Innovative underwater image enhancement algorithm: Combined application of adaptive white balance color compensation and pyramid image fusion to submarine algal microscopy

Abstract

Real-time collected microscopic images of harmful algal blooms (HABs) in coastal areas often suffer from significant color deviations and loss of fine cellular details. To address these issues, this paper proposes an innovative method for enhancing underwater marine algal microscopic images based on Adaptive White Balance Color Compensation (AWBCC) and Image Pyramid Fusion (IPF). Firstly, an effective Algorithm Adaptive Cyclic Channel Compensation (ACCC) is proposed based on the gray world assumption to enhance the color of underwater images. Then, the Maximum Color Channel Attention Guidance (MCCAG) method is employed to reduce color disturbance caused by ignoring light absorption. This paper introduces an Empirical Contrast Enhancement (ECH) module based on multi-scale IPF tailored for underwater microscopic images of algae, which is used for global contrast enhancement, texture detail enhancement, and noise control. Secondly, this paper proposes a network based on a diffusion probability model for edge detection in HABs, which simultaneously considers both high-order and low-order features extracted from images. This approach enriches the semantic information of the feature maps and enhances edge detection accuracy. This edge detection method achieves an ODS of 0.623 and an OIS of 0.683. Experimental evaluations demonstrate that our underwater algae microscopic image enhancement method amplifies local texture features while preserving the original image structure. This significantly improves the accuracy of edge detection and key point matching. Compared to several state-of-the-art underwater image enhancement methods, our approach achieves the highest values in contrast, average gradient, entropy, and Enhancement Measure Estimation (EME), and also delivers competitive results in terms of image noise control. .