Remote Sensing Single-Image Super-Resolution Using Convolutional Block Attention Residual Network With Joint Adversarial Mechanisms

As super-resolution techniques continue to evolve, there is a growing requirement for more advanced methods to capture finer details, particularly when dealing with the smaller pixels within an image. In remote sensing, enhanced spatial details can find utility in diverse applications, such as disaster management, urban planning, and environmental change detection. Many existing image super-resolution algorithms are there to improve image resolution. However, they are not explicitly crafted to accommodate the distinctive attributes of remote-sensing images, rendering them less effective in restoring the details of the images. Therefore, we proposed a convolutional block attention residual network with joint adversarial mechanisms (CRNJAM) to capture finer details in remote sensing images. We first designed a generator based on the residual network and attention mechanism. This has the ability to produce high-quality images with superior resolution, even when the input is of low quality. Then, we train the super-resolved images with high-resolution images with the help of two types of discriminators to generate more realistic images. The first discriminator evaluates an input sample's local regions or patches. On the other hand, the second discriminator evaluates the entire input sample as a whole. The result shows that the proposed model can significantly reduce the noise in the generated super-resolved image; also, the SR image generated using the proposed method provides competitive advantages over the images generated using other models.