https://github.com/Zheng222/PPON
공식 코드Progressive Perception-Oriented Network for Single Image Super-Resolution
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/ZhengyuLiang24/LFT
공식 코드https://github.com/ZhouYiiFeng/CDSR
공식 코드Joint Learning Content and Degradation Aware Feature for Blind Super-Resolution
https://github.com/ZhuKeven/MOBOSR
공식 코드Perceptual-Distortion Balanced Image Super-Resolution is a Multi-Objective Optimization Problem
https://github.com/aamir-mustafa/super-resolution-adversarial-defense
공식 코드Image Super-Resolution as a Defense Against Adversarial Attacks
https://github.com/aaravamudan2014/SIDDIS
공식 코드Data-driven Super-Resolution of Flood Inundation Maps using Synthetic Simulations
https://github.com/abnervictor/hyperthumbnail
공식 코드Real-time 6K Image Rescaling with Rate-distortion Optimization
https://github.com/adamian98/pulse
공식 코드PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/aesrgan/A-ESRGAN
공식 코드A-ESRGAN: Training Real-World Blind Super-Resolution with Attention U-Net Discriminators
https://github.com/agusgun/cisr
공식 코드https://github.com/aimerykong/predictive-filter-flow
공식 코드https://github.com/aimiokab/misr-s2
공식 코드Cross-sensor super-resolution of irregularly sampled Sentinel-2 time series
https://github.com/aitical/cfsr
공식 코드Transforming Image Super-Resolution: A ConvFormer-based Efficient Approach
https://github.com/aitical/scnet
공식 코드Fully $1\times1$ Convolutional Network for Lightweight Image Super-Resolution
https://github.com/aitical/sscsr
공식 코드Exploiting Self-Supervised Constraints in Image Super-Resolution
https://github.com/aitical/tcsr
공식 코드Incorporating Transformer Designs into Convolutions for Lightweight Image Super-Resolution
https://github.com/ajiljalal/code-cs-fairness
공식 코드Fairness for Image Generation with Uncertain Sensitive Attributes
https://github.com/akshaydudhane16/bipnet
공식 코드https://github.com/alexzou14/cvhssr
공식 코드Cross-View Hierarchy Network for Stereo Image Super-Resolution
https://github.com/alexzou14/scet
공식 코드Self-Calibrated Efficient Transformer for Lightweight Super-Resolution
https://github.com/algolzw/bsrt
공식 코드BSRT: Improving Burst Super-Resolution with Swin Transformer and Flow-Guided Deformable Alignment
https://github.com/algolzw/image-restoration-sde
공식 코드Image Restoration with Mean-Reverting Stochastic Differential Equations
https://github.com/algolzw/ncnet
공식 코드Fast Nearest Convolution for Real-Time Efficient Image Super-Resolution
https://github.com/alik033/ml-craist
공식 코드https://github.com/alireza-aghelan/ig-cfat
공식 코드https://github.com/alphacatplus/vmambair
공식 코드https://github.com/amanchadha/iSeeBetter
공식 코드https://github.com/amazingren/ntire2024_esr
공식 코드The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
https://github.com/andreas128/SRFlow
공식 코드SRFlow: Learning the Super-Resolution Space with Normalizing Flow
https://github.com/andreasfloros/trustworthy-super-resolution
공식 코드Trustworthy Image Super-Resolution via Generative Pseudoinverse
https://github.com/aneesh3108/AeroRIT
공식 코드https://github.com/angelvillar96/super-resolution-noisy-images
공식 코드Deep learning architectural designs for super-resolution of noisy images
https://github.com/arashfree/TLW-in-Super-Resolution
공식 코드https://github.com/arcananana/cdcn
공식 코드Bridging Component Learning with Degradation Modelling for Blind Image Super-Resolution
https://github.com/arcananana/dssr
공식 코드Learning Detail-Structure Alternative Optimization for Blind Super-Resolution
https://github.com/arctichare105/s3diff
공식 코드Degradation-Guided One-Step Image Super-Resolution with Diffusion Priors
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.