https://github.com/pingfansong/CDLSR
공식 코드Multimodal Image Super-resolution via Joint Sparse Representations induced by Coupled Dictionaries
Papers with Code 아카이브
Super-Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.
<span style="color:grey; opacity: 0.6">( Credit: MemNet )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/pk94/WVS2Benchmark
공식 코드MuS2: A Real-World Benchmark for Sentinel-2 Multi-Image Super-Resolution
https://github.com/pkuliyi2015/sd-webui-stablesr
공식 코드Exploiting Diffusion Prior for Real-World Image Super-Resolution
https://github.com/pkuxmq/Invertible-Image-Rescaling
공식 코드https://github.com/placerkyo/bsrd
공식 코드Burst Super-Resolution with Diffusion Models for Improving Perceptual Quality
https://github.com/pmm09c/ntire-dehazing
공식 코드https://github.com/pnavarre/esr
공식 코드https://github.com/polimi-ispl/nah-srcnn
공식 코드Near field Acoustic Holography on arbitrary shapes using Convolutional Neural Network
https://github.com/polimi-ispl/sr-bvoc
공식 코드Super-Resolution of BVOC Emission Maps Via Domain Adaptation
https://github.com/polltooh/traffic_video_analysis
공식 코드Understanding Traffic Density from Large-Scale Web Camera Data
https://github.com/ppplang/e2fif
공식 코드https://github.com/pranavphoenix/WaveMixSR
공식 코드WaveMixSR: A Resource-efficient Neural Network for Image Super-resolution
https://github.com/pris-cv/mssrm
공식 코드https://github.com/pris-cv/pidsr
공식 코드PIDSR: Complementary Polarized Image Demosaicing and Super-Resolution
https://github.com/pris-cv/wfen
공식 코드Efficient Face Super-Resolution via Wavelet-based Feature Enhancement Network
https://github.com/prismformore/multi-task-transformer
공식 코드Joint 2D-3D Multi-Task Learning on Cityscapes-3D: 3D Detection, Segmentation, and Depth Estimation
https://github.com/proteus1991/RawVSR
공식 코드Exploit Camera Raw Data for Video Super-Resolution via Hidden Markov Model Inference
https://github.com/prs-eth/diffusion-super-resolution
공식 코드https://github.com/prs-eth/graph-super-resolution
공식 코드https://github.com/prs-eth/thera
공식 코드Neural Fields with Thermal Activations for Arbitrary-Scale Super-Resolution
https://github.com/psrben/u2net
공식 코드U2Net: A General Framework with Spatial-Spectral-Integrated Double U-Net for Image Fusion
https://github.com/ptkin/Awesome-Super-Resolution
공식 코드https://github.com/puppy210/DaLPSR
공식 코드DaLPSR: Leverage Degradation-Aligned Language Prompt for Real-World Image Super-Resolution
https://github.com/qianngli/dualsr
공식 코드Dual-Stage Approach Toward Hyperspectral Image Super-Resolution
https://github.com/qibao77/CFSNet
공식 코드CFSNet: Toward a Controllable Feature Space for Image Restoration
https://github.com/qibao77/LFFN-master
공식 코드Lightweight Feature Fusion Network for Single Image Super-Resolution
https://github.com/qizhou000/tfman
공식 코드Single image super-resolution based on trainable feature matching attention network
https://github.com/quan-dao/practical-collab-perception
공식 코드Practical Collaborative Perception: A Framework for Asynchronous and Multi-Agent 3D Object Detection
https://github.com/question110/omnirwkvsr
공식 코드Exploring Linear Attention Alternative for Single Image Super-Resolution
https://github.com/qyp2000/varsr
공식 코드https://github.com/qyp2000/xpsr
공식 코드XPSR: Cross-modal Priors for Diffusion-based Image Super-Resolution
https://github.com/r9y9/wavenet_vocoder
공식 코드https://github.com/rachddou/deadleavesplus
공식 코드VibrantLeaves: A principled parametric image generator for training deep restoration models
https://github.com/rami0205/ngramswin
공식 코드N-Gram in Swin Transformers for Efficient Lightweight Image Super-Resolution
https://github.com/rami0205/ramit
공식 코드Reciprocal Attention Mixing Transformer for Lightweight Image Restoration
https://github.com/raoumer/rbsricnn
공식 코드RBSRICNN: Raw Burst Super-Resolution through Iterative Convolutional Neural Network
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.