LuvCjy

LuvCjy's Stars

• bubbliiiing/mask-rcnn-tf2

  这是一个mask-rcnn-tf2的库，可以用于训练自己的模型。

  Language:Python639

• XPixelGroup/BasicSR

  Open Source Image and Video Restoration Toolbox for Super-resolution, Denoise, Deblurring, etc. Currently, it includes EDSR, RCAN, SRResNet, SRGAN, ESRGAN, EDVR, BasicVSR, SwinIR, ECBSR, etc. Also support StyleGAN2, DFDNet.

  Language:Python6.6k1.2k

• murufeng/FUIR

  A Flexible and Unified Image Restoration Framework (PyTorch), including state-of-the-art image restoration model. Such as NAFNet, Restormer, MPRNet, MIMO-UNet, SCUNet, SwinIR, HINet, etc. ⭐⭐⭐⭐⭐⭐

  Language:Python27831

• open-mmlab/mmagic

  OpenMMLab Multimodal Advanced, Generative, and Intelligent Creation Toolbox. Unlock the magic 🪄: Generative-AI (AIGC), easy-to-use APIs, awsome model zoo, diffusion models, for text-to-image generation, image/video restoration/enhancement, etc.

  Language:Jupyter Notebook6.8k1.1k

• hee0624/process_image

  generate noise image 生成噪声图片，用来cv领域

  Language:Python103

• FanChiMao/SUNet

  SUNet: Swin Transformer with UNet for Image Denoising

  Language:Python19222

• scnu/scnuthesis

  符合华南师范大学硕士/博士学位论文格式要求的LaTeX模板。

  Language:TeX21561

• Fan-Treasure/Restormer

  Reproduce of the CVPR 2022 paper "Restormer: Efficient Transformer for High-Resolution Image Restoration"

  Language:Python101

• leftthomas/Restormer

  A PyTorch implementation of Restormer based on CVPR 2022 paper "Restormer: Efficient Transformer for High-Resolution Image Restoration"

  Language:Python7014

• dome272/VQGAN-pytorch

  Pytorch implementation of VQGAN (Taming Transformers for High-Resolution Image Synthesis) (https://arxiv.org/pdf/2012.09841.pdf)

  Language:Python42772

• wyhuai/DDNM

  [ICLR 2023 Oral] Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model

  Language:Python1.1k78

• sashrika15/noise2noise

  Pytorch implementation of Noise2Noise paper.

  Language:Python42

• ZhenyuTan/Noise2Noise-Cryo-EM-image-denoising

  pytorch implementation of noise2noise for Cryo-EM image denoising

  Language:Python4013

• juglab/DivNoising

  DivNoising is an unsupervised denoising method to generate diverse denoised samples for any noisy input image. This repository contains the code to reproduce the results reported in the paper https://openreview.net/pdf?id=agHLCOBM5jP

  Language:Python416

• jeongHwarr/various_FCM_segmentation

  Image segmentation Using Various Fuzzy C-means Algorithms (FCM, EnFCM, MFCM).

  Language:Python5123

• ariffyasri/fuzzy-c-means

  Image Segmentation using Fuzzy C Means

  Language:HTML2361

• SINGROUP/Graph-AFM

  Machine learning molecule graphs from atomic force microscopy images.

  Language:Python121

• Probe-Particle/ppafm

  Classical force field model for simulating atomic force microscopy images.

  Language:Python4918

• bubbliiiing/unet-pytorch

  这是一个unet-pytorch的源码，可以训练自己的模型

  Language:Python1.3k252

• bigmb/Unet-Segmentation-Pytorch-Nest-of-Unets

  Implementation of different kinds of Unet Models for Image Segmentation - Unet , RCNN-Unet, Attention Unet, RCNN-Attention Unet, Nested Unet

  Language:Python1.8k345

• zhixuhao/unet

  unet for image segmentation

  Language:Jupyter Notebook4.5k2k

• heroineyy/cv_homework--2.0

  Language:Python2

• hoichanjung/AFM_Image_Denoising

  Comparative Study of Deep Learning Algorithms for Atomic Force Microscope Image Denoising

  Language:Python31

• CVHub520/X-AnyLabeling

  Effortless data labeling with AI support from Segment Anything and other awesome models.

  Language:Python3.5k398

• alshedivat/keras-gp

  Keras + Gaussian Processes: Learning scalable deep and recurrent kernels.

  Language:Python24555

• yatengLG/ISAT_with_segment_anything

  Labeling tool with SAM(segment anything model),supports SAM, SAM2, sam-hq, MobileSAM EdgeSAM etc.交互式半自动图像标注工具

  Language:Python1.2k128

• TommyZihao/MMSegmentation_Tutorials

  Jupyter notebook tutorials for MMSegmentation

  Language:Jupyter Notebook45985

• TommyZihao/Train_Custom_Dataset

  标注自己的数据集，训练、评估、测试、部署自己的人工智能算法

  Language:Jupyter Notebook3k628

• sumner-harris/Deep-Learning-with-ICCD-Images

  Language:Jupyter Notebook1

• AdityaTheDev/ReconstructionOfImage-Using-DeepAutoEnccoders

  Autoencoder is a type of neural network where the output layer has the same dimensionality as the input layer. In simpler words, the number of output units in the output layer is equal to the number of input units in the input layer. An autoencoder replicates the data from the input to the output in an unsupervised manner and is therefore sometimes referred to as a replicator neural network. The autoencoders reconstruct each dimension of the input by passing it through the network. It may seem trivial to use a neural network for the purpose of replicating the input, but during the replication process, the size of the input is reduced into its smaller representation. The middle layers of the neural network have a fewer number of units as compared to that of input or output layers. Therefore, the middle layers hold the reduced representation of the input. The output is reconstructed from this reduced representation of the input.

  Language:Jupyter Notebook1