Journal
IEEE ACCESS
Volume 9, Issue -, Pages 168342-168354Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3137993
Keywords
Image color analysis; Feature extraction; Image restoration; Brightness; Annotations; Histograms; Licenses; Interactive contrast enhancement; personalized contrast enhancement; convolutional neural network; adaptive gamma correction
Categories
Funding
- National Research Foundation of Korea (NRF) - Korea Government (MSIT) [NRF-2018R1A2B3003896, NRF-2021R1A4A1031864]
- MSIT, South Korea, under the ITRC support program [IITP-2021-2016-0-00464]
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IceNet is a CNN-based interactive contrast enhancement algorithm that allows users to easily adjust image contrast by providing parameters and scribbles. By estimating gamma mapping and color restoration, IceNet generates enhanced images, and users can iteratively provide annotations to achieve satisfactory results.
A CNN-based interactive contrast enhancement algorithm, called IceNet, is proposed in this paper, which enables a user to adjust image contrast easily according to his or her preference. Specifically, a user provides a parameter for controlling the global brightness and two types of scribbles to darken or brighten local regions in an image. Then, given these annotations, IceNet estimates a gamma map for the pixel-wise gamma correction. Finally, through color restoration, an enhanced image is obtained. The user may provide annotations iteratively to obtain a satisfactory image. IceNet is also capable of producing a personalized enhanced image automatically, which can serve as a basis for further adjustment if so desired. Moreover, to train IceNet effectively and reliably, we propose three differentiable losses. Extensive experiments demonstrate that IceNet can provide users with satisfactorily enhanced images.
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