This work proposes to develop an underwater image enhancement method based on histogram-equalization (HE) approximation using physics-based dichromatic modeling (PDM). Images captured underwater usually suffer from low contrast and color distortions due to light scattering and attenuation. The PDM describes the image formation process, which can be used to restore nature-degraded images, such as underwater images. However, it does not assure that the restored images have good contrast. Thus, we propose approximating the conventional HE based on the PDM to recover the color distortions of underwater images and enhance their contrast through convex optimization. Experimental results demonstrate the proposed method performs favorably against state-of-the-art underwater image restoration approaches.

This study presents a novel method based on entropy‐driven gradient evaluation ( P‐EdGE ) for detecting perceptual edges that represent boundaries of objects as perceived by human eyes. P‐EdGE is characterised by iteratively employing a shape‐changeable mask centred at a target pixel to sample gradient orientations of neighbouring pixels for measuring the directivity of the target pixel. The mask deforms to automatically cover pixels most suitable for exhibiting the directivity of the target pixel. The authors show that such an iterative scheme satisfies the similarity and proximity laws in Gestalt theory. The converged directivity in conjunction with the gradient magnitude are subjected to a Bayesian process, they show that doing so can help effectively determine whether the target pixel belongs to a perceptual edge. Experimental results are presented to justify the superiority of P‐EdGE over other methods in detecting perceptual edges.

Recently, an upsurge of deep learning has provided a new direction for the field of computer vision and visual tracking. However, expensive offline training time and the large number of images required by deep learning have greatly hindered progress. This paper aims to further improve the computational performance of CNT which is reported to deliver 5 fps performance in visual tracking, we propose a method called Fast-CNT which differs from CNT in three aspects: firstly, an adaptive k value (rather than a constant 100) is determined for an input video; secondly, background filters used in CNT are omitted in this work to save computation time without affecting performance; thirdly, SURF feature points are used in conjunction with the particle filter to address the drift problem in CNT. Extensive experimental results on land and undersea video sequences show that Fast-CNT outperforms CNT by 2~10 times in terms of computational efficiency.