  Image correction aims to adjust an input image into a visually pleasing one.
Existing approaches are proposed mainly from the perspective of image pixel
manipulation. They are not effective to recover the details in the under/over
exposed regions. In this paper, we revisit the image formation procedure and
notice that the missing details in these regions exist in the corresponding
high dynamic range (HDR) data. These details are well perceived by the human
eyes but diminished in the low dynamic range (LDR) domain because of the tone
mapping process. Therefore, we formulate the image correction task as an HDR
transformation process and propose a novel approach called Deep Reciprocating
HDR Transformation (DRHT). Given an input LDR image, we first reconstruct the
missing details in the HDR domain. We then perform tone mapping on the
predicted HDR data to generate the output LDR image with the recovered details.
To this end, we propose a united framework consisting of two CNNs for HDR
reconstruction and tone mapping. They are integrated end-to-end for joint
training and prediction. Experiments on the standard benchmarks demonstrate
that the proposed method performs favorably against state-of-the-art image
correction methods.
