RQVR: A multi-exposure image fusion network that optimizes rendering quality and visual realism

Abstract

Deep learning has made significant strides in multi-exposure image fusion in recent years. However, it is still challenging to maintain the integrity of texture details and illumination. This paper proposes a novel multi-exposure image fusion method to optimize Rendering Quality and Visual Realism (RQVR), addressing limitations in recovering details lost under extreme lighting conditions. The Contextual and Edge-aware Module (CAM) enhances image quality by balancing global features and local details, ensuring the texture details of fused images. To enhance the realism of visual effects, an Illumination Equalization Module (IEM) is designed to optimize light adjustment. Moreover, a fusion module (FM) is introduced to minimize information loss in the fused images. Comprehensive experiments conducted on two datasets demonstrate that our proposed method surpasses existing state-of-the-art techniques. The results show that our method not only attains substantial improvements in image quality but also outperforms most advanced techniques in terms of computational efficiency.