Papers with Code 아카이브
Denoising
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Denoising is a task in image processing and computer vision that aims to remove or reduce noise from an image. Noise can be introduced into an image due to various reasons, such as camera sensor limitations, lighting conditions, and compression artifacts. The goal of denoising is to recover the original image, which is considered to be noise-free, from a noisy observation.
<span style="color:grey; opacity: 0.6">( Image credit: Beyond a Gaussian Denoiser )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/yifanfanfanfan/reverse-engineering-of-imperceptible-adversarial-image-perturbations
공식 코드Reverse Engineering of Imperceptible Adversarial Image Perturbations
https://github.com/yifita/DSS
공식 코드Differentiable Surface Splatting for Point-based Geometry Processing
https://github.com/yikai-wang/semani
공식 코드https://github.com/yilinliu97/fasterdip-devil-in-upsampling
공식 코드https://github.com/yimutianyang/kdd24-gbsr
공식 코드https://github.com/yimutianyang/sigir2025-sgil
공식 코드Invariance Matters: Empowering Social Recommendation via Graph Invariant Learning
https://github.com/ying-fu/brve
공식 코드https://github.com/yingqinghe/lvdm
공식 코드Latent Video Diffusion Models for High-Fidelity Long Video Generation
https://github.com/yisuitt/mobius
공식 코드Mobius: Text to Seamless Looping Video Generation via Latent Shift
https://github.com/yixianqianzy/dph
공식 코드https://github.com/yiyanxu/diffrec
공식 코드https://github.com/yk7333/d3po
공식 코드Using Human Feedback to Fine-tune Diffusion Models without Any Reward Model
https://github.com/ykiiiiii/grade_if
공식 코드https://github.com/yluo42/fra-rir
공식 코드FRA-RIR: Fast Random Approximation of the Image-source Method
https://github.com/ylwhxht/l4dr
공식 코드L4DR: LiDAR-4DRadar Fusion for Weather-Robust 3D Object Detection
https://github.com/ylwhxht/v2x-r
공식 코드V2X-R: Cooperative LiDAR-4D Radar Fusion for 3D Object Detection with Denoising Diffusion
https://github.com/ymxbj/TexGaussian
공식 코드TexGaussian: Generating High-quality PBR Material via Octree-based 3D Gaussian Splatting
https://github.com/yongliuy/patchscaler
공식 코드PatchScaler: An Efficient Patch-Independent Diffusion Model for Image Super-Resolution
https://github.com/yongsongh/aidsrgan-miccai2022
공식 코드Rethinking Degradation: Radiograph Super-Resolution via AID-SRGAN
https://github.com/yongyizang/trainingfreemultistepasr
공식 코드https://github.com/yorgoon/diffe
공식 코드Diff-E: Diffusion-based Learning for Decoding Imagined Speech EEG
https://github.com/youngjoohan/ss-bsn
공식 코드SS-BSN: Attentive Blind-Spot Network for Self-Supervised Denoising with Nonlocal Self-Similarity
https://github.com/youngtimmy/ugd
공식 코드You Can't Ignore Either: Unifying Structure and Feature Denoising for Robust Graph Learning
https://github.com/youshanzhang/birdsoundsdenoising
공식 코드BirdSoundsDenoising: Deep Visual Audio Denoising for Bird Sounds
https://github.com/youwyu/Adaptive-Diffusion-Terrain
공식 코드Adaptive Diffusion Terrain Generator for Autonomous Uneven Terrain Navigation
https://github.com/youzunzhi/nim-advdef
공식 코드Beyond Pretrained Features: Noisy Image Modeling Provides Adversarial Defense
https://github.com/yqx7150/HFDAEP
공식 코드Learning Priors in High-frequency Domain for Inverse Imaging Reconstruction
https://github.com/yqx7150/HGGDP
공식 코드Homotopic Gradients of Generative Density Priors for MR Image Reconstruction
https://github.com/yqx7150/IFR-Net-Code
공식 코드IFR-Net: Iterative Feature Refinement Network for Compressed Sensing MRI
https://github.com/yqx7150/WACM
공식 코드Wavelet Transform-assisted Adaptive Generative Modeling for Colorization
https://github.com/yqx7150/WDAEPRec
공식 코드Denoising Auto-encoding Priors in Undecimated Wavelet Domain for MR Image Reconstruction
https://github.com/yqx7150/hgm
공식 코드High-dimensional Assisted Generative Model for Color Image Restoration
https://github.com/ysy31415/unipaint
공식 코드Uni-paint: A Unified Framework for Multimodal Image Inpainting with Pretrained Diffusion Model
https://github.com/ytg7146/DU_CNN
공식 코드https://github.com/yu-li/AGLLNet
공식 코드Attention Guided Low-light Image Enhancement with a Large Scale Low-light Simulation Dataset
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.