Deep learning techniques have revolutionized the field of computer vision and image processing, offering significant advancements in image enhancement and reconstruction tasks. This paper presents a systematic review of deep learning approaches applied to image enhancement and reconstruction in environmental applications. We analyze and summarize the state-of-the-art methods, datasets, and evaluation metrics employed in this domain. Furthermore, we discuss the challenges and future directions for the development of deep learning techniques in environmental image processing.