https://github.com/aselsan-research-imaging-team/bicubic-plusplus
공식 코드Bicubic++: Slim, Slimmer, Slimmest -- Designing an Industry-Grade Super-Resolution Network
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/asindel/VSRF
공식 코드Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution Forests
https://github.com/aulrichsen/sronn
공식 코드Operational Neural Networks for Parameter-Efficient Hyperspectral Single-Image Super-Resolution
https://github.com/aupendu/sr-uncertainty
공식 코드https://github.com/aurangau/ICIP2024
공식 코드https://github.com/avc2-uestc/craft-sr
공식 코드Exploring Frequency-Inspired Optimization in Transformer for Efficient Single Image Super-Resolution
https://github.com/avc2-uestc/frequency-inspired-optimization-for-efficientsr
공식 코드Exploring Frequency-Inspired Optimization in Transformer for Efficient Single Image Super-Resolution
https://github.com/basiralab/AGSR-Net
공식 코드https://github.com/basiralab/GSR-Net
공식 코드https://github.com/batmanlab/ha-gan
공식 코드Hierarchical Amortized Training for Memory-efficient High Resolution 3D GAN
https://github.com/berkdogan2/GWAInet
공식 코드Exemplar Guided Face Image Super-Resolution without Facial Landmarks
https://github.com/bia006/darts
공식 코드DARTS: Double Attention Reference-based Transformer for Super-resolution
https://github.com/bia006/hitsr
공식 코드HiTSR: A Hierarchical Transformer for Reference-based Super-Resolution
https://github.com/bichunyang419/deedsr
공식 코드DeeDSR: Towards Real-World Image Super-Resolution via Degradation-Aware Stable Diffusion
https://github.com/billhhh/FQSR
공식 코드https://github.com/bipin-saha/bnvd
공식 코드https://github.com/bitszwang/dpt
공식 코드Detail-Preserving Transformer for Light Field Image Super-Resolution
https://github.com/brian-moser/diwa
공식 코드Waving Goodbye to Low-Res: A Diffusion-Wavelet Approach for Image Super-Resolution
https://github.com/bychelsea/cmos
공식 코드https://github.com/caojiezhang/ciaosr
공식 코드https://github.com/caojiezhang/datsr
공식 코드Reference-based Image Super-Resolution with Deformable Attention Transformer
https://github.com/ceciliavision/zoom-learn-zoom
공식 코드https://github.com/centreborelli/L1BSR
공식 코드https://github.com/chaofengc/Face-SPARNet
공식 코드https://github.com/chaofengc/femasr
공식 코드Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution Priors
https://github.com/chaofengc/iter
공식 코드Iterative Token Evaluation and Refinement for Real-World Super-Resolution
https://github.com/chaofengc/quantexsr
공식 코드Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution Priors
https://github.com/chaoma99/sr-metric
공식 코드Learning a No-Reference Quality Metric for Single-Image Super-Resolution
https://github.com/chaowentao/lfsrdiff
공식 코드LFSRDiff: Light Field Image Super-Resolution via Diffusion Models
https://github.com/chaowentao/maskblur
공식 코드MaskBlur: Spatial and Angular Data Augmentation for Light Field Image Super-Resolution
https://github.com/cheeun/cadyq
공식 코드CADyQ: Content-Aware Dynamic Quantization for Image Super-Resolution
https://github.com/cheeun/odm
공식 코드Overcoming Distribution Mismatch in Quantizing Image Super-Resolution Networks
https://github.com/chenbong/arm-net
공식 코드https://github.com/chrisdud0257/hggt
공식 코드Human Guided Ground-truth Generation for Realistic Image Super-resolution
https://github.com/chrisdud0257/ssl
공식 코드SSL: A Self-similarity Loss for Improving Generative Image Super-resolution
https://github.com/chunmeifeng/SANet
공식 코드Exploring Separable Attention for Multi-Contrast MR Image Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.