Application of Edge Detection for Enhancing Object Boundary Clarity in Satellite Image Super-Resolution

Recent advancements in satellite image super-resolution (SR) have been driven by neural network-based methods, emphasizing the reconstruction of high-frequency information such as object edges and texture details. These high-frequency elements are critical for enhancing image clarity and overall visual quality. Mainstream approaches typically involve deep neural network architectures and altering loss function formulations to improve high-frequency detail retention. However, as resolution magnification increases substantially (e.g., by a factor of five or more), reconstructing the high-frequency information of object edges becomes increasingly challenging, often resulting in edge blurring. To address this issue and to enhance edge definition, this study integrates an edge detection block into the SGNET network. It uses a Laplacian of Gaussian operator to detect initial SR image edges, process these through a streamlined convolutional network to reconstruct high-frequency edge information, and subsequently add these enhanced edges to the initial SR image, boosting edge contrast and clarity in the final output. This method effectively mitigates edge blurring, demonstrating significant improvements in visual metrics such as SSIM (Structural Similarity Index Measure) and LPIPS (Learned Perceptual Image Patch Similarity), which collectively enhance the overall visual quality of the SR images.