RGB-D mutual guidance for semi-supervised defocus blur detection

Abstract

Defocus blur detection (DBD) aims to detect and locate defocus features in visual scenes. While available fully supervised DBD methods improve detection accuracy, they rely on large-scale handcrafted pixel-level labels and single-mode images, which makes them expensive and error-prone. In this work, we explore the use of depth information in a semi-supervised DBD method for the first time. Different from previous approaches, we generate a pair of reversible defocused homogeneous regions in weakly supervised mutual guidance networks (MGMs) to provide weak semantic guidance for this task. In a strongly supervised mutual attention network, depth information and RGB features are used to learn the defocus blur homogeneous region from the ground truth (GT). Meanwhile, depth information is extracted using a depth estimation network to guide the defocus location and provide a strong prior for the weakly supervised part. The experimental results show that our network outperform the available fully supervised methods in DBD, and provides new inspiration for research on semi-supervised robust and RGB-D multi-modal DBD.