Although many post-processing solutions were proposed, they are not applicable to continuous vehicle operations. Vehicles operating in a water medium sometimes encounter harsh conditions with high turbidity and low scene illumination, making it challenging to obtain reliable target information through optical devices. 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. 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. 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. It combines three main steps: homomorphic filtering, empirical modal feature map extraction, and dual-image wavelet fusion. 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. 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.

Besides, the proposed method is also suitable for dehazing regular fogged images and local feature points matching.

Our experimental results demonstrate the superior performance of the proposed method in both subjective and objective evaluations. Last, ‘exposedness’ and ‘contrast’ are set as two weights, being blended into the famous multi-scale fusion framework to generate the enhanced result. Then, gamma adjustment operation is employed to generate artificial over−/under-exposure visions of color-balancing image. Third, a novel algorithm based on two non-reference quantitative evaluation indicators is utilized to choose the optimal color-balancing version. Second, three kinds of color-balanced images are generated from the operation, which combines color compensation algorithms and famous Gray-World assumption. First, an adaptive reduction operation on the two stronger color-channels of inputs is employed to avoid the red over-compensated deficiency appearing in color-balanced result.

To address the issue, this paper introduces an effective and robust underwater image enhancement method without any dedicated hardware or prior knowledge. Current dehazing methods could not improve the visual quality of underwater images/frames robustly and effectively, especially in removing the undesired color cast. Underwater images/frames are always subjected to color distortion, contrast reduction and detail loss, which degrade the visual quality severely.

Red colorcast underwater images