Event cameras are novel vision sensors that output pixel-level brightness changes ("
events") instead of traditional video frames. These asynchronous sensors offer several …

Existing defocus blur detection (DBD) methods usually explore multi-scale and multi-level
features to improve performance. However, defocus blur regions normally have incomplete …

Defocus blur detection (DBD) is a fundamental yet challenging topic, since the
homogeneous region is obscure and the transition from the focused area to the unfocused …

Defocus blur detection (DBD) for natural images is a challenging vision task especially in the
presence of homogeneous regions and gradual boundaries. In this paper, we propose a …

Shape from focus (SFF) reconstructs 3D shape of the scene from a sequence of multi-focus
images, and the quality of reconstructed shape mainly depends on the accuracy of image …

Although existing fully-supervised defocus blur detection (DBD) models significantly improve
performance, training such deep models requires abundant pixel-level manual annotation …

Understanding blur from a single defocused image contains two tasks of defocus detection
and deblurring. This paper makes the earliest effort to jointly learn both defocus detection …

Abstract Depth-from-focus (DFF) is a technique that infers depth using the focus change of a
camera. In this work, we propose a convolutional neural network (CNN) to find the best …

For better photography, most recent commercial cameras including smartphones have either
adopted large-aperture lens to collect more light or used a burst mode to take multiple …

In this paper, we address the problem of depth recovery from a sequence of multi-focus
images, known as shape-from-focus (SFF). The conventional SFF techniques typically …