Convolutional neural networks (CNNs) are powerful machine learning models that have
become the state of the art in several problems in the areas of computer vision and image …

Underwater images are prone to suffer from color distortion and low visibility because of the
strong light attenuation. The traditional dark channel prior (DCP) method tends to fail when …

Images captured in dusty environments suffering from poor visibility and quality.
Enhancement of these images such as sand dust images plays a critical role in various …

Abstract Hybrid Unmanned Aerial Underwater Vehicle (HUAUV) is a class of multi-modal
mobile robots characterized by the ability to fly and navigate underwater, as well as being …

Modern imaging devices can capture faithful color and characteristics of natural and man-
made scenes. However, there exist conditions in which the light radiated by objects cannot …

The depth of field of the imaging device is limited, which makes it sometimes difficult to
present all of the different objects on the same image. In order to solve this problem, the …

When light propagates through water, there will be absorption and scattering. Due to this
reason, underwater images suffer from color distortion, contrast loss, and detail blurriness. In …

In response to the needs of field environment monitoring and pollution source search, a
trans-media multicopter is presented which combined with the two structural characteristics …

Overexposed and underexposed digital images may occur either by excess or deficiency of
lighting during acquisition. These problems are common in uncontrolled environments …

Convolutional Neural Networks became a state-of-the-art approach for many different
problems of computer vision, pattern recognition, and image processing. However, due to …