An analysis and method for contrast enhancement turbulence mitigation.

A common problem for imaging in the atmosphere is fog and atmospheric turbulence. Over the years, many researchers have provided insight into the physics of either the fog or turbulence but not both. Most recently, researchers have proposed methods to remove fog in images fast enough for real-time processing.

A no-reference perceptual based contrast enhancement metric for ocean scenes in fog.

In this paper, we develop a perceptually based contrast enhancement metric as a means to solve the problem of autonomously enhancing images degraded by fog that are perceptually pleasing to humans. A learning based approach is considered to develop the contrast enhancement using human observations and low-level contrast enhancement metrics based on the human vision system. In addition, we provide new low-level metrics based on the physics of the scene to improve the performance of existing contrast enhancement metrics.

An investigation of dehazing effects on image and video coding.

This paper makes an investigation of the dehazing effects on image and video coding for surveillance systems. The goal is to achieve good dehazed images and videos at the receiver while sustaining low bitrates (using compression) in the transmission pipeline. At first, this paper proposes a novel method for single-image dehazing, which is used for the investigation.

An augmented Lagrangian method for total variation video restoration.

This paper presents a fast algorithm for restoring video sequences. The proposed algorithm, as opposed to existing methods, does not consider video restoration as a sequence of image restoration problems. Rather, it treats a video sequence as a space-time volume and poses a space-time total variation regularization to enhance the smoothness of the solution.