioning with Prior Knowledge Embeddings

GPU-accelerated math function graphers in web browsers, both 3D and 2D.

基于数字图像处理和深度学习的图像质量提升 使用PRIDNet 和SRCNN 进行去噪和超分. 用 SpringBoot+Mybatis plus+Vue进行界面和后端设计