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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/ree1s/idm

공식 코드

Implicit Diffusion Models for Continuous Super-Resolution

https://github.com/reinhardh/supplement_deep_decoder

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Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks

https://github.com/rem105-210/difffashion

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DiffFashion: Reference-based Fashion Design with Structure-aware Transfer by Diffusion Models

https://github.com/renanrojasg/adv_robust_autoencoder

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Inverting Adversarially Robust Networks for Image Synthesis

https://github.com/rengotj/cobra_denoising

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Non-linear aggregation of filters to improve image denoising

https://github.com/renhaa/semantic-diffusion

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Discovering Interpretable Directions in the Semantic Latent Space of Diffusion Models

https://github.com/renyu2016/Generalized-3D-Manipulation

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Learning Generalizable 3D Manipulation With 10 Demonstrations

https://github.com/researchmm/mm-diffusion

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MM-Diffusion: Learning Multi-Modal Diffusion Models for Joint Audio and Video Generation

https://github.com/retinal-research/redot

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OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing

https://github.com/revelio-diffusion/revelio

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$\textit{Revelio}$: Interpreting and leveraging semantic information in diffusion models

https://github.com/reyhanehne/cvf-sid_pytorch

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CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise from Image

https://github.com/reysam93/Graph_Deep_Decoder

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An Underparametrized Deep Decoder Architecture for Graph Signals

https://github.com/reysam93/graph_denoising

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Robust graph-filter identification with graph denoising regularization

https://github.com/rf5/transfusion-asr

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TransFusion: Transcribing Speech with Multinomial Diffusion

https://github.com/rfuruta/pixelRL

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PixelRL: Fully Convolutional Network with Reinforcement Learning for Image Processing

https://github.com/rgsl888/U-Finger-A-Fingerprint-Denosing-Network

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U-Finger: Multi-Scale Dilated Convolutional Network for Fingerprint Image Denoising and Inpainting

https://github.com/rhgao/on-demand-learning

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On-Demand Learning for Deep Image Restoration

https://github.com/ricedsp/D-AMP_Toolbox

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Learned D-AMP: Principled Neural Network based Compressive Image Recovery

https://github.com/ricedsp/prDeep

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prDeep: Robust Phase Retrieval with a Flexible Deep Network

https://github.com/rktamplayo/DenoiseSum

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Unsupervised Opinion Summarization with Noising and Denoising

https://github.com/rob-platt/n2n4m

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Noise2Noise Denoising of CRISM Hyperspectral Data

https://github.com/robert-graf/mago-sp

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MAGO-SP: Detection and Correction of Water-Fat Swaps in Magnitude-Only VIBE MRI

https://github.com/robert-graf/readable-conditional-denoising-diffusion

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Denoising diffusion-based MRI to CT image translation enables automated spinal segmentation

https://github.com/robinwangsd/diffusion-with-multi-modal-priors

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Controllable Motion Generation via Diffusion Modal Coupling

https://github.com/roboterax/humanoid-gym

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Advancing Humanoid Locomotion: Mastering Challenging Terrains with Denoising World Model Learning

https://github.com/rockeycoss/spo

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Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization

https://github.com/ronjonxu/naid

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NIR-Assisted Image Denoising: A Selective Fusion Approach and A Real-World Benchmark Dataset

https://github.com/rosedwayane/aieeg

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IC-U-Net: A U-Net-based Denoising Autoencoder Using Mixtures of Independent Components for Automatic EEG Artifact Removal

https://github.com/rotot0/tab-ddpm

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TabDDPM: Modelling Tabular Data with Diffusion Models

https://github.com/royerlab/ssi-code

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Image Deconvolution via Noise-Tolerant Self-Supervised Inversion

https://github.com/rshaojimmy/ECCV2020-OSAD

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Open-set Adversarial Defense

https://github.com/rskarkare/Robust-Hadamard-Autoencoders

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Blind Image Denoising and Inpainting Using Robust Hadamard Autoencoders

https://github.com/ru-sit/context-is-king

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Plug-and-Play AMC: Context Is King in Training-Free, Open-Set Modulation with LLMs

https://github.com/ruanyuhui/adn-qsdl

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QS-ADN: Quasi-Supervised Artifact Disentanglement Network for Low-Dose CT Image Denoising by Local Similarity Among Unpaired Data

https://github.com/rubenohana/gibbs-diffusion

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Listening to the Noise: Blind Denoising with Gibbs Diffusion

https://github.com/ruizhecao96/cmgan

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CMGAN: Conformer-Based Metric-GAN for Monaural Speech Enhancement

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