Wide dynamic range processing algorithm for infrared images based on reinforcement learning

Adaptive processing of infrared imagery with a wide dynamic range is a formidable task in thermal imaging systems. Existing work has mainly addressed histogram equalization and statistical transforms, which fail to maintain details for extreme temperature swings in the same scene. Standard techniques lead to a loss of detail in high-temperature areas or a lack of enough contrast in low-temperature environments, which keeps infrared systems ineffective in challenging situations. Research overcomes such limitations by formulating a Wide Dynamic Range processing algorithm based on Double Deep Q-Network (WDR-DDQN), an intelligent framework capable of optimizing image enhancement parameters according to scene properties. WDR-DDQN applies a double deep Q-network (DDQN) reinforcement learning structure with experience replay and prioritization, dueling networks, and multi-scale thermal decomposition methods that enhance adaptive local and global contrast, maintaining important thermal details. The WDR-DDQN framework attained a 42% improvement in structural similarity index, a 37% improvement in target recognition rate, and a 28% improvement in processing latency over conventional methods on various thermal datasets. These gains allow for much improved visualization and detection performance in difficult cases with extreme temperature gradients. Research promotes deep reinforcement learning in real-world infrared image processing applications, thereby providing novel capabilities for security systems, self-driving cars, and military usage where accurate thermal imaging over a broad range of temperatures is crucial.