Open AccessDepth Estimation of Single Defocused Images Based on Multi-Feature Fusion
Author(s) -
Feng-Yun Cao
Publication year - 2021
Publication title -
traitement du signal/ts. traitement du signal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.279
H-Index - 11
eISSN - 1958-5608
pISSN - 0765-0019
DOI - 10.18280/ts.380510
Subject(s) - artificial intelligence , computer vision , feature (linguistics) , gaussian , gaussian blur , computer science , mathematics , segmentation , image restoration , filter (signal processing) , rotation (mathematics) , image (mathematics) , pattern recognition (psychology) , image processing , philosophy , linguistics , physics , quantum mechanics
Based on multi-feature fusion, this paper introduces a novel depth estimation method to suppress defocus and motion blurs, as well as focal plane ambiguity. Firstly, the node features formed by occlusion were fused to optimize image segmentation, and obtain the position relations between image objects. Next, the Gaussian gradient ratio between the defocused input image and the quadratic Gaussian blur was calculated to derive the edge sparse blur. After that, the fast guided filter was adopted to diffuse the sparse blur globally, and estimate the relative depth of the scene. Experimental results demonstrate that our method excellently resolves the ambiguity of depth estimation, and accurately overcomes the noise problem in real-time.