Research on Image Super Resolution Reconstruction Based on Deep Learning

Abstract

To enhance the precision and clarity of graphic and image depictions, we propose a super-resolution image reconstruction method driven by the power of deep learning. This method initiates by obtaining the reconstruction object from graphics and images, subsequently simulating their degradation process. The preprocessing of initial images is accomplished via registration and expansion, setting a solid foundation for the subsequent stages. Deep learning algorithms are employed to interrogate and dissect the inherent features of the graphics and images. Subsequently, a lineup of techniques including feature fusion and bilinear interpolation are deployed to gain super-resolution reconstruction results of the graphics and images. Upon examining and juxtaposing our deep learning-based method with conventional techniques, we discerned a noticeable advantage of the former. Intriguingly, the resolution deviation within the image reconstruction results derived via our idealized strategy has been remarkably minimized. Concurrently, peak signal-to-noise ratio and structural similarity attributes have been substantially augmented. This unique confluence of improvements as embodied in our approach places it squarely as a potential game-changer in the domain of super-resolution image reconstruction.