Single Image Super-Resolution Through Image Pixel Information Clustering and Generative Adversarial Network

Recent advances in Super-Resolution (SR) image reconstruction using Convolutional Neural Networks (CNNs) have encountered significant challenges in effectively modeling the complex mapping between Low-Resolution (LR) and High-Resolution (HR) images. While Generative Adversarial Networks (GANs) have been explored as a potential solution to enhance SR performance, these models often suffer from prolonged training and inference times, and may fail to preserve intricate texture details in the reconstructed images. In response to these limitations, we propose a novel fusion network architecture, termed CLustering and Generative Adversarial Network (CL-GAN), designed to concurrently learn and integrate the features of clustered image segments and low-resolution inputs, thereby enhancing the SR reconstruction process. The CL-GAN framework comprises two primary components: a local network that emphasizes feature extraction from clustered image regions, and a global network built upon a GAN framework to model global image characteristics. To further improve texture recovery, we incorporate dense connection mechanisms within both the local and global networks, facilitating the preservation of fine-grained details in the generated SR images. Extensive experiments conducted on publicly available datasets demonstrate that the proposed CL-GAN framework outperforms existing state-of-the-art methods, delivering superior SR images with enhanced detail fidelity and visual quality.