Lightweight Reinforcement-Based Approach for HDR Conversion

Various deep learning-based methods have shown excellent performances in converting images from Low Dynamic Range (LDR) to High Dynamic Range (HDR). Many of them employ architectures like Autoencoders or U-Nets, and demonstrate significant improvements in performance, however, they demand large network sizes and associated computational loads. It leads to serious issues of overheating and power consumption, especially for average users handling many LDR images on personal devices such as smartphones, laptops, and tablets. Our study addresses this issue using reinforcement learning aiming for a practical solution. In this approach, we integrate a lightweight agent with traditional methods by designing a simple and effective reward function to ensure that the lightweight agent could effectively execute the traditional methods. Consequently, we perform the task of HDR conversion using a network that requires relatively low computational resources.