Dark Channel-Assisted Depth-from-Defocus from a Single Image

In this paper, we utilize the dark channel as a complementary cue to estimate the depth of a scene from a single space-variant defocus blurred image due to its effectiveness in implicitly capturing the local statistics of blurred images and the scene structure. Existing depth-from-defocus (DFD) techniques typically rely on multiple images with varying apertures or focus settings to recover depth information. Very few attempts have focused on DFD from a single defocused image due to the underconstrained nature of the problem. Our method capitalizes on the relationship between local defocus blur and contrast variations as key depth cues to enhance the overall performance in estimating the scene's structure. The entire pipeline is trained adversarially in a fully end-to-end fashion. Experiments conducted on real data with realistic depth-induced defocus blur demonstrate that incorporating dark channel prior into single image DFD yields meaningful depth estimation results, validating the effectiveness of our approach.