An Underwater Image Enhancement Algorithm Based on MSR Parameter Optimization

Abstract

The quality of underwater images is often affected by the absorption of light and the scattering and diffusion of floating objects. Therefore, underwater image enhancement algorithms have been widely studied. In this area, algorithms based on Multi-Scale Retinex (MSR) represent an important research direction. Although the visual quality of underwater images can be improved to some extent, the enhancement effect is not good due to the fact that the parameters of these algorithms cannot adapt to different underwater environments. To solve this problem, based on classical MSR, we propose an underwater image enhancement optimization (MSR-PO) algorithm which uses the non-reference image quality assessment (NR-IQA) index as the optimization index. First of all, in a large number of experiments, we choose the Natural Image Quality Evaluator (NIQE) as the NR-IQA index and determine the appropriate parameters in MSR as the optimization object. Then, we use the Gravitational Search Algorithm (GSA) to optimize the underwater image enhancement algorithm based on MSR and the NIQE index. The experimental results show that this algorithm has an excellent adaptive ability to environmental changes.

Comparative Analysis of Image Enhancement Algorithms

“…Edwin Land composed the Retinex Theory. [10][11][12] The word 'Retinex' was derived from two words -Retina + Cortex. This theory states that the way we perceive the colours of the objects around us results from the light incident around it and the wavelength of light that the object reflects, which enters our eyes.…”

“…• The image of each channel is given a linear stretch and the final RGB image is combined. [12] Multi-Scale Retinex (MSR)…”

“…The MSR output weighs the different scales of SSR and adds them together. [12] Multi-Scale Retinex with Colour Recovery (MSRCR)…”

“…The algorithms mentioned above lead to the dominance of a certain color from the RGB, leading to the formation of grayish images. Thus, the MSRCR algorithm comes with a color recovery step, which multiplies the output of MSR with a color recovery factor C. [12] PArAmeters cAlculAted i. Entropy: In the context of image processing, it can be defined as the lower limit for the average coding length in bits per pixel, realized by a coding scheme without loss of information. [12] (1) n = no.…”

Signals and Images in Sea Technologies

“…This is precisely the scope of the paper "An Underwater Image Enhancement Algorithm Based on MSR Parameter Optimization" by Hu at al. [4]. Among the general-purpose image enhancement approaches, algorithms based on the so-called Multi-Scale Retinex (MSR) have achieved good results for overland images.…”