Deep coded exposure: end-to-end co-optimization of flutter shutter and deblurring processing for general motion blur removal  

作　　者：ZHIHONG ZHANG KAIMING DONG JINLI SUO QIONGHAI DAI 

机构地区：[1]Department of Automation,Tsinghua University,Beijing 100084,China [2]Institute for Brain and Cognitive Sciences,Tsinghua University,Beijing 100084,China [3]Shanghai Artificial Intelligence Laboratory,Shanghai 200030,China

出　　处：《Photonics Research》2023年第10期1678-1686,共9页光子学研究（英文版）

基　　金：Ministry of Science and Technology of the People's Republic of China (2020AAA0108202);National Natural Science Foundation of China (61931012, 62088102)。

摘　　要：Coded exposure photography is a promising computational imaging technique capable of addressing motion blur much better than using a conventional camera, via tailoring invertible blur kernels. However, existing methods suffer from restrictive assumptions, complicated preprocessing, and inferior performance. To address these issues,we proposed an end-to-end framework to handle general motion blurs with a unified deep neural network, and optimize the shutter's encoding pattern together with the deblurring processing to achieve high-quality sharp images. The framework incorporates a learnable flutter shutter sequence to capture coded exposure snapshots and a learning-based deblurring network to restore the sharp images from the blurry inputs. By co-optimizing the encoding and the deblurring modules jointly, our approach avoids exhaustively searching for encoding sequences and achieves an optimal overall deblurring performance. Compared with existing coded exposure based motion deblurring methods, the proposed framework eliminates tedious preprocessing steps such as foreground segmentation and blur kernel estimation, and extends coded exposure deblurring to more general blind and nonuniform cases. Both simulation and real-data experiments demonstrate the superior performance and flexibility of the proposed method.

关 键 词：motion optimization DEEP 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]