In this paper, we propose the first end-to-end convolutional neural network (CNN)
architecture, Defocus Map Estimation Network (DMENet), for spatially varying defocus map …

Single image defocus deblurring (SIDD) is a challenging task due to the spatially-varying
nature of defocus blur, characterized by per-pixel point spread functions (PSFs). Existing …

Albeit great success has been achieved in image defocus blur detection, there are still
several unsolved challenges, eg, interference of background clutter, scale sensitivity and …

Single image defocus deblurring (SIDD) refers to recovering an all-in-focus image from a
defocused blurry one. It is a challenging recovery task due to the spatially-varying defocus …

Defocus blur detection aims to detect out-of-focus regions from an image. Although attracting
more and more attention due to its widespread applications, defocus blur detection still …

Defocus blur is one kind of blur effects often seen in images, which is challenging to remove
due to its spatially variant amount. This paper presents an end-to-end deep learning …

Defocus blur detection (DBD) is aimed to estimate the probability of each pixel being in-
focus or out-of-focus. This process has been paid considerable attention due to its …

This paper proposes an end-to-end deep learning approach for removing defocus blur from
a single defocused image. Defocus blur is a common issue in digital photography that poses …

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 often degrades the performance of image understanding, such as object
recognition and image segmentation. Restoring an all-in-focus image from its defocused …