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 …

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 …

Lightfield phase modulation has become an effective implementation for extending depth-of-
field (DOF) of computational imaging. However, correct reconstruction of tiny details and …

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 …

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 …

We present a method to estimate the depth of field effect from a single image. Most existing
methods related to this task provide either a per-pixel estimation of blur and/or depth …

Convolutional neural networks have achieved competitive performance in defocus blur
detection (DBD). However, due to the different receptive fields of different convolutional …

Image restoration (IR) tasks aim to form a balance between complex textures and spatial
details. To this end, the combination of local and non-local attention mechanisms has been …

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 …

Surveillance videos taken in unconstrained environments can be tampered with due to
different environmental factors and malicious human activities. They often blur the video …