Integrating deep learning and traditional image enhancement techniques for underwater image enhancement

Abstract

Underwater images usually suffer from colour distortion, blur, and low contrast, which hinder the subsequent processing of underwater information. To address these problems, this paper proposes a novel approach for single underwater images enhancement by integrating data‐driven deep learning and hand‐crafted image enhancement techniques. First, a statistical analysis is made on the average deviation of each channel of input underwater images to that of its corresponding ground truths, and it is found that both the red channel and the green channel of an underwater image contribute to its colour distortion. Concretely, the red channel of an underwater image is usually seriously attenuated, and the green channel is usually over strengthened. Motivated by such an observation, an attention mechanism guided residual module for underwater image colour correction is proposed, where the colour of the red channel of the underwater image and that of the green channel is compensated in a different way, respectively. Coupled with an attention mechanism, the residual module can adaptively extract and integrate the most discriminative features for colour correction. For scene contrast enhancement and scene deblurring, the traditional image enhancement techniques such as CLAHE (contrast limited adaptive histogram equalization) and Gamma correction are coupled with a multi‐scale convolutional neural network (MSCNN), where CLAHE and Gamma correction are used as complement to deal with the complex and changeable underwater imaging environment. Experiments on synthetic and real underwater images demonstrate that the proposed method performs favourably against the state‐of‐the‐art underwater image enhancement methods.

Color subspace exploring for natural image matting

“…Assefa [28] proposed an over‐exposure image correction method based on the observation that the RGB components are often not over‐exposed at the same position in an image. Shi [29] proposed an underwater image enhancement method based on the statistical analysis that the red channel of an underwater image is usually seriously attenuated, and the green channel is usually over strengthened.…”

Underwater Image Enhancement Based on Hybrid Enhanced Generative Adversarial Network

“…Xue et al jointly predicted underwater degradation factors based on a multi-branch multivariate network to achieve simultaneous image colour correction and contrast enhancement, compensating for image colour and removing the veil [14]. Shi et al proposed an attention mechanism residual module for colour correction based on the a priori information that both the underwater red and green channels cause colour distortion, with a combination of CLAHE and gamma algorithms to enhance the image [15].…”

A novel gamut expansion method based on combined global–local mapping for sRGB‐to‐ProPhoto conversion

“…Recently, deep learning (DL) methods have demonstrated superior performance in many compute vision tasks [4], such as image enhancement [5,6], image restoration [7,8], and image super-resolution [9,10]. The main purpose of image restoration is to restore the degraded images to their original version and make the image more convenient for observation or further analysis and processing.…”