Super-resolution reconstruction of side-scan sonar target images based on improved SRCNN

As ocean exploration deepens, efficient and accurate identification of underwater targets becomes particularly crucial. However, traditional methods and current technologies face challenges such as scarce samples and complex imaging conditions when processing side-scan sonar images. Given the current state of limited sample augmentation methods and low image resolution for side-scan sonar, this paper improves upon the SRCNN method by integrating the CBAM attention mechanism and Perceptual loss function. This approach mitigates the issue of increased noise typically associated with conventional image super-resolution reconstruction, thereby enhancing the accuracy of the side-scan sonar target detection model. Consequently, this method has been proven to enhance the quality of super-resolution reconstruction of side-scan sonar target images, offering a new approach to improving the construction of underwater target detection models.