Underwater image enhancement method based on background light statistical model and transmission map optimization

Abstract

The present invention relates to an underwater image enhancement method based on a background light statistical model and transmission map optimization. The method includes the following steps: Step S1. First, an artificially selected background light estimation data set (MABLs) is established, and then the features of the model are selected. parameters, and finally establish the background light estimation model of R channel and GB channel; step S2. Based on the a priori conclusion of the new UDCP underwater dark channel, the R channel transmission map (TM) is obtained, and the depth map is inferred, and finally the GB channel is estimated. TMs and image restoration; step S3. Adaptive stretching in the CIE‑Lab color model. The advantages are as follows: the method of the invention is suitable for underwater image enhancement in complex environment, improves the operation efficiency of image enhancement, improves the visibility of the image and obtains a higher amount of information. Compared with other underwater image enhancement and restoration methods, this method has better effectiveness and robustness.

Description

technical field [0001] The invention relates to the technical field of underwater image restoration and enhancement, in particular to an underwater image enhancement method based on a background light statistical model and transmission map optimization. Background technique [0002] Underwater activities such as underwater archaeology, underwater resource exploration, and underwater pipeline exploration are facing great challenges, so obtaining clear underwater images plays a key role in ocean exploration. However, due to the influence of the underwater environment and its physical mechanism, underwater image processing has gradually attracted people's attention in recent years. In the imaging process of underwater images, the propagation of light in water is affected by the underwater environment, and there are problems such as selective absorption and dispersion, resulting in blurred underwater images, color casts, and low visibility. The attenuation of underwater images ...

AI Technical Summary

Problems solved by technology

In 2010, Chao et al. directly used DCP to restore underwater images. This algorithm can only improve the contrast of some underwater images, but most of the restored images have color distortion.

[0005] The image restoration method based on the physical model cannot meet the application in the actual environment, and the required method is often real-time. However, most of the image restoration methods based on the physical model currently require a long processing time, which requires optimization and optimization. improve

However, the robustness and adaptive ability of the existing methods are insufficient, and the application of different types of degraded images cannot be adaptively adjusted, and the ideal underwater image enhancement method should be aimed at different underwater application scenarios and different types of images. Adaptive adjustment of degraded images should not be limited by application scenarios and external conditions, and has good robustness and adaptive capabilities

[0006] In summary, there is a need for an underwater image enhancement method that can not only improve the visual effect of the image, but also effectively improve the accuracy of image classification and image recognition after the enhanced image, but there is no report on this underwater image enhancement method.

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