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Denoising

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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/Yqcca/CMLoRA

공식 코드

Cached Multi-Lora Composition for Multi-Concept Image Generation

https://github.com/YuGuangWang/UFG

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How Framelets Enhance Graph Neural Networks

https://github.com/Yuan-Jinghui/Riemannian-Optimization-on-Relaxed-Indicator-Matrix-Manifold

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Riemannian Optimization on Relaxed Indicator Matrix Manifold

https://github.com/Yuanshi9815/OminiControl

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OminiControl2: Efficient Conditioning for Diffusion Transformers

https://github.com/Yushgoel/AdversarialAttack

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An Empirical Review of Adversarial Defenses

https://github.com/ZBWpro/CoT-BERT

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CoT-BERT: Enhancing Unsupervised Sentence Representation through Chain-of-Thought

https://github.com/ZGCTroy/ED-DPM

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Entropy-driven Sampling and Training Scheme for Conditional Diffusion Generation

https://github.com/ZHKKKe/PixelSSL

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Guided Collaborative Training for Pixel-wise Semi-Supervised Learning

https://github.com/ZJU-DAILY/SGDL

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Self-Guided Learning to Denoise for Robust Recommendation

https://github.com/ZhangJC-2k/RandomSampling

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Random Sampling for Diffusion-based Adversarial Purification

https://github.com/Zhangyanbo/diffusion-evolution

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Diffusion Models are Evolutionary Algorithms

https://github.com/ZhaomingKong/Denoising-Comparison

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A Comparison of Image Denoising Methods

https://github.com/ZhaomingKong/color_image_denoising

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A Brief Review of Real-World Color Image Denoising

https://github.com/ZhendongWang6/Uformer

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Uformer: A General U-Shaped Transformer for Image Restoration

https://github.com/ZhonghaoZ/AMP-Net_TIP

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AMP-Net: Denoising based Deep Unfolding for Compressive Image Sensing

https://github.com/ZhuHaoranEIS/DN-TOD

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Robust Tiny Object Detection in Aerial Images amidst Label Noise

https://github.com/Zi-YuanYang/SCAN-PhysFed

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Patient-Level Anatomy Meets Scanning-Level Physics: Personalized Federated Low-Dose CT Denoising Empowered by Large Language Model

https://github.com/ZichengDuan/EZIGen

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EZIGen: Enhancing zero-shot personalized image generation with precise subject encoding and decoupled guidance

https://github.com/ZikangXiong/rl-detect-and-denoise-defense

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Defending Observation Attacks in Deep Reinforcement Learning via Detection and Denoising

https://github.com/ZiyiZhang27/tdpo

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Confronting Reward Overoptimization for Diffusion Models: A Perspective of Inductive and Primacy Biases

https://github.com/ZqlwMatt/Tex4D

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Tex4D: Zero-shot 4D Scene Texturing with Video Diffusion Models

https://github.com/a-fsh-r/IBO

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IBO: Inpainting-Based Occlusion to Enhance Explainable Artificial Intelligence Evaluation in Histopathology

https://github.com/aaberdam/AdaLISTA

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Ada-LISTA: Learned Solvers Adaptive to Varying Models

https://github.com/aaleka/retree

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Denoising Diffusion Probabilistic Model for Retinal Image Generation and Segmentation

https://github.com/aalkaid/usdenoising

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A Complementary Global and Local Knowledge Network for Ultrasound denoising with Fine-grained Refinement

https://github.com/aamir-mustafa/Transformation-CR

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Transformation Consistency Regularization- A Semi-Supervised Paradigm for Image-to-Image Translation

https://github.com/aau-es-ml/ssl_noise-robust_kws

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Noise-Robust Keyword Spotting through Self-supervised Pretraining

https://github.com/abcdef2000/R2RNet

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R2RNet: Low-light Image Enhancement via Real-low to Real-normal Network

https://github.com/abidlabs/contrastive

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Contrastive Principal Component Analysis

https://github.com/aboriginer/hfs-sde

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High-Frequency Space Diffusion Models for Accelerated MRI

https://github.com/absDL/absDL.github.io

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Single-exposure absorption imaging of ultracold atoms using deep learning

https://github.com/abtahimajeed/checkerboarddenoiser

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Contextual Checkerboard Denoise -- A Novel Neural Network-Based Approach for Classification-Aware OCT Image Denoising

https://github.com/acecreamu/deep-hs-prior

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Deep Hyperspectral Prior: Denoising, Inpainting, Super-Resolution

https://github.com/ad12/meddlr

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Noise2Recon: Enabling Joint MRI Reconstruction and Denoising with Semi-Supervised and Self-Supervised Learning

https://github.com/ada-cheng/cat-pruning

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CAT Pruning: Cluster-Aware Token Pruning For Text-to-Image Diffusion Models

https://github.com/adamdad/repfusion

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Diffusion Model as Representation Learner

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.