TY - GEN
T1 - Color image enhancement based on retinex theory with guided filter
AU - Tang, Shi
AU - Dong, Mingjie
AU - Ma, Jinlei
AU - Zhou, Zhiqiang
AU - Li, Changqing
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/12
Y1 - 2017/7/12
N2 - Color image enhancement is widely used in digital image processing. Retinex performs well in color image enhancement, however, traditional Gaussian filter-based retinex algorithms exist some problems such as halo artifacts and detail loss. To solve these problems, we propose an improved retinex image enhancement algorithm based on the guided filter, which is processed in IHS color space. We replace Gaussian filter with the guided filter to get the detail information in different fine scales to better enhance different bands of high-frequency information. Then, we also extract a certain amount of low-frequency information through the decomposition with guided filter in the log domain, while the retinex method based on Gaussian filter only extracts the high-information to enhance the image. Next, we enhance the high-frequency information of the image and combine the enhanced high-frequency information and low-frequency information to get the combined image. Finally, we stretch the combined image to enhance the contrast of the image. In this way, we get the result image with enhanced details and contrast. Compared with some existing retinex methods in image enhancement, our algorithm can avoid the halo artifacts and detail loss. Key Words: Image Enhancement, Retinex, The Guided Filter.
AB - Color image enhancement is widely used in digital image processing. Retinex performs well in color image enhancement, however, traditional Gaussian filter-based retinex algorithms exist some problems such as halo artifacts and detail loss. To solve these problems, we propose an improved retinex image enhancement algorithm based on the guided filter, which is processed in IHS color space. We replace Gaussian filter with the guided filter to get the detail information in different fine scales to better enhance different bands of high-frequency information. Then, we also extract a certain amount of low-frequency information through the decomposition with guided filter in the log domain, while the retinex method based on Gaussian filter only extracts the high-information to enhance the image. Next, we enhance the high-frequency information of the image and combine the enhanced high-frequency information and low-frequency information to get the combined image. Finally, we stretch the combined image to enhance the contrast of the image. In this way, we get the result image with enhanced details and contrast. Compared with some existing retinex methods in image enhancement, our algorithm can avoid the halo artifacts and detail loss. Key Words: Image Enhancement, Retinex, The Guided Filter.
UR - http://www.scopus.com/inward/record.url?scp=85028077827&partnerID=8YFLogxK
U2 - 10.1109/CCDC.2017.7978178
DO - 10.1109/CCDC.2017.7978178
M3 - Conference contribution
AN - SCOPUS:85028077827
T3 - Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017
SP - 5676
EP - 5680
BT - Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 29th Chinese Control and Decision Conference, CCDC 2017
Y2 - 28 May 2017 through 30 May 2017
ER -