Identifying concrete surface defects in underwater infrastructure is crucial to ensure safe and stable operation. At present, using ROV to gather concrete surface images is the most effective measure for underwater image detection. However, the concrete images obtained by ROV have some phenomena, such as uneven illumination, color imbalance, poor contrast, and weak edge information. In this study, the issue of underwater concrete photos with poor image quality in nonuniform lighting scenarios is investigated, and a method for underwater concrete image enhancement is suggested, which provides efficient data support for the detection and analysis of concrete surface defects in underwater infrastructure.

The local highlight issue caused by the fill light spots in underwater images is processed based on image repair. First, the image is thresholded, and the highlighted pixel area of the image is retained. Second, using the hough circle detection method, the annular fill light spot area on the image is retrieved. The resulting annular fill light spot image is then used as the Mask image of the original image for restoration. Finally, the local highlighted area caused by the fill light spot is repaired by filling the adjacent pixel area. An improved dark channel prior (DCP) technique is used to enhance the image to address the issue of poor image quality brought by the undersea environment's uneven illumination. However, the single window size will have the following three problems: (1) A small size window area will lead to the oversaturation of local areas in the enhanced image. (2) A large window size can better eliminate the haze of the image but may produce halos. (3) A single-sized window is difficult to adapt to different pixel size images. Therefore, the selection area of the dark channel window size is expanded upon in this work using the contrast perception method. Image enhancement is done in each local window region by computing the contrast of seven neighborhood windows of one pixel and choosing the relevant dark channel window size following the contrast of the windows.

To confirm the effectiveness of the algorithm, the image enhancement method suggested in the manuscript was compared with low-light underwater images using local contrast and multiscale fusion (L²UWE), relative global histogram stretching (GRHS), underwater light attenuation prior (ULAP), image blurriness and light absorption (IBLA), dark channel prior (DCP), and contrast limited adaptive histogram equalization (CLAHE). Simultaneously, four metrics, natural image quality evaluator (NIQE), perception-based image quality evaluator (PIQE), blind/reference less image spatial quality evaluator (BRISQUE), and underwater color image quality evaluation (UCIQE), were used to assess the enhanced images. Experiment results showed that the PIQE, BRISQUE, and UCIQE of the proposed method obtained scores of 25.75±3.93, 29.39±1.80, and 1.04±0.01, respectively, which performed best.

The proposed image enhancement method achieves balanced enhancement of images in terms of color, contrast, and brightness, and the algorithm in this paper can successfully enhance the quality of underwater images. Our study will aid in measuring underwater concrete fault levels.