Underwater image enhancement based on turbulence model corrected by transmittance and dynamically adjusted retinex

Abstract

Objective: The attenuation of light is typically serious even in the purest water after filtration. Experiments show that the attenuation of water is caused by two unrelated physical processes, namely, absorption and scattering. Water has obvious selectivity for light absorption, and the absorption abilities of different spectral regions vary. Such variation leads to the loss of light energy, which makes underwater imaging difficult with such phenomena as low definition and color distortion. The scattering of light by water can be divided into forward and backward scattering. The suspended particles in water cause the attenuation of light in the forward and reverse directions of propagation, which limits the detection range and distance of underwater optical imaging. This limitation results in a decrease in image contrast, blurred details, and degradation. A series of degradation problems has weakened the detection rate and accuracy of defects. Therefore, the denoising and enhancement of images with surface defects of underwater structures are significant. An improved image enhancement algorithm based on turbulence model and multiscale retinex (MSR) is proposed to improve the contrast and sharpness of surface defect images acquired underwater and facilitate the subsequent segmentation, extraction, and recognition of the defect region. The proposed method combines physical model with the nonphysical model method. Thus, this method improves the considerably simple model parameters and poor versatility. The color cast of the enhanced image is considered, and the image noise is suppressed. Method: An underwater image with uneven illumination is converted from RGB space to Lab space, and histogram equalization (HE) is performed on the luminance space. After the incident light is reflected from the structure surface, it will be affected by the suspended particles from the water before it reaches the imaging device, and the scattering phenomenon will generate noise. The absorption of the spectrum by the water will attenuate the light intensity, and this condition will result in low image contrast. The process is similar to the imaging model of a foggy-degraded image. Thus, the reconstructed method of the degraded image can be used to process the blurred underwater image. The light intensity distribution in the imaged scene after homogenization is similar, which can be approximated as a fixed value. The transmittance of the homogenized image can be estimated in accordance with dark channel prior theory. It describes the medium transmission that is unscattered and reaches the light portion of the imaging system, and it is also the degree of blur at each pixel. It reflects the degree of transparency of the light source components in the scene, which indicates the extent to which the image is affected by the scattering of water. In consideration of the same degraded underwater images and remote sensing images, the two exhibit similarities in optical properties, fluid media, and external forms. The atmospheric turbulence model can accordingly be applied to simulate the degradation process of underwater images. The transmittance obtained above is combined with a general model of atmospheric turbulence to simulate an underwater degradation image by adjusting the transmittance coefficient. The image noise is filtered by Wiener filter, and the filtered image is used as a guide image. An edge-preserved image is obtained using a guide filter to refine. Image enhancement is performed in accordance with retinex theory. The MSR result is color-corrected on the basis of the 3σ criterion to obtain an enhanced underwater image. Multiple images collected under different turbulent environments are selected as the research object. The method proposed in this study is used and compared with classic methods, such as the dark channel, HE, and single-scale retinex (SSR) algorithms. The indicators of signal-to-noise ratio (SNR), peak SNR, information entropy (IE), standard deviation (SD), and average gradient (AG) are evaluated. Result: Experimental results show that the IE and SD of the proposed algorithm are 11.7% and 25.6% higher than those of HE algorithm and SSR, respectively. The AG is 31.2% higher than that of HE algorithm, and the segmentation accuracy is increased by 3.1%. The oversegmentation rate is the lowest among similar algorithms. From the perspective of subjective visual effects, the image after restoration by the dark channel algorithm is rich in color and prominent in detail, but its gray value is mostly distributed in the low-gray area. Such distribution results in a low image recognition rate and an unsatisfactory segmentation effect. The visual effect of HE algorithm is the closest to the original image, but the problem that the gray scale distribution is excessively concentrated remains. The segmentation accuracy is slightly improved compared with that of the dark channel algorithm. The image after SSR shows a color cast phenomenon, and the overall gray value of the image after the dynamically adjusted MSR decreases. The range of gray scale is enlarged, and the color cast effect is improved. The color and detail of the image are the richest, and the visual effect is the most natural among all the tested algorithms. Conclusion: On the basis of the analysis of an underwater image degradation model and the difference among pixels, the image transmittance is estimated, and the gray scale distribution of the enhanced image is expanded by the 3σ criterion. An enhanced underwater image with high contrast, high definition, and balanced color is obtained. The algorithm improves the adaptive problem of the degenerate model with excellent performance in comprehensive indicators, such as IE, SD, and AG. Compared with the dark channel prior method, the proposed method exhibits greatly improved SNR and AG. The edge information of the image is preserved, which provides a good information source for image segmentation and recognition in the next stage.