Underwater Fused Image Classification Using Deep Learning Based Resnet and Hybrid PSO + HHO Model

Abstract

In image fusion, multiple images are combined into one with minimal distortion and data loss. Image fusion forms a highly configured image with good results. The image fusion technique is employed in various domains, including remote sensing, robotics, medical applications, and underwater image processing. The focus of this study is on a unique underwater image fusion approach that allows for greater flexibility in the construction of fusion criteria. The characteristics of the input images are extracted using a Modified Tetrolet Transform (MMT), which may be employed together or independently. Our aim is to apply image deep learning algorithms such as GoogleNet, AlexNet, and ResNet for fusion to the original image and optimized algorithm PSO and HHO to be used for optimizing the fused image. Finally, the images are classified as good quality images and poor-quality images. The ResNet with hybrid HHO and PSO method has high efficiency in image fusion, according to numerical data the proposed model with good quality attains accuracy, sensitivity, specificity, precision, and F1-measure are 96.32, 94.25, 95.73, 98.34, and 95.55. in addition, the poor-quality images attain accuracy, sensitivity, specificity, precision, and F1-measure are 96.22, 94.15, 95.63, 98.24, and 95.45 This method optimizes the exposure of the dark areas, increases contrast, and preserves and enhances the edges. Our image trial findings show that this technique substantially improves the underwater image quality.