https://github.com/robotic-vision-lab/Attention-With-Varying-Receptive-Fields-Network
공식 코드Thermal Image Super-Resolution Using Second-Order Channel Attention with Varying Receptive Fields
Papers with Code 아카이브
Image Super-Resolution
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Image Super-Resolution is a machine learning task where the goal is to increase the resolution of an image, often by a factor of 4x or more, while maintaining its content and details as much as possible. The end result is a high-resolution version of the original image. This task can be used for various applications such as improving image quality, enhancing visual detail, and increasing the accuracy of computer vision algorithms.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/robotic-vision-lab/dual-interactive-implicit-neural-network
공식 코드Single Image Super-Resolution via a Dual Interactive Implicit Neural Network
https://github.com/roldbach/autoencoder-3d-ct-sisr
공식 코드https://github.com/royson/metakernelgan
공식 코드Meta-Learned Kernel For Blind Super-Resolution Kernel Estimation
https://github.com/rubbbbbbbbby/swinoir
공식 코드https://github.com/sad192/large-kernel-transformer
공식 코드LKFormer: Large Kernel Transformer for Infrared Image Super-Resolution
https://github.com/saeed-anwar/SRsurvey
공식 코드https://github.com/saic-mdal/HiDT
공식 코드https://github.com/sanghyun-son/srwarp
공식 코드SRWarp: Generalized Image Super-Resolution under Arbitrary Transformation
https://github.com/sangyun884/mssr
공식 코드https://github.com/saturnian77/asid
공식 코드https://github.com/saturnian77/drsan
공식 코드A Dynamic Residual Self-Attention Network for Lightweight Single Image Super-Resolution
https://github.com/sbelharbi/sr-caco-2
공식 코드SR-CACO-2: A Dataset for Confocal Fluorescence Microscopy Image Super-Resolution
https://github.com/sczhou/IGNN
공식 코드Cross-Scale Internal Graph Neural Network for Image Super-Resolution
https://github.com/sdl-asu/acla
공식 코드https://github.com/sdv4/FDPL
공식 코드https://github.com/sejinpark-cv/im-lut
공식 코드IM-LUT: Interpolation Mixing Look-Up Tables for Image Super-Resolution
https://github.com/seungho-snu/SROOE
공식 코드Perception-Oriented Single Image Super-Resolution using Optimal Objective Estimation
https://github.com/seungho-snu/fxsr
공식 코드Flexible Style Image Super-Resolution using Conditional Objective
https://github.com/sfm-sr-denoising/sfm
공식 코드Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks
https://github.com/shadyabh/Correction-Filter
공식 코드Correction Filter for Single Image Super-Resolution: Robustifying Off-the-Shelf Deep Super-Resolvers
https://github.com/shadyabh/IAGAN
공식 코드https://github.com/shallowdream204/SODA-SR
공식 코드Uncertainty-Aware Source-Free Adaptive Image Super-Resolution with Wavelet Augmentation Transformer
https://github.com/shamanDevel/IsosurfaceSuperresolution
공식 코드Volumetric Isosurface Rendering with Deep Learning-Based Super-Resolution
https://github.com/shangqigao/gsq-image-SR
공식 코드Multi-scale deep neural networks for real image super-resolution
https://github.com/shangwei5/amsr
공식 코드High-Frequency Prior-Driven Adaptive Masking for Accelerating Image Super-Resolution
https://github.com/shank2358/mudet
공식 코드https://github.com/sheqi/GAN_Review
공식 코드Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy
https://github.com/shermanlian/kbnet
공식 코드https://github.com/shermanlian/swrn
공식 코드Sliding Window Recurrent Network for Efficient Video Super-Resolution
https://github.com/shuntama/srdd
공식 코드https://github.com/sijieliu518/sgenet
공식 코드Efficient scene text image super-resolution with semantic guidance
https://github.com/sisrformer/grformer
공식 코드GRFormer: Grouped Residual Self-Attention for Lightweight Single Image Super-Resolution
https://github.com/sleepylin/tasr
공식 코드TASR: Timestep-Aware Diffusion Model for Image Super-Resolution
https://github.com/srijamiya/LittleYOLO-SPP
공식 코드LittleYOLO-SPP: A Delicate Real-Time Vehicle Detection Algorithm
https://github.com/ssarfraz/SPL
공식 코드Content and Colour Distillation for Learning Image Translations with the Spatial Profile Loss
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.