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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/inouye-lab/saub

공식 코드

Expressive Score-Based Priors for Distribution Matching with Geometry-Preserving Regularization

https://github.com/inpefess/neural-semigroups

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A Neural Network for Semigroups

https://github.com/inria-thoth/t3sc

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A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration

https://github.com/insitro/insitro-research

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DEL-Dock: Molecular Docking-Enabled Modeling of DNA-Encoded Libraries

https://github.com/intel/rho-diffusion

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$ρ$-Diffusion: A diffusion-based density estimation framework for computational physics

https://github.com/intellabs/cgvqm

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CGVQM+D: Computer Graphics Video Quality Metric and Dataset

https://github.com/intellabs/intelneuromorphicdnschallenge

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The Intel Neuromorphic DNS Challenge

https://github.com/iorism/CNN

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Beyond Deep Residual Learning for Image Restoration: Persistent Homology-Guided Manifold Simplification

https://github.com/ipc-lab/deepjscc-diffusion

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High Perceptual Quality Wireless Image Delivery with Denoising Diffusion Models

https://github.com/iqua/vggbase

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Language-Guided Diffusion Model for Visual Grounding

https://github.com/irmvlab/diff-ip2d

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Diff-IP2D: Diffusion-Based Hand-Object Interaction Prediction on Egocentric Videos

https://github.com/irmvlab/mmtwin

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Novel Diffusion Models for Multimodal 3D Hand Trajectory Prediction

https://github.com/it-hao/ADFNet

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Adaptive Dynamic Filtering Network for Image Denoising

https://github.com/itskalvik/WiFi-user-recognition

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Deep CSI Learning for Gait Biometric Sensing and Recognition

https://github.com/itsmag11/omegance

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Omegance: A Single Parameter for Various Granularities in Diffusion-Based Synthesis

https://github.com/ivanmontero/autobot

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Sentence Bottleneck Autoencoders from Transformer Language Models

https://github.com/ivrl/ccid

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Image Denoising with Control over Deep Network Hallucination

https://github.com/j-fhu/cem

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Interpreting Low-level Vision Models with Causal Effect Maps

https://github.com/jackfrost168/cf_diff

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Collaborative Filtering Based on Diffusion Models: Unveiling the Potential of High-Order Connectivity

https://github.com/jacobdeasy/heavy-tail-dsm

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Heavy-tailed denoising score matching

https://github.com/jaewan7599/rdlae_sigir2023

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It's Enough: Relaxing Diagonal Constraints in Linear Autoencoders for Recommendation

https://github.com/jakubmicorek/MULDE-Multiscale-Log-Density-Estimation-via-Denoising-Score-Matching-for-Video-Anomaly-Detection

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MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection

https://github.com/janani-suresh-97/first-line-defence

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First line of defense: A robust first layer mitigates adversarial attacks

https://github.com/jannerm/diffuser

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Planning with Diffusion for Flexible Behavior Synthesis

https://github.com/jarchv/solverip

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Generative Flows as a General Purpose Solution for Inverse Problems

https://github.com/jarridrb/dem

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Iterated Denoising Energy Matching for Sampling from Boltzmann Densities

https://github.com/jarucezh/cldfd

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Cross-Level Distillation and Feature Denoising for Cross-Domain Few-Shot Classification

https://github.com/jason011212/star-net

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STAR-Net: An Interpretable Model-Aided Network for Remote Sensing Image Denoising

https://github.com/jasonfu1998/diffpad

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DiffPAD: Denoising Diffusion-based Adversarial Patch Decontamination

https://github.com/javiersgjavi/costi

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CoSTI: Consistency Models for (a faster) Spatio-Temporal Imputation

https://github.com/javyduck/diffsmooth

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DiffSmooth: Certifiably Robust Learning via Diffusion Models and Local Smoothing

https://github.com/jbartlett6/accelerated-first-order-method-for-variational-imaging

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Accelerated First Order Methods for Variational Imaging

https://github.com/jbrenig/rescdc

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Higher fidelity perceptual image and video compression with a latent conditioned residual denoising diffusion model

https://github.com/jchenxu/human-motion-prior

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Exploring Versatile Prior for Human Motion via Motion Frequency Guidance

https://github.com/jcy132/DML_CT

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Patch-wise Deep Metric Learning for Unsupervised Low-Dose CT Denoising

https://github.com/jd-ai-research-nlp/bort

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BORT: Back and Denoising Reconstruction for End-to-End Task-Oriented Dialog

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