Papers with Code 아카이브
Image Restoration
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 13
- 평가표
- 3
- 보존된 평가 기록
- 17
Image Restoration is a family of inverse problems for obtaining a high quality image from a corrupted input image. Corruption may occur due to the image-capture process (e.g., noise, lens blur), post-processing (e.g., JPEG compression), or photography in non-ideal conditions (e.g., haze, motion blur).
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/sharonwang1/seg_denoising
Segmentation-Aware Image Denoising without Knowing True Segmentation
https://github.com/shibuiwilliam/DeepLearningDenoise
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/shimo8810/NTIRE2017
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/shinde-Pratik/Image-Restoration
https://github.com/shivamsaboo17/Deep-Restore-PyTorch
https://github.com/shmilyo/Low-Cost-Edge-Sensing-for-High-Quality-Demosaicking
https://github.com/shnhrtkyk/satellite-super-resolution
The 2018 PIRM Challenge on Perceptual Image Super-resolution
https://github.com/siavashbigdeli/DMSP
https://github.com/simoneparisotto/Manuscripts-restoration
https://github.com/sjmoran/difar
https://github.com/sjmoran/neural_curve_layers
https://github.com/skchen1993/SwinIR
https://github.com/sldyns/DnCNN_paddle
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/sldyns/MIRNet_paddle
Learning Enriched Features for Real Image Restoration and Enhancement
https://github.com/sobieskibj/rcsb
https://github.com/sotiraslab/AgileFormer
https://github.com/stephen0808/dnlut
Restormer: Efficient Transformer for High-Resolution Image Restoration
https://github.com/subeeshvasu/2018_subeesh_epsr_eccvw
The 2018 PIRM Challenge on Perceptual Image Super-resolution
https://github.com/sunilbelde/Imagedenoising-dncnn-keras
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/sunilbelde/Imagedenoising-dncnn-ridnet-keras
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/susunghong/seg-sdxl
Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidance
https://github.com/sweetcocoa/IKC
https://github.com/taowangzj/llformer
https://github.com/teakkkz/imageSR
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/tgieruc/Noise2Noise_PyTorch
https://github.com/theotziol/Depth-images-Denoising
https://github.com/thstkdgus35/EDSR-PyTorch
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/tinnunculus/Denoising_SR
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/titu1994/Image-Super-Resolution
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/tnwei/waternet
An Underwater Image Enhancement Benchmark Dataset and Beyond
https://github.com/tuvovan/MIRNet---Keras
Learning Enriched Features for Real Image Restoration and Enhancement
https://github.com/txyugood/Restormer_Paddle
Restormer: Efficient Transformer for High-Resolution Image Restoration
https://github.com/ucaswangls/stformer
https://github.com/ugrulas/DivNoising
Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders
https://github.com/utsav-akhaury/SUNet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.