TY - GEN
T1 - Joint regression and ranking for image enhancement
AU - Chandakkar, Parag Shridhar
AU - Li, Baoxin
N1 - Publisher Copyright:
© 2017 IEEE.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/5/11
Y1 - 2017/5/11
N2 - Research on automated image enhancement has gained momentum in recent years, partially due to the need for easy-To-use tools for enhancing pictures captured by ubiquitous cameras on mobile devices. Many of the existing leading methods employ machine-learning-based techniques, by which some enhancement parameters for a given image are found by relating the image to the training images with known enhancement parameters. While knowing the structure of the parameter space can facilitate search for the optimal solution, none of the existing methods has explicitly modeled and learned that structure. This paper presents an end-To-end, novel joint regression and ranking approach to model the interaction between desired enhancement parameters and images to be processed, employing a Gaussian process (GP). GP allows searching for ideal parameters using only the image features. The model naturally leads to a ranking technique for comparing images in the induced feature space. Comparative evaluation using the ground-Truth based on the MIT-Adobe FiveK dataset plus subjective tests on an additional data-set were used to demonstrate the effectiveness of the proposed approach.
AB - Research on automated image enhancement has gained momentum in recent years, partially due to the need for easy-To-use tools for enhancing pictures captured by ubiquitous cameras on mobile devices. Many of the existing leading methods employ machine-learning-based techniques, by which some enhancement parameters for a given image are found by relating the image to the training images with known enhancement parameters. While knowing the structure of the parameter space can facilitate search for the optimal solution, none of the existing methods has explicitly modeled and learned that structure. This paper presents an end-To-end, novel joint regression and ranking approach to model the interaction between desired enhancement parameters and images to be processed, employing a Gaussian process (GP). GP allows searching for ideal parameters using only the image features. The model naturally leads to a ranking technique for comparing images in the induced feature space. Comparative evaluation using the ground-Truth based on the MIT-Adobe FiveK dataset plus subjective tests on an additional data-set were used to demonstrate the effectiveness of the proposed approach.
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U2 - 10.1109/WACV.2017.33
DO - 10.1109/WACV.2017.33
M3 - Conference contribution
AN - SCOPUS:85020174583
T3 - Proceedings - 2017 IEEE Winter Conference on Applications of Computer Vision, WACV 2017
SP - 235
EP - 243
BT - Proceedings - 2017 IEEE Winter Conference on Applications of Computer Vision, WACV 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE Winter Conference on Applications of Computer Vision, WACV 2017
Y2 - 24 March 2017 through 31 March 2017
ER -