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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/hengliusky/UltraSound_SSSR
공식 코드Perception Consistency Ultrasound Image Super-resolution via Self-supervised CycleGAN
https://github.com/hopoolinz/daot
공식 코드DAOT: Domain-Agnostically Aligned Optimal Transport for Domain-Adaptive Crowd Counting
https://github.com/hreynaud/dartagnan
공식 코드https://github.com/huanngzh/EpiDiff
공식 코드EpiDiff: Enhancing Multi-View Synthesis via Localized Epipolar-Constrained Diffusion
https://github.com/hyoo14/finetuned-sd3-correcting-anomalous-images
공식 코드Fine Tuning Text-to-Image Diffusion Models for Correcting Anomalous Images
https://github.com/iamlab-ryerson/mlp-srgan
공식 코드MLP-SRGAN: A Single-Dimension Super Resolution GAN using MLP-Mixer
https://github.com/icon-lab/denomamba
공식 코드DenoMamba: A fused state-space model for low-dose CT denoising
https://github.com/ideal-iqa/iqa-eval
공식 코드A study on the adequacy of common IQA measures for medical images
https://github.com/idiosyncracies/Sebica
공식 코드Sebica: Lightweight Spatial and Efficient Bidirectional Channel Attention Super Resolution Network
https://github.com/illaitar/based
공식 코드BASED: Benchmarking, Analysis, and Structural Estimation of Deblurring
https://github.com/imsb-uke/bias-transfer-microscopy
공식 코드Deep learning-based bias transfer for overcoming laboratory differences of microscopic images
https://github.com/iprobe-lab/synthetic_forestry_image_using_gan
공식 코드Synthesizing Forestry Images Conditioned on Plant Phenotype Using a Generative Adversarial Network
https://github.com/jaegercq/slingbag
공식 코드https://github.com/jarchv/solverip
공식 코드Generative Flows as a General Purpose Solution for Inverse Problems
https://github.com/jh-chung1/imgregister2dto3d
공식 코드https://github.com/jianhuapei/ldm-enabled-semcom-system
공식 코드https://github.com/jiayuanwang-jw/adc-cyclegan
공식 코드An Attentive-based Generative Model for Medical Image Synthesis
https://github.com/jiayuanwang-jw/dc-cyclegan
공식 코드DC-cycleGAN: Bidirectional CT-to-MR Synthesis from Unpaired Data
https://github.com/jingyuanli001/RFR-Inpainting
공식 코드https://github.com/jinhokim46/mrcp_dlrecon
공식 코드Deep Learning-based Accelerated MR Cholangiopancreatography without Fully-sampled Data
https://github.com/jinnan-chen/HGM
공식 코드https://github.com/jlzcode/pfan
공식 코드Progressive Frequency-Aware Network for Laparoscopic Image Desmoking
https://github.com/jnuchenyihong/nafrssr
공식 코드NAFRSSR: a Lightweight Recursive Network for Efficient Stereo Image Super-Resolution
https://github.com/johncolddd/ppn
공식 코드A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging
https://github.com/juglab/microssim
공식 코드MicroSSIM: Improved Structural Similarity for Comparing Microscopy Data
https://github.com/junyuchen245/Fully_Unsupervised_CNN_Registration_Keras
공식 코드https://github.com/jwen307/quality_uq
공식 코드Conformal Bounds on Full-Reference Image Quality for Imaging Inverse Problems
https://github.com/kangx99/smic
공식 코드https://github.com/kent0n-li/fgdm
공식 코드Zero-shot Medical Image Translation via Frequency-Guided Diffusion Models
https://github.com/kgr20/e2fnet
공식 코드From Brainwaves to Brain Scans: A Robust Neural Network for EEG-to-fMRI Synthesis
https://github.com/kn0wthing/netsdb
공식 코드https://github.com/komyeongjin/specdiff-gan
공식 코드https://github.com/krtit/semanticregen
공식 코드Removing Watermarks with Partial Regeneration using Semantic Information
https://github.com/kunkun0w0/sga
공식 코드Eliminating Gradient Conflict in Reference-based Line-Art Colorization
https://github.com/labhai-dev/Cyclic-2.5D-Perceptual-Loss
공식 코드Cyclic 2.5D Perceptual Loss for Cross-Modal 3D Medical Image Synthesis: T1w MRI to Tau PET
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.