BFMEF: Brightness-Free Multi-exposure Image Fusion via Adaptive Correction

Pan Mu, Binjia Zhou, Qirui Wang, Zhiying Du, Xiaoyan Wang

Abstract: In recent years, deep learning has revolutionized the field of multi-exposure image fusion (MEF), overcoming the limitations of traditional techniques and proving more effective in complex and diverse scenarios. However, existing MEF methods mainly focus on paired extreme exposure dual inputs and pay little attention to single inputs or other extreme conditions. To address this gap, this paper introduces a fusion architecture that can adaptively correct various input forms with Auto-Gamma Correction (AGC). By leveraging the powerful information interaction capability of the Transformer, the Light-Guided Module (LGM) effectively extracts the brightness information from the input images. Furthermore, a specially designed color enhancement algorithm obtains a high-saturation fused image. Experimental results show that compared with the most advanced methods, our method achieves the best effects in terms of visuals and performance.

External IDs:dblp:conf/icmcs/MuZWDW24