HDR-DANet: Single HDR Image Reconstruction via Dual Attention

Abstract

Restoring high dynamic range images with a single low dynamic range image is a challenging task due to the limited brightness range captured by most consumer camera sensors, leading to missing details in overexposed or underexposed areas of the image. The current methods suffer from weak reconstruction ability for inconspicuous complex features in the extreme exposure area, resulting in arti-facts still appearing in the extreme exposure area after reconstruction. This paper proposes an end-to-end network based on dual attention to solve this problem. Specifically, this paper designs a pixel-level spatial attention mechanism with luminance mask to refine the spatial attention to the pixel level, and can focus on inconspicuous feature areas in the overexposed and underexposed regions. This paper also designs a concentrated channel attention mechanism to maximize the use of image context information, accurately assigning the channel weight and effectively extracting the complex features in the image. Qualitative and quantitative evaluations show the superiority of our approach over state-of-the-art methods.