Fusing Multiple Exposure Images for HDR Images by Deep Learning

This paper explores the application of deep learning techniques in the fusion of high dynamic range (HDR) images, emphasizing its transformative impact on traditional HDR imaging methods. HDR images are renowned for capturing a broader range of luminosity; however, traditional methods face challenges such as camera shake and ghosting in dynamic scenes. The introduction of deep learning has automated and enhanced the HDR image generation process, particularly in image fusion, deblurring, and artifact correction. This paper reviews relevant deep learning algorithms and architectures, analyzes the strengths and limitations of current HDR imaging approaches, and suggests future research directions aimed at improving efficiency, accuracy, and applicability across various domains.