RADIOELECTRONIC AND COMPUTER SYSTEMS

The subject of this article is Image Super-Resolution (ISR) using deep learning techniques.  ISR is a rapidly evolving research area in computer science that focuses on producing high-resolution images from one or more low-resolution sources. It has garnered substantial interest due to its broad applications in areas such as medical imaging, remote sensing, and multimedia. The rise of deep learning techniques has brought a revolution in ISR, providing superior performance and computational efficiency compared to traditional methods and driving further advancements in overcoming the challenges associated with enhancing image resolution. The goal of this study is to enhance the quality of super-resolved images by developing a novel deep learning approach. Specifically, we explore the integration of Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) to address the inherent challenges of producing high-quality images from low-resolution data. This study aims to push the boundaries of ISR by combining these architectures for greater precision and visual fidelity. The tasks are as follows: 1) design and implement a hybrid model using CNNs and GANs for image super-resolution tasks; 2) train the model on benchmark datasets like Set5, Set14, DIV2K, and specialized datasets such as X-ray images; 3) assess the model’s performance using numerical metrics like Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM); 4) to compare the proposed method against existing state-of-the-art ISR techniques and demonstrate its superiority. The following results were obtained in this study: Our deep learning model, which integrates the Super-Resolution Convolutional Neural Network (SRCNN) and the Super-Resolution Generative Adversarial Network (SRGAN), demonstrated significant performance improvements. The CNN successfully learned to map low-resolution image patches to their high-resolution counterparts, and the GAN further refined the images, enhancing both precision and visual quality. The evaluation metrics yielded highly promising results, with Peak Signal-to-Noise Ratio (PSNR) reaching up to 36.1368 dB and Structural Similarity Index Measure (SSIM) reaching 0.9670. These values exceed the benchmarks set by contemporary ISR methods, thus validating the superiority and effectiveness of our approach in the field of image super-resolution. Conclusions. This study demonstrated the potential of combining CNN and GAN in the domain of image super-resolution. The proposed model exhibits significant advancements over existing ISR methods, offering higher accuracy and improved image quality. The findings confirm the efficiency of deep learning methods in overcoming traditional imaging challenges, making the proposed model valuable for both academic research and practical applications in ISR.

Deep learning; Image Super-resolution; Convolutional Neural Network; Generative Adversarial Network; SRCNN; SRGAN

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