The attenuation (sum of absorption and scattering), which is caused by the dense and non-uniform medium, generally leads to problems of color degradation and detail loss in underwater imaging. In this study, we describe an underwater image enhancement method based on adaptive attenuation-curve prior. This method uses color channel transfer (CCT) to preprocess the underwater images, light smoothing, and wavelength-dependent attenuation to estimate water light and obtain the attenuation ratio between color channels, and estimates and refines the initial relative transmission of the channel. Additionally, the method calculates the attenuation factor and saturation constraints of the three color channels and generates an adjusted reverse saturation map (ARSM) to address uneven light intensity, after which the image is restored through water light and transmission estimation. Furthermore, we applied white balance fusion globally guided image filtering (G-GIF) technology to achieve color enhancement and edge detail preservation in the underwater images. Comparison experiments showed that the proposed method obtained better color and de-hazing effects, as well as clearer edge details, relative to current methods.
Underwater optical imaging technology plays a vital role in humans’ underwater activities. However, the serious quality degradation of underwater optical images hinders further development of such technology. This phenomenon is mainly caused by the absorption and scattering of light in the underwater medium. The blurred image formation model is widely used in the field of optical images and depends on two optical parameters: background light (BL) and the transmission map (TM). Therefore, we propose an underwater optical image enhancement method in the context of underwater optical image restoration and color correction. First, BL estimation based on the gray close operation, which can avoid the influence of white objects while accurately calculating BL, is proposed. Then, an improved adaptive transmission fusion (IATF) method is proposed, and the adjusted reversed saturation map (ARSM) method is applied to compensate for and refine the estimated TMs to obtain the final TMs. This paper also proposes a new underwater light attenuation prior (NULAP) method. Finally, to enhance color saturation and edge details, a statistical colorless slant correction fusion smoothing filter method is proposed. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods for dehazing, color and detail enhancement, and (uneven) light intensity.
Underwater optical images often have serious quality degradations and distortions, which hinders the development of underwater optics and vision systems. Currently, there are two mainstream solutions: non-learning based and learning-based. Both have their advantages and disadvantages. To fully integrate the advantages of both, we propose an enhancement method based on superresolution convolutional neural network (SRCNN) and perceptual fusion. First, we introduce a weighted fusion BL estimation model with a saturation correction factor (SCF-BLs fusion), the accuracy of image prior information is improved effectively. Next, a refined underwater dark channel prior (RUDCP) is proposed, which combines guided filtering and an adaptive reverse saturation map (ARSM) to restore the image, which not only preserves edge details but also avoids the interference of artificial light. Then, the SRCNN fusion adaptive contrast enhancement is proposed to enhance the colour and contrast. Finally, to further enhance image quality, we employ efficient perceptual fusion to blend the different resulting outputs. Extensive experiments demonstrate that our method has outstanding visual results in underwater optical image dehazing, color enhancement and is artefact- and halo-free.
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