With the rapid development of artificial intelligence, image processing technology has been more and more widely used. Image enhancement is an important part of image processing, and has become a research hotspot of theory and application of image processing technology. This article proposes a new method for underwater image enhancement to solve the problems of color distortion, low contrast and blurring in underwater images. The compensation factor is used to compensate the badly damaged color channels, and the compensation factor is constructed by the mean differences between the damaged color channels and the well-preserved color channel. Then, multi-scale convolution MSRCR technology is used to denoising and correct color distortion, in conclusion, CLAHS and global contrast stretching are used to improve the local and global contrast of the images. Qualitative and quantitative evaluations prove that the proposed method can solve the color cast effect and improve the contrast of underwater images. The images processed by our method have natural color, high contrast and high clarity. Similarly, our method can also achieve good results in underwater low light and underwater images captured by different camera scenes.

随着人工智能的飞速发展，图像处理技术得到了越来越广泛的应用。图像增强是图像处理的重要组成部分，已经成为图像处理技术的理论和应用研究的热点。本文提出了一种新的水下图像增强方法，以解决水下图像色彩失真，对比度低和模糊的问题。补偿因子用于补偿严重损坏的颜色通道，补偿因子由损坏的颜色通道与保存良好的颜色通道之间的平均差构成。然后，将多尺度卷积MSRCR技术用于去噪和校正颜色失真，总而言之，CLAHS和全局对比度拉伸用于改善图像的局部和全局对比度。定性和定量评估证明该方法可以解决偏色效果，提高水下图像的对比度。通过我们的方法处理的图像具有自然色彩，高对比度和高清晰度。同样，我们的方法在水下微光和由不同相机场景捕获的水下图像中也能取得良好的效果。