https://github.com/orpatashnik/local-prompt-mixing
Localizing Object-level Shape Variations with Text-to-Image Diffusion Models
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/overwindows/PALM
PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation
https://github.com/ozekri/SEPO
Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution
https://github.com/ozercandevecioglu/zero-shot-bearing-fault-detection-by-blind-domain-transition
https://github.com/pawan47/nilmtk_readings
Neural NILM: Deep Neural Networks Applied to Energy Disaggregation
https://github.com/pbonazzi/guassian-diffusion
https://github.com/pfmarteau/ShapeTimeSeparation
Times series averaging and denoising from a probabilistic perspective on time-elastic kernels
https://github.com/pfmarteau/eKATS
Times series averaging and denoising from a probabilistic perspective on time-elastic kernels
https://github.com/phanisankar-nidadavolu/phanisankar-nidadavolu.github.io
Feature Enhancement with Deep Feature Losses for Speaker Verification
https://github.com/phymhan/prompt-to-prompt
An Edit Friendly DDPM Noise Space: Inversion and Manipulations
https://github.com/picsart-ai-research/streamingt2v
https://github.com/pilot7747/sldl
https://github.com/piyushpandey615/GAN-based-Image-Compressor-System
An End-to-End Compression Framework Based on Convolutional Neural Networks
https://github.com/pksvision/Deep-WaveNet-Underwater-Image-Restoration
Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
https://github.com/pminhtam/DBSN_pytorch
https://github.com/pminhtam/DeepBurstDenoising
https://github.com/pminhtam/KPN_attention
https://github.com/pminhtam/KPN_tf
Attention Mechanism Enhanced Kernel Prediction Networks for Denoising of Burst Images
https://github.com/pminhtam/MIRnet_SIDD
Learning Enriched Features for Real Image Restoration and Enhancement
https://github.com/pminhtam/MWCNN
https://github.com/pminhtam/NBNet
NBNet: Noise Basis Learning for Image Denoising with Subspace Projection
https://github.com/pminhtam/Neigh2Neigh
Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images
https://github.com/pminhtam/PT-MWRN
Progressive Training of Multi-level Wavelet Residual Networks for Image Denoising
https://github.com/poloclub/robust-principles
Better Diffusion Models Further Improve Adversarial Training
https://github.com/poppinace/indexnet_matting
https://github.com/ppooiiuuyh/ComRecCNN
An End-to-End Compression Framework Based on Convolutional Neural Networks
https://github.com/pputzky/invertible_rim
https://github.com/prakashSidd18/blind_augmentation
Restormer: Efficient Transformer for High-Resolution Image Restoration
https://github.com/prateekgv/sasdpr
Sparsity-Assisted Signal Denoising and Pattern Recognition in Time-Series Data
https://github.com/princeton-computational-imaging/allyouneedisraw
All You Need is RAW: Defending Against Adversarial Attacks with Camera Image Pipelines
https://github.com/priyamtejaswin/multistep-retrieve-summarize
https://github.com/proteus1991/GridDehazeNet
https://github.com/puffnjackie/pytorch-super-resolution-implementations
https://github.com/pui-nantheera/Image-Fusion-via-Sparse-Regularization
Image Fusion via Sparse Regularization with Non-Convex Penalties
https://github.com/pwc-1/Paper-10/tree/main/tapex
Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks
https://github.com/pwc-1/Paper-9/tree/main/1/wavlm
WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.