This thesis provides an in-depth study of methods for improving image quality using deep learning techniques. By exploring in detail two important deep learning architectures, Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), we propose innovative methods for image quality improvement. Through a series of experiments, we validate the significant effectiveness of these methods in improving image quality. In the CNN-based study, we focus on image super-resolution and significantly improve image clarity by training the network to generate high-resolution images from low-resolution images. And in the GAN-based research, we constructed a powerful image generation framework to achieve more realistic and high-quality image generation through adversarial training. The effectiveness and feasibility of these methods are finally verified by experiments. Eco-design principles are also considered in the design process. Our core concerns include how to minimize the negative impact of deep learning image processing on the environment and how to incorporate eco-design concepts in the image quality improvement process.