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Color-coded aperture (CCA) methods can physically measure the depth of ascene given by physical cues from a single-shot image of a monocular camera.However, they are vulnerable to actual lens aberrations in real scenes becausethey assume an ideal lens for simplifying algorithms. In this paper, we proposephysical cue-based deep learning for CCA photography. To address actual lensaberrations, we developed a deep deaberration network (DDN) that isadditionally equipped with a self-attention mechanism of position and colorchannels to efficiently learn the lens aberration. Furthermore, a new Bayes L1loss function based on Bayesian deep learning enables to handle the uncertaintyof depth estimation more accurately. Quantitative and qualitative comparisonsdemonstrate that our method is superior to conventional methods including realoutdoor scenes. Furthermore, compared to a long-baseline stereo camera, theproposed method provides an error-free depth map at close range, as there is noblind spot between the left and right cameras.

Physical Cue based Depth-Sensing by Color Coding with Deaberration Network