Attention-Guided Network for Ghost-Free High Dynamic Range Imaging

Ghosting artifacts caused by moving objects or misalignments is a key challenge in high dynamic range (HDR) imaging for dynamic scenes. Previous methods first register the input low dynamic range (LDR) images using optical flow before merging them, which are error-prone and cause ghosts in results. A very recent work tries to bypass optical flows via a deep network with skip-connections, however, which still suffers from ghosting artifacts for severe movement. To avoid the ghosting from the source, we propose a novel attention-guided end-to-end deep neural network (AHDRNet) to produce high-quality ghost-free HDR images. Unlike previous methods directly stacking the LDR images or features for merging, we use attention modules to guide the merging according to the reference image. The attention modules automatically suppress undesired components caused by misalignments and saturation and enhance desirable fine details in the non-reference images. In addition to the attention model, we use dilated residual dense block (DRDB) to make full use of the hierarchical features and increase the receptive field for hallucinating the missing details. The proposed AHDRNet is a non-flow-based method, which can also avoid the artifacts generated by optical-flow estimation error. Experiments on different datasets show that the proposed AHDRNet can achieve state-of-the-art quantitative and qualitative results.

1. Introduction

The dynamic range of natural luminance values varies over several orders of magnitude. However, most digital photography sensors can only measure a limited fraction of this range. The resulting low dynamic range (LDR) images thus often have over or underexposed regions and don’t reflect the human ability to see details in both bright and dark areas of a scene. High dynamic range (HDR) imaging has been developed to compensate for these limitations, and ideally aims to generate a single image that represents a broad range of illuminations.