Underwater Image Enhancement Based on the Improved Algorithm of Dark Channel

Abstract

Enhancing underwater images presents a challenging problem owing to the influence of ocean currents, the refraction, absorption and scattering of light by suspended particles, and the weak illumination intensity. Recently, different methods have relied on the underwater image formation model and deep learning techniques to restore underwater images. However, they tend to degrade the underwater images, interfere with background clutter and miss the boundary details of blue regions. An improved image fusion and enhancement algorithm based on a prior dark channel is proposed in this paper based on graph theory. Image edge feature sharpening, and dark detail enhancement by homomorphism filtering in CIELab colour space are realized. In the RGB colour space, the multi-scale retinal with colour restoration (MSRCR) algorithm is used to improve colour deviation and enhance colour saturation. The contrast-limited adaptive histogram equalization (CLAHE) algorithm defogs and enhances image contrast. Finally, according to the dark channel images of the three processing results, the final enhanced image is obtained by the linear fusion of multiple images and channels. Experimental results demonstrate the effectiveness and practicality of the proposed method on various data sets.

Underwater Image Enhancement via Higher-Order Moment CLAHE Model and V Channel Substitute

“…Image restoration methods solve the parameters of the image model using priors to restore well-visible images [13]- [17]. A representative method is the dark channel prior (DCP) theory proposed by He et al [15] which was originally used for haze removal but has been adapted by many researchers for underwater image processing.…”

“…As a huge part of the Earth, the ocean still has many unknown and unexplored fields for humanity. Driven by curiosity and longing for rich resources, it becomes an important way to know more about underwater world through imaging systems [1], technologies linked to underwater exploration and resource development have consistently commanded substantial attention [2] [3]. Throughout the ages, within exploration in this field, images have consistently been one of essential instruments of cognition.…”

“…Similarly, a single-color correction method cannot improve contrast and detail of images. To address these problems simultaneously, a variety of approaches have been presented in the last decade [6]- [11], [13]- [17], [20]- [23], which can be broadly categorized into three types: image enhancement methods, image restoration methods, and deep learning methods.…”

Underwater polarization image fusion based on NSCT

“…Finally, we make a comprehensive comparison from six evaluation metrics: information entropy, 23 enhancement measure evaluation (EME), 24 contrast, 25 average gradient, 26 standard deviation, 27 and running time. These six indexes reflect the information richness, detail performance, overall performance, sharpness of the image, image information distribution, and algorithm complexity.…”

Enhancement and Optimization of Underwater Images and Videos Mapping

“…Existing underwater restoration methods are mostly based on the image formation model (IFM) [ 6 , 7 , 8 ], which describes the linear relationship among the direct component, the forward scattering component, and the backward scattering component. In this model, two crucial parameters, the background lights and the transmission map, were estimated to recover underwater haze-free images.…”