Papers with Code 아카이브
Image Denoising
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 21
- 보존된 평가 기록
- 110
Image Denoising is a computer vision task that involves removing noise from an image. Noise can be introduced into an image during acquisition or processing, and can reduce image quality and make it difficult to interpret. Image denoising techniques aim to restore an image to its original quality by reducing or removing the noise, while preserving the important features of the image.
<span style="color:grey; opacity: 0.6">( Image credit: Wide Inference Network for Image Denoising via Learning Pixel-distribution Prior )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/tub-rip/event_based_image_rec_inverse_problem
공식 코드https://github.com/tum-vision/sublabel_relax
공식 코드Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies
https://github.com/txphome/dfpir
공식 코드Degradation-Aware Feature Perturbation for All-in-One Image Restoration
https://github.com/utat-ss/FINCH-destriping
공식 코드https://github.com/va1shn9v/promptir
공식 코드https://github.com/visinf/n3net
공식 코드https://github.com/vlogroup/pogmdm
공식 코드https://github.com/wangzhi-swu/dtnfm
공식 코드A Novel Truncated Norm Regularization Method for Multi-channel Color Image Denoising
https://github.com/wencongwu/tsp-rdanet
공식 코드Two-stage Progressive Residual Dense Attention Network for Image Denoising
https://github.com/wentxul/linr
공식 코드Revisiting Implicit Neural Representations in Low-Level Vision
https://github.com/wlydlut/c2f-dft
공식 코드Learning A Coarse-to-Fine Diffusion Transformer for Image Restoration
https://github.com/woldier/witunet
공식 코드https://github.com/workingcoder/EDCNN
공식 코드EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoising
https://github.com/wuqi-coder/rnsd
공식 코드https://github.com/wyf0912/ExposureDiffusion
공식 코드ExposureDiffusion: Learning to Expose for Low-light Image Enhancement
https://github.com/xianggkl/otlrm
공식 코드OTLRM: Orthogonal Learning-based Low-Rank Metric for Multi-Dimensional Inverse Problems
https://github.com/xiexinheng/node-imgnet
공식 코드NODE-ImgNet: a PDE-informed effective and robust model for image denoising
https://github.com/xiuheng-wang/Plug_and_Play_HSI_unmixing
공식 코드https://github.com/xlearning-scu/2022-neurips-msanet
공식 코드https://github.com/xmed-lab/diffcmr
공식 코드DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic Models
https://github.com/xpixelgroup/diffbir
공식 코드DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior
https://github.com/xpixelgroup/hat
공식 코드https://github.com/xuandongzhao/watermarkattacker
공식 코드Invisible Image Watermarks Are Provably Removable Using Generative AI
https://github.com/xyuan01/self-supervised-noise2noise-for-ldct
공식 코드https://github.com/yaziwel/region-attention-transformer-for-medical-image-restoration
공식 코드https://github.com/yaziwel/restore-rwkv
공식 코드Restore-RWKV: Efficient and Effective Medical Image Restoration with RWKV
https://github.com/ychfan/nsr
공식 코드https://github.com/ychfan/scn
공식 코드https://github.com/ycl92/tmd-ldct
공식 코드Cross-domain Denoising for Low-dose Multi-frame Spiral Computed Tomography
https://github.com/yifanfanfanfan/reverse-engineering-of-imperceptible-adversarial-image-perturbations
공식 코드Reverse Engineering of Imperceptible Adversarial Image Perturbations
https://github.com/yilinliu97/fasterdip-devil-in-upsampling
공식 코드https://github.com/youngjoohan/ss-bsn
공식 코드SS-BSN: Attentive Blind-Spot Network for Self-Supervised Denoising with Nonlocal Self-Similarity
https://github.com/ysy31415/unipaint
공식 코드Uni-paint: A Unified Framework for Multimodal Image Inpainting with Pretrained Diffusion Model
https://github.com/yulunzhang/RDN
공식 코드https://github.com/yulunzhang/RNAN
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.