Low noise super-resolution reconstruction algorithm based on network integration

Due to the challenges associated with traditional methods in reconstructing complex water images, such as low resolution, absence of key information, and significant noise, this paper presents a network integration-based algorithm for low noise super-resolution reconstruction. In order to make the reconstructed image texture clear, the implicit neural expression of the image is applied in the traditional SRGAN algorithm. We also utilize the concept of network integration to effectively capture both the surface-level and in-depth information from the image. Experimental results indicate that the algorithm we propose outperforms the current mainstream algorithms in terms of both subjective visual effects and objective quality evaluation indicators for reconstructed images.