A Robust Methodology Design to Perform Single Image Super-Resolution using Hybrid Learning and Contrast Enhancement Principles

Single Image Super-Resolution (SISR) remains a pivotal challenge in computer vision, aiming to reconstruct high-resolution (HR) images from their lowresolution (LR) counterparts. The current models concentrate on deep convolutional feature learning or perceptual refinement based on adversarial networks, which tend to produce constrained generalization and edge diffusion during uncertain changes in texture. This paper proposes a new hybrid learning model combined with dual-stage contrast-enhancement plan in order to accelerate the development of the structural fidelity and perceptual quality of the reconstructed images significantly. Based on the BSD100 dataset, the proposed approach starts with a bicubic down sampling and contrast preconditioning with CLAHE and nonlinear sigmoid-logarithmic fusion. The hybrid deep architecture consists of shallow CNNs, Residual Dense Blocks (RDB), bi-directional GRU, and attention gate units (AGUs) that are expected to extract spatial, sequential, and salient features. Moreover, a Contrast-Aware Residual Refinement (CARR) module is added to improve edge sharpness after reconstruction in terms of gradient-domain learning. The model trained on a composite loss comprising of MSE, SSIM, and perceptual loss performs better than baseline models such as EDSR, SwinIR, and RCAN. On 2x super-resolution tasks, empirical assessment provides PSNR of 33.10, SSIM of 0.901, LPIPS of 0.144 and NIQE of 3.42. The model has been supported by visual checks and human assessments that are able to give sharper images, which are more realistic. The proposed contrast-guided hybrid learning methodology is highly robust in aspects of scales and is scalable in the field of medical imaging, satellite and surveillance imaging. In this study a new benchmark is established in combining contrast-awareness with hybrid learning into the next-generation super-resolution tasks.