Low dynamic range (LDR) images captured by consumer cameras have a limited luminance range. As the conventional method for generating high dynamic range (HDR) images involves merging multiple-exposure LDR images of the same scene (assuming a stationary scene), we introduce a learning-based model for single-image HDR reconstruction. An input LDR image is sequentially segmented into the local region maps based on the cumulative histogram of the input brightness distribution. Using the local region maps, SParam-Net estimates the parameters of an inverse tone mapping function to generate a pseudo-HDR image. We process the segmented region maps as the input sequences on long short-term memory. Finally, a fast super-resolution convolutional neural network is used for HDR image reconstruction. The proposed method was trained and tested on datasets including HDR-Real, LDR-HDR-pair, and HDR-Eye. The experimental results revealed that HDR images can be generated more reliably than using contemporary end-to-end approaches.

Low dynamic range; high dynamic range; deep learning; convolutional long short-term memory; inverse tone mapping function