FTUnet: Feature Transferred U-Net For Single HDR Image Reconstruction

Shifeng Xie, Yi LIU, Shuai Wenjing

Abstract:

The development of the display technology supports the application of High Dynamic Range (HDR) enabling devices. In order to
meet the surging demand for the HDR media content, we propose a feature-transferred U-shaped network (FTUnet) to convert existing
Standard Dynamic Range (SDR) images into their HDR counterparts. The proposed FTUnet is a feature transformation network
that converts the encoded SDR features to the HDR features. This transformation network extracts features rich of spatial information
by a self-attention mechanism, in order to improve the reconstruction of the over-exposed regions and avoid unreasonable patches.
Besides, we propose an Excitation-Restoration (ER) sub-network to involve the inter-channel attention mechanism. The ER network
is used to remove redundant information between channels and reserve the key features. Therefore, the proposed FTUnet can efficiently merge feature channels and contribute to the advantage in color accuracy for the generated HDR images. Experimental
results show that our proposed FTUnet achieves state-of-the-art performance in both quantitative comparison and visual quality
for the single HDR image reconstruction. The ablation study is also performed to demonstrate the effectiveness of each module of the
proposed FTUnet.