Abstract
Due to refraction, absorption, and scattering of light by suspended particles in water, underwater images are characterized by low contrast, blurred details, and color distortion. In this paper, a fusion algorithm to restore and enhance underwater images is proposed. It consists of a color restoration module, an end-to-end defogging module and a brightness equalization module. In the color restoration module, a color balance algorithm based on CIE Lab color model is proposed to alleviate the effect of color deviation in underwater images. In the end-to-end defogging module, one end is the input image and the other end is the output image. A CNN network is proposed to connect these two ends and to improve the contrast of the underwater images. In the CNN network, a sub-network is used to reduce the depth of the network that needs to be designed to obtain the same features. Several depth separable convolutions are used to reduce the amount of calculation parameters required during network training. The basic attention module is introduced to highlight some important areas in the image. In order to improve the defogging network’s ability to extract overall information, a cross-layer connection and pooling pyramid module are added. In the brightness equalization module, a contrast limited adaptive histogram equalization method is used to coordinate the overall brightness. The proposed fusion algorithm for underwater image restoration and enhancement is verified by experiments and comparison with previous deep learning models and traditional methods. Comparison results show that the color correction and detail enhancement by the proposed method are superior.
Highlights
IntroductionInitial applications are mainly focused on the estimation of the transmission in images, for example in [16,17,18,19]
The rest of the paper is organized as: Section 2 gives the description of underwater imaging model and the color model used in the study; Section 3 illustrates the methods employed in the proposed fusion algorithm and the training dataset; Section 4 presents the image processing results and corresponding explanation; and the conclusion is given in the final section
A hybrid underwater image enhancement and restoration algorithm is proposed that is composed of a color balance algorithm, an end-to-end defogging network algorithm, and a Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm
Summary
Initial applications are mainly focused on the estimation of the transmission in images, for example in [16,17,18,19] These CNN based models are trained with synthetic data set to regress transmission and obtain more refined restored images than conventional methods. Further applications concern both the transmission and the ambient light, for example in [20,21,22]. It includes a color balance algorithm based on the CIE Lab color model, an end-to-end CNN defogging algorithm based on foggy training set and a brightness equalization module In this way, the mapping relationship between the underwater blurred image and the clear image can be obtained without synthesizing the data set of the underwater image. The rest of the paper is organized as: Section 2 gives the description of underwater imaging model and the color model used in the study; Section 3 illustrates the methods employed in the proposed fusion algorithm and the training dataset; Section 4 presents the image processing results and corresponding explanation; and the conclusion is given in the final section
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