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SSIM

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/woldier/witunet

공식 코드

WiTUnet: A U-Shaped Architecture Integrating CNN and Transformer for Improved Feature Alignment and Local Information Fusion

https://github.com/wuyushuwys/compiler-aware-nas-sr

공식 코드

Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-Resolution

https://github.com/wyq2021/s2lic

공식 코드

S2LIC: Learned Image Compression with the SwinV2 Block, Adaptive Channel-wise and Global-inter Attention Context

https://github.com/xfsun99/ccyclegan-tf2

공식 코드

Constrained CycleGAN for Effective Generation of Ultrasound Sector Images of Improved Spatial Resolution

https://github.com/xiangyu8/mofa

공식 코드

MOFA: A Model Simplification Roadmap for Image Restoration on Mobile Devices

https://github.com/xiaojiao929/fgc2f-udiff

공식 코드

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis

https://github.com/xray-imaging/xfusion

공식 코드

Deep learning-based spatio-temporal fusion for high-fidelity ultra-high-speed x-ray radiography

https://github.com/xsxjtu/MFF-SSIM

공식 코드

Towards Reducing Severe Defocus Spread Effects for Multi-Focus Image Fusion via an Optimization Based Strategy

https://github.com/xtarx/StainGAN

공식 코드

StainGAN: Stain Style Transfer for Digital Histological Images

https://github.com/yanggaouq/ssdm-mri

공식 코드

Highly Undersampled MRI Reconstruction via a Single Posterior Sampling of Diffusion Models

https://github.com/yaozzz666/dscf-sr

공식 코드

Distillation-Supervised Convolutional Low-Rank Adaptation for Efficient Image Super-Resolution

https://github.com/yazaazou/galaxy-band-conversion

공식 코드

Mapping Galaxy Images Across Ultraviolet, Visible and Infrared Bands Using Generative Deep Learning

https://github.com/yizhangphd/freqpcr

공식 코드

Frequency-Modulated Point Cloud Rendering with Easy Editing

https://github.com/yjump/NAS-for-CSMRI

공식 코드

Neural Architecture Search for Compressed Sensing Magnetic Resonance Image Reconstruction

https://github.com/yongsongh/irsrmamba

공식 코드

IRSRMamba: Infrared Image Super-Resolution via Mamba-based Wavelet Transform Feature Modulation Model

https://github.com/yousefis/ASL2PET

공식 코드

ASL to PET Translation by a Semi-supervised Residual-based Attention-guided Convolutional Neural Network

https://github.com/yousefkotp/Flare-Free-Vision-Empowering-Uformer-with-Depth-Insights

공식 코드

Toward Flare-Free Images: A Survey

https://github.com/yransun/difr3ct

공식 코드

DIFR3CT: Latent Diffusion for Probabilistic 3D CT Reconstruction from Few Planar X-Rays

https://github.com/yransun/legopet

공식 코드

LegoPET: Hierarchical Feature Guided Conditional Diffusion for PET Image Reconstruction

https://github.com/yukui-1999/ecg-cmr

공식 코드

Translating Electrocardiograms to Cardiac Magnetic Resonance Imaging Useful for Cardiac Assessment and Disease Screening: A Multi-Center Study AI for ECG to CMR Translation Study

https://github.com/yunguo224/ug2_deraining

공식 코드

A Two-Stage Real Image Deraining Method for GT-RAIN Challenge CVPR 2023 Workshop UG$^{\textbf{2}}$+ Track 3

https://github.com/yuqing-liu-dut/ISRN

공식 코드

Iterative Network for Image Super-Resolution

https://github.com/yuqing-liu-dut/bit-depth-expansion

공식 코드

Learning Weighting Map for Bit-Depth Expansion within a Rational Range

https://github.com/zehong-ma/magcache

공식 코드

MagCache: Fast Video Generation with Magnitude-Aware Cache

https://github.com/zeyuxiao1997/EDPN

공식 코드

EDPN: Enhanced Deep Pyramid Network for Blurry Image Restoration

https://github.com/zhangbaijin/enlighten-anything

공식 코드

Enlighten Anything: When Segment Anything Model Meets Low-Light Image Enhancement

https://github.com/zhangbaijin/memorynet

공식 코드

Memory augment is All You Need for image restoration

https://github.com/zhangsong1213/pdcfnet

공식 코드

PDCFNet: Enhancing Underwater Images through Pixel Difference Convolution

https://github.com/zhenxuan-zhang/pta4unpaired_hf_mri_syn

공식 코드

Pretext Task Adversarial Learning for Unpaired Low-field to Ultra High-field MRI Synthesis

https://github.com/zhongping-zhang/sgc_net

공식 코드

Complex Scene Image Editing by Scene Graph Comprehension

https://github.com/zhujiarui42/cfp-loss

공식 코드

Feature-oriented Deep Learning Framework for Pulmonary Cone-beam CT (CBCT) Enhancement with Multi-task Customized Perceptual Loss

https://github.com/zjlab-ammi/restormer-plus

공식 코드

Restormer-Plus for Real World Image Deraining: One State-of-the-Art Solution to the GT-RAIN Challenge (CVPR 2023 UG2+ Track 3)

https://gitlab.com/monogenic-layer-m6/monogenic-layer-trainablebio-inspired-cnnlayerforcontrastinvariance

공식 코드

A trainable monogenic ConvNet layer robust in front of large contrast changes in image classification

https://github.com/2024-MindSpore-1/Code7/tree/main/csd

Towards Compact Single Image Super-Resolution via Contrastive Self-distillation

https://github.com/Akash-Ramjyothi/Cloud-Removal-with-GAN-Satellite-Image-Processing

Cloud removal in remote sensing images using generative adversarial networks and SAR-to-optical image translation

https://github.com/Akash-Ramjyothi/Cloud-and-Fog-removal-System-using-Generative-Adversarial-Networks-Satellite-Image-Processing

Cloud removal in remote sensing images using generative adversarial networks and SAR-to-optical image translation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.