Learning-based underwater image enhancement with adaptive color mapping

Abstract

Blurring and color cast are two of the most challenging problems for underwater imaging. The poor quality hinders the automatic segmentation or analysis of images. In this paper, we describe an image enhancement method to reduce the blurring and color cast of the underwater medium. It is a two-folded approach; First, a color correction algorithm is applied to correct the color cast and produce a natural appearance of the sub-sea images. Second, a pair of learned dictionaries based on sparse representation are applied to sharpen the image and enhance the details. Our strategy is a single image approach that does not require additional knowledge of environment such as depth, distance object/camera or water quality. The experimental results show that the proposed method can efficiently enhance almost every underwater image; And offers a quality that is typically sufficient for the high level computer vision algorithms

Systematic review and analysis on underwater image enhancement methods, datasets, and evaluation metrics

“…Farhadifard et al 128 introduced an effective UIE method that does not rely on prior information. The color cast was corrected using a color correction approach based on a guided color mapping scheme.…”

HybrUR: A Hybrid Physical-Neural Solution for Unsupervised Underwater Image Restoration

“…On the basis of traditional methods, researchers introduced deep learning method to directly study an end-toend enhancement network. DCNN is verified to work as a generic image converter with proper loss functions and be widely used in image quality enhancement framework [8], [15], [16]. Wang et al proposed a UIE-Net to address the issue of color correction and dehazing by two individual modular networks [18].…”

“…The learning approach conforming to an optical law has been investigated in recent years. Most of them are concentrated on supervised training, such as WaterGAN and UW-GAN [15], [17]. These methods separately trained the optical feature maps including attenuation layer, scattering layer, and camera halation via decoupling the raw image.…”

“…Factors f D and f B represent the dependencies of the coefficients β D c and β B c on scene depth z, reflectance, spectrum of ambient light, physical scattering and beam attenuation coefficients. Note that some previous literature assumed that β D = β B to simplify the parameters optimization [15], [18]. Instead, we consider keeping the two coefficients independent to distinguish their correlations and describe the degradation in detail [12], [32].…”

SE–RWNN: an synergistic evolution and randomly wired neural network‐based model for adaptive underwater image enhancement

“…These techniques have demonstrated their effectiveness in improving medical images, satellite images, aerial images, and even real‐life photographs that suffer from poor contrast and noise. As for the enhancement of underwater images, several model‐based approaches were developed based on transfer function [9], dictionary learning [10], depth estimation [11], and wavelength compensation [12]. However, due to the turbid nature of water, the multiple scattering is inevitable [11].…”