Numerical computation of ocean HABs image enhancement based on empirical mode decomposition and wavelet fusion - Wu, Geng-Kun; Zhang, Bei-Ping; Xu, Jie (Applied intelligence (Dordrecht, Netherlands), 2023)
Most of the microscopic images of Harmful Algae Blooms (HABs) acquired in marine engineering are subject to blurred cell textures and poor overall clarity due to the effects of seawater impurities, unknown suspended particle deposits and high-speed cell motions. To solve the above problems, a microscopic image enhancement method based on the recursive-overlapped contrast limited adaptive histogram specification (CLAHS) and dual-image wavelet fusion (RO-CLAHS and DIWF) is proposed in this paper. It combines three main steps: homomorphic filtering, empirical modal feature map extraction, and dual-image wavelet fusion. This method firstly adopts homomorphic filtering to strengthen the illumination uniformity of the entire HABs image, and then obtains an empirical modal feature map of algal cells by an improved empirical modal decomposition method, which highlights the detailed features of algal cells due to histogram stretching, clip limit and grey-level mapping. Finally, this paper proposes a dual-image wavelet fusion method adapted for HAB microscopic images, which integrates the images processed by contrast limited adaptive histogram equalization (CLAHE) with an empirical modal feature map and then achieves the image enhancement effect. The comparison experimental results show that the enhanced microscopic images of HABs by this method presents the best texture clarity and global contrast and improves the accuracy of algae image edge detection significantly.