Papers with Code 아카이브

Denoising

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

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/alessandro-f/dif-fuse

공식 코드

Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images

https://github.com/alexanderfrotscher/andi

공식 코드

Unsupervised Anomaly Detection using Aggregated Normative Diffusion

https://github.com/alexdelitzas/fcdae-neural-signal-denoising

공식 코드

Removing Noise from Extracellular Neural Recordings Using Fully Convolutional Denoising Autoencoders

https://github.com/alexdenker/SteerableConditionalDiffusion

공식 코드

Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction

https://github.com/alexdenker/deft

공식 코드

DEFT: Efficient Fine-Tuning of Diffusion Models by Learning the Generalised $h$-transform

https://github.com/alexduarte23/sred

공식 코드

SelfReDepth: Self-Supervised Real-Time Depth Restoration for Consumer-Grade Sensors

https://github.com/alexgraikos/diffusion_priors

공식 코드

Diffusion models as plug-and-play priors

https://github.com/alexiglad/EBT

공식 코드

Energy-Based Transformers are Scalable Learners and Thinkers

https://github.com/alexzhou907/DDBM

공식 코드

Denoising Diffusion Bridge Models

https://github.com/alge24/ADA-UGNN

공식 코드

A Unified View on Graph Neural Networks as Graph Signal Denoising

https://github.com/algolzw/daclip-uir

공식 코드

Controlling Vision-Language Models for Multi-Task Image Restoration

https://github.com/algolzw/image-restoration-sde

공식 코드

Image Restoration with Mean-Reverting Stochastic Differential Equations

https://github.com/ali-vilab/dreamtalk

공식 코드

DreamTalk: When Emotional Talking Head Generation Meets Diffusion Probabilistic Models

https://github.com/alibaba/AliceMind/tree/main/PALM

공식 코드

PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation

https://github.com/aliciadurrer/dm_inpainting

공식 코드

Denoising Diffusion Models for 3D Healthy Brain Tissue Inpainting

https://github.com/alimd94/DiQP

공식 코드

Reversing the Damage: A QP-Aware Transformer-Diffusion Approach for 8K Video Restoration under Codec Compression

https://github.com/alimmat/bss-cffma

공식 코드

BSS-CFFMA: Cross-Domain Feature Fusion and Multi-Attention Speech Enhancement Network based on Self-Supervised Embedding

https://github.com/alipay/style-tokenizer

공식 코드

StyleTokenizer: Defining Image Style by a Single Instance for Controlling Diffusion Models

https://github.com/alirezaafzalaghaei/fKAN

공식 코드

fKAN: Fractional Kolmogorov-Arnold Networks with trainable Jacobi basis functions

https://github.com/alisiahkoohi/csgm

공식 코드

Conditional score-based diffusion models for Bayesian inference in infinite dimensions

https://github.com/alphacatplus/vmambair

공식 코드

VmambaIR: Visual State Space Model for Image Restoration

https://github.com/alphanumericslab/ecg-image-kit

공식 코드

ECG-Image-Kit: A Synthetic Image Generation Toolbox to Facilitate Deep Learning-Based Electrocardiogram Digitization

https://github.com/alr-internship/self-supervised-depth-denoising

공식 코드

Multi-Object Self-Supervised Depth Denoising

https://github.com/alsabahi2030/mh-gec

공식 코드

Multi-head Sequence Tagging Model for Grammatical Error Correction

https://github.com/alsabahi2030/synthetic-data-generation

공식 코드

Multi-head Sequence Tagging Model for Grammatical Error Correction

https://github.com/alsdudrla10/DG

공식 코드

Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models

https://github.com/alsdudrla10/DG_imagenet

공식 코드

Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models

https://github.com/aluo-x/brainsail

공식 코드

Brain Mapping with Dense Features: Grounding Cortical Semantic Selectivity in Natural Images With Vision Transformers

https://github.com/alvariseppanen/4denoisenet

공식 코드

4DenoiseNet: Adverse Weather Denoising from Adjacent Point Clouds

https://github.com/alvariseppanen/smednet

공식 코드

Multi-Echo Denoising in Adverse Weather

https://github.com/alvinhan123/ldmlr

공식 코드

Latent-based Diffusion Model for Long-tailed Recognition

https://github.com/alwaysuu/CLIPDenoising

공식 코드

Transfer CLIP for Generalizable Image Denoising

https://github.com/alwaysuu/ScoreDVI

공식 코드

Score Priors Guided Deep Variational Inference for Unsupervised Real-World Single Image Denoising

https://github.com/amaai-lab/mustango

공식 코드

Mustango: Toward Controllable Text-to-Music Generation

https://github.com/amano-k-lab/braincodec

공식 코드

BrainCodec: Neural fMRI codec for the decoding of cognitive brain states

https://github.com/amazon-science/masked-diffusion-lm

공식 코드

A Cheaper and Better Diffusion Language Model with Soft-Masked Noise

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