Underwater Image Enhancement Based on Dense Cascaded Convolutional Neural Network

To solve the low contrast problem of underwater degraded images, an underwater image enhancement algorithm based on a deep cascaded convolutional neural network is proposed. First, the degraded underwater image is converted from traditional red, green, and blue to hue, saturation, and value color space, which retains the hue and lightness component without changes, and the cascaded convolutional neural network is employed to examine the saturation component improvement. New dense blocks are introduced in the process of feature extraction network encoding and decoding. The dense block combines residual connection, skip connection, and multiscale convolution to correct color distortion. The texture refinement network employs six texture refinement units to extract feature information from the refined image. Finally, the S-channel image is extracted using the cascaded convolutional neural network, which is combined with the H- and V-channel images to achieve an improved underwater image. The experimental findings reveal that the average underwater color image quality estimation of underwater images improved using the proposed algorithm can reach 0.616875, and the average underwater image quality measurement can reach 5. 197000. The comparison algorithm findings reveal that the proposed underwater image enhancement algorithm not only has a good improvement effect but also ensures the improved images are in line with human vision.