https://github.com/tum-vision/learn_prox_ops
공식 코드Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems
Papers with Code 아카이브
Demosaicking
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Most modern digital cameras acquire color images by measuring only one color channel per pixel, red, green, or blue, according to a specific pattern called the Bayer pattern. Demosaicking is the processing step that reconstruct a full color image given these incomplete measurements.
Source: Revisiting Non Local Sparse Models for Image Restoration
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/xyvirtualgroup/adaptivepnp_sci
공식 코드Adaptive Deep PnP Algorithm for Video Snapshot Compressive Imaging
https://github.com/yqx7150/hgm
공식 코드High-dimensional Assisted Generative Model for Color Image Restoration
https://github.com/yulunzhang/RNAN
공식 코드https://github.com/yunfanlu/ev-demosaic
공식 코드Event Camera Demosaicing via Swin Transformer and Pixel-focus Loss
https://gitlab.com/trougnouf/rawnind_jddc
공식 코드Learning Joint Denoising, Demosaicing, and Compression from the Raw Natural Image Noise Dataset
https://github.com/12dmodel/camera_sim
Generating Training Data for Denoising Real RGB Images via Camera Pipeline Simulation
https://github.com/HedgehogCode/deep-plug-and-play-prior
https://github.com/HedgehogCode/denoising-gan
https://github.com/HilaManor/Generative-deep-features
https://github.com/Hsintao/CAR
https://github.com/JVision/Handheld-Multi-Frame-Super-Resolution
https://github.com/MKFMIKU/RAW2RGBNet
HighEr-Resolution Network for Image Demosaicing and Enhancing
https://github.com/MindCode-4/code-8/tree/main/Projected-Distribution
https://github.com/MindSpore-scientific/code-5/tree/main/Projected-Distribution
https://github.com/Scientific-Research-Algorithm-Toolbox/SCI-algorithms
Memory-Efficient Network for Large-scale Video Compressive Sensing
https://github.com/ZZL-1998/MW-ISPNet
AIM 2020 Challenge on Learned Image Signal Processing Pipeline
https://github.com/ah7149407/ee193-03_project
DeepISP: Towards Learning an End-to-End Image Processing Pipeline
https://github.com/aiff22/PyNET-PyTorch
Replacing Mobile Camera ISP with a Single Deep Learning Model
https://github.com/bruinxiong/RNAN
https://github.com/cig-skoltech/burst-cvpr-2019
https://github.com/gmalivenko/pynet-v2
Replacing Mobile Camera ISP with a Single Deep Learning Model
https://github.com/iset/iset3d
Soft Prototyping Camera Designs for Car Detection Based on a Convolutional Neural Network
https://github.com/jihoojo03/UNet-CBAM_Keras
https://github.com/kunzmi/ImageStackAlignator
https://github.com/nickolor/Learning-to-See-in-the-Dark_and_DeepISP
DeepISP: Towards Learning an End-to-End Image Processing Pipeline
https://github.com/pruthvip98/Image-Forgery-Detection
https://github.com/ricsonc/more-pixels-less-noise
https://github.com/saurabh-kataria/projected-distribution-loss
https://github.com/shmilyo/Low-Cost-Edge-Sensing-for-High-Quality-Demosaicking
https://github.com/siavashbigdeli/DMSP
https://github.com/sjmoran/difar
https://github.com/sjmoran/neural_curve_layers
https://github.com/ucaswangls/stformer
https://github.com/zxr931120/-
Deep Residual Network for Joint Demosaicing and Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.