Advancements in image processing: Towards near-reversible data hiding and enhanced dehazing using deep learning

In the age of digital transformation, image processing techniques play a crucial role in various applications, ranging from security to entertainment. This paper explores two significant advancements in the field: near-reversible data hiding schemes and deep learning-based single-image dehazing techniques. Near-reversible data hiding focuses on embedding secret information into digital images with minimal distortion, ensuring that the original image can be almost perfectly recovered. Conversely, deep learning-based single-image dehazing aims to enhance the quality and robustness of images affected by atmospheric haze, using reference images and advanced neural network architectures. This paper delves into the principles, methodologies, and applications of these cutting-edge techniques, shedding light on their potential impact on the future of image processing.