We present a new deep learning framework for low-contrast image enhancement, which trains the network using the multi-exposure sequences rather than explicit ground-truth images. The purpose of our method is to enhance a low-contrast image so as to contain abundant details in various exposure levels. To realize this, we propose to design the loss function using the structure tensor representation, which has been widely used as high-dimensional image contrast. Our loss function penalizes the difference of the structure tensors between the network output and the multi-exposure images in a multi-scale manner. Eventually, the network trained by the loss function produces a high-quality image approximating the overall contrast of the sequence. We provide in-depth analysis on our method and comparison with conventional loss functions. Quantitative and qualitative evaluations demonstrate that the proposed method outperforms the existing state-of-the art approaches in various benchmarks.
|Title of host publication||35th AAAI Conference on Artificial Intelligence, AAAI 2021|
|Publisher||Association for the Advancement of Artificial Intelligence|
|Number of pages||9|
|Publication status||Published - 2021|
|Event||35th AAAI Conference on Artificial Intelligence, AAAI 2021 - Virtual, Online|
Duration: 2021 Feb 2 → 2021 Feb 9
|Name||35th AAAI Conference on Artificial Intelligence, AAAI 2021|
|Conference||35th AAAI Conference on Artificial Intelligence, AAAI 2021|
|Period||21/2/2 → 21/2/9|
Bibliographical noteFunding Information:
This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.2020-0-00056, To create AI systems that act appropriately and effectively in novel situations that occur in open worlds).
Copyright © 2021, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved
All Science Journal Classification (ASJC) codes
- Artificial Intelligence