Abstract
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