Extracting clear crack images from an underwater dam image is the fundamental step in digital imaging application. These images are the crucial prerequisite for good understanding of real underwater dam scenes. However

complex underwater environmental conditions

such as uneven illumination distribution

low signal-to-noise ratio

and low contrast

make the task of extracting clear crack images more challenging than in other conditions. We propose a self-adaptive image enhancement algorithm based on improved dark channel prior for underwater dam crack images. Global illumination balance and noise suppression processes were conducted to deal with the uneven illumination problem while maintaining the original image textures. The underwater dam images with different illumination conditions correspond to different brightness balance layers. Accurate estimation of denoising image was processed on the basis of improved dark channel prior and guided filtering. The proposed algorithm improved the original dark channel prior method in terms of optical transmittance

which is determined by the absolute difference value of the global atmospheric light and the dark channel values. If the absolute difference value is greater than the threshold

then the image pixels are in the dark zone. Otherwise

the image pixels are in the bright zone. The optical transmittance formula varies according to the different illumination zones. A self-adaptive

parameter-calculating method of 3 introduction principle was used to compute the image tensile stretch points and enhance the image. A series of evaluation parameters

such as mean

variance

peak signal-to-noise ratio (PSNR)

contrast

and information entropy

were proposed to assess the image enhancement algorithms. The proposed algorithm is tested to enhance underwater dam crack images with different uneven illumination conditions. Two typical underwater dam crack images

namely

a medium-sized crack image and a small crack image

are selected as research objects. The enhancement effects of the proposed algorithm are compared with those of other enhancement algorithms such as histogram equalization

homomorphic filtering

multiscale retinex

and multiscale retinex with color restoration. To test the robustness of the proposed algorithm

salt-pepper noise and Gaussian noise are added to the original underwater dam crack images to evaluate the anti-interference ability of the proposed algorithm with specific distribution noise. The different sizes of salt-pepper noise and Gaussian noise are used in this experiment. The proposed algorithm has the best enhancement effects compared with the aforementioned enhancement algorithms. It has shown strong robustness against specific interferences such as salt-pepper noise and Gaussian noise. The PSNR levels of the proposed algorithm for the two original images are 42.77 and 41.49

which are higher than those of other image enhancement algorithms. The simulation experiment results show that the proposed algorithm can effectively suppress noise interference and enhance image quality of the underwater dam crack image. The proposed algorithm has preferable adaptive performance for underwater dam crack image with different brightness conditions. This improved algorithm is feasible and efficient for underwater dam crack image enhancement.