A typhoon optimization algorithm and difference of CNN integrated bi-level network for unsupervised underwater image enhancement

Underwater image processing presents a greater challenge compared to its land-based counterpart due to inherent issues such as pervasive color distortion, diminished saturation, contrast degradation, and blurred content. Existing methods rooted in general image theory and models of image formation often fall short in delivering satisfactory results, as they typically consider only common factors and make assumptions that do not hold in complex underwater environments. Furthermore, the scarcity of extensive real-world datasets for underwater image enhancement (UIE) covering diverse scenes hinders progress in this field. To address these limitations, we propose an end-to-end unsupervised underwater image enhancement network, TOLPnet. It adopts a bi-level structure, utilizing the Typhoon Optimization (TO) algorithm at the upper level to optimize the super-parameters of the convolutional neural network (CNN) model. The lower level involves a Difference of CNN that employs trainable parameters for image input-output mapping. A novel energy-limited method is proposed for dehazing, and the Laplacian pyramid mechanism decomposes the image into high-frequency and low-frequency components for enhancement. The TO algorithm is leveraged to select enhancement strength and weight coefficients for loss functions. The cascaded CNN acts as a refining network. Experimental results on typical underwater image datasets demonstrate that our proposed method surpasses many state-of-the-art approaches.