The Application of Deep Learning in Image Super-resolution Reconstruction and Enhancement Technology

With the widespread expansion of digital image applications, the demand for high-resolution images is increasing, driving the development of Image Super Resolution (ISR) technology. ISR improves image quality by increasing the pixel density of the image, which is an effective method to enhance resolution. In this field, deep learning (DL) technology has become the core force driving the development of ISR reconstruction technology due to its powerful feature learning and representation capabilities. This study adopts a deep learning model based on Convolutional Neural Network (CNN), which automatically learns the mapping relationship between low resolution and high resolution images by designing a multi-layer network structure. This article uses an improved residual learning framework to reduce information loss during the learning process, and employs Generative Adversarial Network (GAN) to enhance image details and texture texture. For images of 256x256 size, the processing time of the model is only 20 milliseconds. This means that the model can process such small-sized images very quickly, making it very suitable for applications that require high real-time performance. The application of deep learning in the field of ISR reconstruction and enhancement not only improves the quality and resolution of images, but also greatly promotes the development and application of related technologies, which has important research value and broad market application prospects.