We consider the generic problem of detecting low-level structures in images, which includes
segmenting the manipulated parts, identifying out-of-focus pixels, separating shadow …

In recent years, deep neural networks have been successful in both industry and academia,
especially for computer vision tasks. The great success of deep learning is mainly due to its …

The images captured by the cameras contain distortions, misclassified pixels, uncertainties
and poor contrast. Therefore, the multi-focus image fusion (MFIF) integrates various input …

Abstract Zero-shot Video Object Segmentation (ZSVOS) aims at segmenting the primary
moving object without any human annotations. Mainstream solutions mainly focus on …

Recently, deep convolutional neural network (CNN) based methods for multi-focus image
fusion have achieved adequate performance. However, most of them cannot obtain spatially …

Existing defocus blur detection (DBD) methods generally perform well on a single type of
unfocused blur scene (eg, foreground focus), thereby suffering from the performance …

Foreground segmentation is a fundamental problem in computer vision, which includes
salient object detection, forgery detection, defocus blur detection, shadow detection, and …

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 …

The emergence of powerful deep learning architectures has resulted in breakthrough
innovations in several fields such as healthcare, precision farming, banking, education, and …

By adopting popular pixel-wise loss, existing methods for defocus deblurring heavily rely on
well aligned training image pairs. Although training pairs of ground-truth and blurry images …