recurrent-defocus-deblurring-synth-dual-pixel
Data generator
This project provides tools and models to generate realistic data for camera systems with defocus blur, aiming to improve image deblurring techniques.
Reference github repository for the paper "Learning to Reduce Defocus Blur by Realistically Modeling Dual-Pixel Data". We propose a procedure to generate realistic DP data synthetically. Our synthesis approach mimics the optical image formation found on DP sensors and can be applied to virtual scenes rendered with standard computer software. Leveraging these realistic synthetic DP images, we introduce a new recurrent convolutional network (RCN) architecture that can improve defocus deblurring results and is suitable for use with single-frame and multi-frame data captured by DP sensors.
49 stars
5 watching
9 forks
Language: Python
last commit: almost 3 years ago autofocuscomputational-photographycomputer-visiondatasetdatasetsdeep-learningdeep-neural-networksdeeplearningdefocus-blurdefocus-deblurringdepth-of-fielddual-pixelrecurrent-neural-networkssynthetic-datasynthetic-dataset-generation
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