Coarse-to-fine underwater image enhancement with lightweight CNN and attention-based refinement

Graphical Abstract of the proposed network, Module 1 shows the Global-local network, Module 2 shows RGB histogram equalizer, and Module 3 shows the attention module. (b) The structure of B_Block, and (c) The structure of C_block in the proposed network. [Display omitted] •An end-to-end synergetic integration of classic image processing and CNNs.•Using an attention module to emphasize meaningful parts of underwater images.•Proper employing histogram equalization to avoid producing color artifacts.•Introducing a lightweight model, suitable for different types of underwater images. This paper presents a deep learning-based underwater image enhancement method supported by a classical image processing technique. The proposed method includes an end-to-end three-module structure. The first module is a lightweight two-branch network that retrieves lost colors and to some extent overall appearance through the global and local image enhancement. The second module is the modified histogram equalization to improve the global intensity, contrast and local colors of the image by controlling over-intensity and artificial colors that may result from histogram equalization. The last part is the attention module, utilized to help the proposed framework have a synergistic combination of the previous modules. The attention module is designed to combine the advantages of the previous modules and evade their drawbacks. Experiments to objectively and subjectively evaluate the performance of the proposed model show that the proposed model is superior to existing underwater image enhancement methods.