MIEHDR CNN: Main Image Enhancement based Ghost-Free High Dynamic Range Imaging using Dual-Lens Systems

Xuan Dong, Xiaoyan hu, Weixin Li, Xiaojie Wang, Yunhong Wang

Abstract:

We study the High Dynamic Range (HDR) imaging problem using two Low Dynamic Range (LDR) images that are shot
from dual-lens systems in a single shot time with different exposures. In most of the related HDR imaging methods, the
problem is usually solved by Multiple Images Merging, i.e. the final HDR image is fused from pixels of all the input LDR
images. However, ghost artifacts can be hardly avoided using this strategy. Instead of directly merging the multiple LDR
inputs, we use an indirect way which enhances the main image, i.e. the short exposure image IS, using the long exposure
image IL serving as guidance. In detail, we propose a new model, named MIEHDR CNN model, which consists of three
subnets, i.e. Soft Warp CNN, 3D Guided Denoising CNN and Fusion CNN. The Soft Warp CNN aligns IL to get the aligned
result ILA using the soft exposed result of IS as reference. The 3D Guided Denoising CNN denoises the soft exposed
result of IS using ILA as guidance, whose result are fed into the Fusion CNN with IS to get the HDR result. The MIEHDR
CNN model is implemented by MindSpore and experimental results show that we can outperform related methods largely
and avoid ghost artifacts.