Learning a Reinforced Agent for Flexible Exposure Bracketing Selection

CVPR 2020 Zhouxia WangJiawei ZhangMude LinJiong WangPing LuoJimmy Ren

Automatically selecting exposure bracketing (images exposed differently) is important to obtain a high dynamic range image by using multi-exposure fusion. Unlike previous methods that have many restrictions such as requiring camera response function, sensor noise model, and a stream of preview images with different exposures (not accessible in some scenarios e.g. some mobile applications), we propose a novel deep neural network to automatically select exposure bracketing, named EBSNet, which is sufficiently flexible without having the above restrictions... (read more)

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