All of the above issues will bring great difficulties to underwater image analysis through computer vision . In particular, in low-light underwater scenes, illumination depends only on artificial light sources, which will cause problems of low brightness and uneven illumination. ![]() These technologies have obvious disadvantages of high cost, low flexibility, and inability to process images that have already been collected. In the early stage, relevant researchers usually used polarization imaging , artificial light sources, distance gating, and other hardware devices to improve the original quality of the collected underwater images in engineering applications. In addition, natural light will completely disappear in deep-sea areas after reaching a certain depth. Therefore, underwater images generally appear blue and green color . In particular, the transmittance of blue–green light is the largest, while the attenuation of red light is the most serious. The intensity of absorption is closely related to the light wavelength. Therefore, obtaining high-quality underwater images is of great importance for marine resource exploration, marine environment monitoring, terrain surveying, cruise guidance, marine military, and national defense construction. Underwater images often exhibit problems of low contrast caused by backscattering, detail blurring by forward scattering, and color attenuation by light absorption. The approach can achieve better performance of color restoration, blur removal, and low illumination enhancement. Experiments demonstrate that the proposed method significantly outperforms several state-of-the-arts in both qualitative and quantitative qualities. Since the underwater image after color restoration still suffers from scattering and blurring, an effective method based on dual image wavelet fusion (DIWF) and Generative Adversarial Network (GAN) is designed to further enhance the edge details and improve the contrast of the color restored image. Color restoration is further implemented to estimate the illuminant color cast caused by the selective attenuation of light. ![]() First, an adaptive color compensation method is proposed to make up for the loss of severely attenuated channels. To address these issues, this paper presents a two-step strategy based on color restoration and image fusion by combining deep learning and conventional image enhancement technologies to improve the visual performance of underwater images. Due to the severe light absorption and scattering, underwater images often exhibit problems such as low contrast, detail blurring, color attenuation, and low illumination.
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