Papers with Code 아카이브
Denoising
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 6
- 평가표
- 6
- 보존된 평가 기록
- 15
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/mpaepper/stablediffusion_magicmix
https://github.com/mq0829/DL-CACTI
FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising
https://github.com/mrfanuel/denoising-modulo-samples-k-NN-regression
Denoising modulo samples: k-NN regression and tightness of SDP relaxation
https://github.com/msaddler/auditory-model-denoising
https://github.com/mszpc/denoising-diffusion-mindspore
https://github.com/murufeng/FUIR
https://github.com/mv-lab/swin2sr
https://github.com/nahmiasd/DeepSign
DeepSign: Deep Learning for Automatic Malware Signature Generation and Classification
https://github.com/naserdamer/mordiff
Diffusion Autoencoders: Toward a Meaningful and Decodable Representation
https://github.com/neeraj3029/Ne2Ne-Image-Denoising
Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images
https://github.com/neerakara/TTA_prostate
Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation
https://github.com/neillu23/DiffuSE
https://github.com/neillu23/cdiffuse
https://github.com/nekitmm/starnet
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/neonbjb/tortoise-tts
https://github.com/nickgkan/3d_diffuser_actor
3D Diffuser Actor: Policy Diffusion with 3D Scene Representations
https://github.com/nickolor/Learning-to-See-in-the-Dark
https://github.com/nickolor/Learning-to-See-in-the-Dark_and_DeepISP
DeepISP: Towards Learning an End-to-End Image Processing Pipeline
https://github.com/nlmatics/llmsherpa
https://github.com/nqian1/survey-on-odisr-and-odvsr
RealOSR: Latent Unfolding Boosting Diffusion-based Real-world Omnidirectional Image Super-Resolution
https://github.com/nunof98/AiVision_Anomaly_Detection
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/nybupt/athena
ATHENA: A Framework based on Diverse Weak Defenses for Building Adversarial Defense
https://github.com/nyrahealth/crisperwhisper
WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing
https://github.com/ofsoundof/Heterogeneity_Hypothesis
https://github.com/oktaykarakus/CauchySAR-v1.0
Convergence Guarantees for Non-Convex Optimisation with Cauchy-Based Penalties
https://github.com/oktaykarakus/QuantLUS-CPS-v1.0
Convergence Guarantees for Non-Convex Optimisation with Cauchy-Based Penalties
https://github.com/olaviinha/NeuralImageSuperResolution
https://github.com/olawod/freevc
WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing
https://github.com/omer11a/bounded-attention
Be Yourself: Bounded Attention for Multi-Subject Text-to-Image Generation
https://github.com/omerb01/puq
https://github.com/open-mmlab/mmgeneration
https://github.com/open-mmlab/mmsegmentation/tree/main/configs/vpd
Unleashing Text-to-Image Diffusion Models for Visual Perception
https://github.com/opendenoising/opendenoising-benchmark
OpenDenoising: an Extensible Benchmark for Building Comparative Studies of Image Denoisers
https://github.com/opendilab/DI-engine
https://github.com/optml-group/red-adv
Reverse Engineering of Imperceptible Adversarial Image Perturbations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.