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CLC number: TP391.4
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2019-01-22
Cited: 0
Clicked: 6278
Automatic image enhancement by learning adaptive patch selection
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Na Li, Jian Zhang. Automatic image enhancement by learning adaptive patch selection[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(2): 206-221.
@article{title="Automatic image enhancement by learning adaptive patch selection",
author="Na Li, Jian Zhang",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="20",
number="2",
pages="206-221",
year="2019",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1700125"
}
%0 Journal Article
%T Automatic image enhancement by learning adaptive patch selection
%A Na Li
%A Jian Zhang
%J Frontiers of Information Technology & Electronic Engineering
%V 20
%N 2
%P 206-221
%@ 2095-9184
%D 2019
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1700125
TY - JOUR
T1 - Automatic image enhancement by learning adaptive patch selection
A1 - Na Li
A1 - Jian Zhang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 20
IS - 2
SP - 206
EP - 221
%@ 2095-9184
Y1 - 2019
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1700125
Abstract: Today, digital cameras are widely used in taking photos. However, some photos lack detail and need enhancement. Many existing image enhancement algorithms are patch based and the patch size is always fixed throughout the image. Users must tune the patch size to obtain the appropriate enhancement. In this study, we propose an automatic image enhancement method based on adaptive patch selection using both dark and bright channels. The double channels enhance images with various exposure problems. The patch size used for channel extraction is selected automatically by thresholding a contrast feature, which is learned systematically from a set of natural images crawled from the web. Our proposed method can automatically enhance foggy or under-exposed/backlit images without any user interaction. Experimental results demonstrate that our method can provide a significant improvement in existing patch-based image enhancement algorithms.
[1]Assefa M, Poulie T, Kervec J, et al., 2014. Correction of over-exposure using color channel correlations. IEEE Global Conf on Signal and Information Processing, p.1078-1082.
[2]Cai B, Xu X, Jia K, et al., 2016. DehazeNet: an end-to-end system for single image haze removal. IEEE Trans Image Process, 25(11):5187-5198.
[3]Celik T, 2014. Spatial entropy-based global and local image contrast enhancement. IEEE Trans Image Process, 23(12):5298-5308.
[4]Chang YC, Chang CM, 2010. A simple histogram modification scheme for contrast enhancement. IEEE Trans Consum Electron, 56(2):737-742.
[5]Chen Y, Lin W, Zhang C, et al., 2013. Intra-and-inter-constraint-based video enhancement based on piecewise tone mapping. IEEE Trans Circ Syst Video Technol, 23(1):74-82.
[6]Fattal R, 2008. Single image dehazing. ACM Trans Graph, 27(3):1-9.
[7]Gonzalez RC, Wintz P, 1987. Digital Image Processing (2nd Ed.). Addison-Wesley, Boston, USA, p.484-486.
[8]He K, Sun J, Tang X, 2011. Single image haze removal using dark channel prior. IEEE Trans Patt Anal Mach Intell, 33(12):2341-2353.
[9]He K, Sun J, Tang X, 2013. Guided image filtering. IEEE Trans Patt Anal Mach Intell, 35(6):1397-1409.
[10]Jain AK, 1989. Fundamentals of Digital Image Processing. Prentice-Hall, Inc., Upper Saddle River, NJ, USA.
[11]Kopf J, Neubert B, Chen B, et al., 2008. Deep photo: model-based photograph enhancement and viewing. ACM Trans Graph, 27(5):1-10.
[12]Li N, Liu Z, Lei J, et al., 2016. Automatic color image enhancement using double channels. Pacific-Rim Conf on Multimedia, p.74-83.
[13]Liu Z, Zhang C, Zhang Z, 2007. Learning-based perceptual image quality improvement for video conferencing. IEEE Int Conf on Multimedia and Expo, p.1035-1038.
[14]Nakai K, Hoshi Y, Taguchi A, 2013. Color image contrast enhancement method based on differential mboxintensity/saturation gray-levels histograms. Int Symp on Intelligent Signal Processing and Communication Systems, p.445-449.
[15]Narasimhan SG, Nayar SK, 2003. Contrast restoration of weather degraded images. IEEE Trans Patt Anal Mach Intell, 25(6):713-724.
[16]Oakley JP, Bu H, 2007. Correction of simple contrast loss in color images. IEEE Trans Image Process, 16(2):511-522.
[17]Pizer SM, Amburn EP, Austin JD, et al., 1987. Adaptive histogram equalization and its variations. Comput Vision Graph Image Process, 39(3):355-368.
[18]Podpora M, Korbaś GP, Kawala-Janik A, 2014. YUV vs RGB—choosing a color space for human-machine interaction. Federated Conf on Computer Science and Information Systems, p.29-34.
[19]Singh K, Kapoor R, 2014. Image enhancement using exposure based sub image histogram equalization. Patt Recogn Lett, 36(1):10-14.
[20]Sugimura D, Mikami T, Yamashita H, et al., 2015. Enhancing color images of extremely low light scenes based on RGB/NIR images acquisition with different exposure times. IEEE Trans Image Process, 24(11):3586-3597.
[21]Wang Y, Zhuo S, Tao D, et al., 2013. Automatic local exposure correction using bright channel prior for under-exposed images. Signal Process, 93(11):3227-3238.
[22]Xie J, Lin W, Li H, et al., 2011. A new temporal-constraint-based algorithm by handling temporal qualities for video enhancement. IEEE Int Symp of Circuits and Systems, p.2789-2792.
[23]Yuan L, Sun J, 2012. Automatic exposure correction of consumer photographs. European Conf on Computer Vision, p.771-785.
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