In this paper, we introduce a variational Bayesian algorithm (VBA) for image blind
deconvolution. Our VBA generic framework incorporates smoothness priors on the unknown …

Global motion greatly increases the number of false alarms for object detection in video
sequences against dynamic backgrounds. Therefore, before detecting the target in the …

In recent years, deep neural network-based restoration methods have achieved state-of-the-
art results in various image deblurring tasks. However, one major drawback of deep learning …

Visual dimension measurement is commonly required in industrial detection. However,
monocular vision measurements have a low accuracy with large field of view (FOV), while …

In the literature, coarse-to-fine or scale-recurrent approach ie progressively restoring a clean
image from its low-resolution versions has been successfully employed for single image …

Supervised networks address the task of low-light enhancement using paired images.
However, collecting a wide variety of low-light/clean paired images is tedious as the scene …

Satellite images are typically subject to multiple distortions. Different factors affect the quality
of satellite images, including changes in atmosphere, surface reflectance, sun illumination …

Several supervised networks exist that remove haze information from underwater images
using paired datasets and pixel-wise loss functions. However, training these networks …

In this paper, we address the problem of dynamic scene deblurring in the presence of
motion blur. Restoration of images affected by severe blur necessitates a network design …

We present a solution for the goal of extracting a video from a single motion blurred image to
sequentially reconstruct the clear views of a scene as beheld by the camera during the time …