Image Enhancement Using Adaptive Region- Guided Multi-Step Exposure Fusion Based on Reinforcement Learning

In photography, accurate exposure is the key to taking high-quality photos, particularly images with uneven exposure levels. Global exposure operations are usually difficult to effectively strengthen the various regions of the image. To achieve a balanced exposure level in different regions of the image, we propose a novel algorithm inspired by the luminance masking frequently employed by professional photographers. We use reinforcement learning to adaptively generate guiding regions and adjusting parameters as a basis for multi-step exposure fusion, to enhance the overall quality of the image. Firstly, reinforcement learning is employed to automatically segment the single image to be enhanced into multiple sub-images, with corresponding appropriate adjusting parameters for each sub-image generated. Then, the input image is enhanced using the local adjusting parameters, yielding a set of images with varying enhancing degrees. Finally, these images are fused in an exposure process to obtain the final result. Experimental results show that our method not only generates intuitive and interpretable guiding regions, but also its performance is comparable to that of other contemporaneous methods.