https://github.com/junhyukk/MAMNet-Tensorflow
공식 코드MAMNet: Multi-path Adaptive Modulation Network for Image Super-Resolution
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/junjun-jiang/Face-Hallucination-Benchmark
공식 코드https://github.com/junjun-jiang/Hyperspectral-Image-Super-Resolution-Benchmark
공식 코드Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery
https://github.com/junjun-jiang/SSPSR
공식 코드Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery
https://github.com/junpan19/ETDM
공식 코드Look Back and Forth: Video Super-Resolution with Explicit Temporal Difference Modeling
https://github.com/junpan19/RRN
공식 코드https://github.com/junpan19/RSDN
공식 코드Video Super-Resolution with Recurrent Structure-Detail Network
https://github.com/junpan19/VSR_TGA
공식 코드https://github.com/junsang7777/rzsr
공식 코드RZSR: Reference-based Zero-Shot Super-Resolution with Depth Guided Self-Exemplars
https://github.com/jwgdmkj/lmlt
공식 코드LMLT: Low-to-high Multi-Level Vision Transformer for Image Super-Resolution
https://github.com/kaankaramanofficial/GSR4B
공식 코드{GSR4B}: Biomass Map Super-Resolution with Sentinel-1/2 Guidance
https://github.com/kai-liu001/2dquant
공식 코드2DQuant: Low-bit Post-Training Quantization for Image Super-Resolution
https://github.com/kai-liu001/bimacosr
공식 코드https://github.com/kai-liu001/condiquant
공식 코드CondiQuant: Condition Number Based Low-Bit Quantization for Image Super-Resolution
https://github.com/kaneigi/weatherproofchallenge
공식 코드Enhanced Semantic Segmentation Pipeline for WeatherProof Dataset Challenge
https://github.com/kangliao929/cylin-painting
공식 코드Cylin-Painting: Seamless {360\textdegree} Panoramic Image Outpainting and Beyond
https://github.com/karlygzhu/rgan
공식 코드A Lightweight Recurrent Grouping Attention Network for Video Super-Resolution
https://github.com/karoslyx/mffssr
공식 코드Multi-Level Feature Fusion Network for Lightweight Stereo Image Super-Resolution
https://github.com/karthickpgunasekaran/superresolutionwithrdn
공식 코드Ultra Sharp : Study of Single Image Super Resolution using Residual Dense Network
https://github.com/kasliwal17/corefusion
공식 코드CoReFusion: Contrastive Regularized Fusion for Guided Thermal Super-Resolution
https://github.com/keqpan/udsr
공식 코드https://github.com/kfukami/Voronoi-CNN
공식 코드Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning
https://github.com/kimchange/pilf
공식 코드Physics-Informed Ensemble Representation for Light-Field Image Super-Resolution
https://github.com/kimsure/tcnet-for-vsr
공식 코드Temporal Consistency Learning of inter-frames for Video Super-Resolution
https://github.com/kiteretsu77/apisr
공식 코드APISR: Anime Production Inspired Real-World Anime Super-Resolution
https://github.com/kiteretsu77/vcisr-official
공식 코드VCISR: Blind Single Image Super-Resolution with Video Compression Synthetic Data
https://github.com/kitoweeknd/RFUAV
공식 코드RFUAV: A Benchmark Dataset for Unmanned Aerial Vehicle Detection and Identification
https://github.com/kli8996/HSISR
공식 코드Hyperspectral Image Super-Resolution with Spectral Mixup and Heterogeneous Datasets
https://github.com/kligvasser/xUnit
공식 코드https://github.com/knazeri/edge-informed-sisr
공식 코드https://github.com/korouuuuu/hma
공식 코드HMANet: Hybrid Multi-Axis Aggregation Network for Image Super-Resolution
https://github.com/krenerd/ultimate-sr
공식 코드https://github.com/kritiksoman/GIMP-ML
공식 코드GIMP-ML: Python Plugins for using Computer Vision Models in GIMP
https://github.com/kuis-ai-tekalp-research-group/perceptual-vsr
공식 코드Spatio-Temporal Perception-Distortion Trade-off in Learned Video SR
https://github.com/kuis-ai-tekalp-research-group/video-deinterlacing
공식 코드https://github.com/kumar-shridhar/PyTorch-BayesianCNN
공식 코드A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.