LA-HDR: Light Adaptive HDR Reconstruction Framework for Single LDR Image Considering Varied Light Conditions

The high dynamic range (HDR) image recovery from the low dynamic range (LDR) image aims to estimate HDR image by decompressing luminance range and enhancing details of the LDR input. In practical usages, when faced with the over-exposed, the under-exposed or the low-light images, the state-of-art prediction methods lack the capability for ideally handling them. Aiming for this, a light adaptation HDR recovery framework (LA-HDR) is proposed, which includes the multi-images generation for adaptive details amplification in different light ranges, and the following multi-details fusion. To create the multi-images, first, the designed bit-depth enhancement network ( EnhanceNet ) produces the high bit-depth result with enhanced contrast. This result can be furtherly processed by user-defined denoising method to refrain the low-light noise. Meanwhile, the proposed exposure bias network ( EBNet ) estimates the global exposure bias of the input for rectifying the mid-range details. With the enhanced result and the exposure bias, the designed transfer functions adaptively create three multi-images containing the enhanced details in different light ranges, and they are fused by the designed multi-images fusion network ( FuseNet ) for the final HDR prediction. The amplification and fusion scheme ensures robust HDR recovery under different light conditions, eliminating high-light recovery artifacts from previous methods. The proposed fusion masks generation (FMG) and the global feature embedding (GFE) modules in FuseNet help eliminate the fusion artifacts. Experimental results show that LA-HDR acquires the best average performance under various light conditions, and it receives low influence from the input light conditions among the tested state-of-art HDR recovery methods.