Image deblurring is a classic problem in low-level computer vision with the aim to recover a
sharp image from a blurred input image. Advances in deep learning have led to significant …

In this article, we consider deep learning strategies in ultrasound systems, from the front end
to advanced applications. Our goal is to provide the reader with a broad understanding of …

Digital receivers are required to recover the transmitted symbols from their observed
channel output. In multiuser multiple-input multiple-output (MIMO) setups, where multiple …

For seismic random noise attenuation, deep learning has attracted much attention and
achieved promising performance. However, compared with conventional methods, the …

The use of photo-activated fluorescent molecules to create long sequences of low emitter-
density diffraction-limited images enables high-precision emitter localization, but at the cost …

Blind image deblurring (BID) has been extensively studied in computer vision and adjacent
fields. Modern methods for BID can be grouped into two categories: single-instance methods …

Image deblurring tries to eliminate degradation elements of an image causing blurriness
and improve the quality of an image for better texture and object visualization. Traditionally …

In this study, a general network model called multi-progressive image deblurring network is
proposed to correct blurring artifacts and local imaging details in underwater images. As a …

Visual perception is one of the key technologies for smart ships to achieve intelligent
navigation in various complex water scenes, which adopts the object detection algorithm …

In this paper, we propose a blueprint of a new deep network unfolding a baseline quantum
mechanics-based adaptive denoising scheme (De-QuIP). Relying on the theory of quantum …