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MS-SSIM
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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A MS-SSIM score helps to analyze how much a De-warping module has been able to de-warp a document image from its initial distorted view.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/DVLP-CMATERJU/RectiNet
공식 코드A Gated and Bifurcated Stacked U-Net Module for Document Image Dewarping
https://github.com/GuoLusjtu/DVC
공식 코드https://github.com/InterDigitalInc/CompressAI
공식 코드CompressAI: a PyTorch library and evaluation platform for end-to-end compression research
https://github.com/JasonZHM/CAE-ADMM
공식 코드CAE-ADMM: Implicit Bitrate Optimization via ADMM-based Pruning in Compressive Autoencoders
https://github.com/JasonZHM/CAEP
공식 코드CAE-ADMM: Implicit Bitrate Optimization via ADMM-based Pruning in Compressive Autoencoders
https://github.com/JianpingLin/M-LVC_CVPR2020
공식 코드M-LVC: Multiple Frames Prediction for Learned Video Compression
https://github.com/JooyoungLeeETRI/CA_Entropy_Model
공식 코드Context-adaptive Entropy Model for End-to-end Optimized Image Compression
https://github.com/KUIS-AI-Tekalp-Research-Group/video-compression
공식 코드End-to-End Rate-Distortion Optimized Learned Hierarchical Bi-Directional Video Compression
https://github.com/Meixu-Chen/MOVI-Codec
공식 코드https://github.com/RenYang-home/HLVC
공식 코드Learning for Video Compression with Hierarchical Quality and Recurrent Enhancement
https://github.com/RenYang-home/OpenDVC
공식 코드OpenDVC: An Open Source Implementation of the DVC Video Compression Method
https://github.com/RenYang-home/RLVC
공식 코드Learning for Video Compression with Recurrent Auto-Encoder and Recurrent Probability Model
https://github.com/Stillrot/RGINP
공식 코드https://github.com/YannickStruempler/LearnedJPEG
공식 코드Learning to Improve Image Compression without Changing the Standard Decoder
https://github.com/ZhengxueCheng/Learned-Image-Compression-with-GMM-and-Attention
공식 코드Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules
https://github.com/ach-1914/ladlenet
공식 코드https://github.com/bowenl0218/bpgan-signal-compression
공식 코드Unified Signal Compression Using a GAN with Iterative Latent Representation Optimization
https://github.com/bowenl0218/mmvc_video_codec
공식 코드https://github.com/curto2/c
공식 코드High-Resolution Deep Convolutional Generative Adversarial Networks
https://github.com/dleninja/adcnet
공식 코드https://github.com/ds-kiel/mcucoder
공식 코드MCUCoder: Adaptive Bitrate Learned Video Compression for IoT Devices
https://github.com/dsaragih/diffuse-gen
공식 코드Using Diffusion Models to Generate Synthetic Labelled Data for Medical Image Segmentation
https://github.com/dsevero/linear-autoregressive-similarity-index
공식 코드The Unreasonable Effectiveness of Linear Prediction as a Perceptual Metric
https://github.com/fab-jul/imgcomp-cvpr
공식 코드https://github.com/fengyurenpingsheng/Learned-image-compression-with-GLLMM
공식 코드https://github.com/finger-monkey/ReID_Adversarial_Defense
공식 코드A Person Re-identification Data Augmentation Method with Adversarial Defense Effect
https://github.com/guanlongzhao/single-image-dehazing
공식 코드https://github.com/ideal-iqa/iqa-eval
공식 코드A study on the adequacy of common IQA measures for medical images
https://github.com/imsb-uke/bias-transfer-microscopy
공식 코드Deep learning-based bias transfer for overcoming laboratory differences of microscopic images
https://github.com/jianhuapei/ldm-enabled-semcom-system
공식 코드https://github.com/juglab/microssim
공식 코드MicroSSIM: Improved Structural Similarity for Comparing Microscopy Data
https://github.com/liuzhening111/bisic
공식 코드Bidirectional Stereo Image Compression with Cross-Dimensional Entropy Model
https://github.com/makbari7/DSSLIC
공식 코드DSSLIC: Deep Semantic Segmentation-based Layered Image Compression
https://github.com/moha23/LF-DAAE
공식 코드Learning-Based Practical Light Field Image Compression Using A Disparity-Aware Model
https://github.com/njuvision/nic
공식 코드Neural Image Compression via Non-Local Attention Optimization and Improved Context Modeling
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.