Underwater Image Enhancement Method Based on Dynamic Heterogeneous Feature Fusion Neural Network

In recent years, signal and image processing based on fractional calculus has attracted extensive attention. Aiming at the serious problem of gray-scale loss in the existing pseudo color methods in high gray-scale image enhancement, a pseudo color enhancement algorithm suitable for Dynamic heterogeneous feature fusion neural network is proposed, and the traditional jet, HSV and rainbow coding are improved. Firstly, bit depth quantization is performed on the high-level gray image; Secondly, color enhancement is realized by using the constructed high gray-scale enhancement algorithm; Then, combined with the convolution neural network, the compact learning method is used to extract the features of the multi-scale image, and the jump connection is used to prevent the gradient dispersion and overcome the fog blur effect of the underwater image The style cost function is used to learn the correlation between various channels of color image, improve the color correction ability of the model, and overcome the problem of color distortion of underwater image. Experimental results show that compared with traditional image enhancement methods, the proposed method has better comprehensive performance in subjective vision and objective indicators, and has advantages in dealing with underwater image enhancement. While improving the brightness of the image, the problem of color distortion and brightness blocking of the enhanced image is solved. The texture information of the image is effectively restored. The brightness distribution of the enhanced image can well restore the brightness distribution of the real shooting environment, which verifies that the algorithm has higher robustness.