Radiometric Calibration for Internet Photo Collections

Zhipeng Mo, Boxin Shi, Sai-Kit Yeung, Yasuyuki Matsushita; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 4738-4746

Abstract


Radiometrically calibrating the images from Internet photo collections brings photometric analysis from lab data to big image data in the wild, but conventional calibration methods cannot be directly applied to such image data. This paper presents a method to jointly perform radiometric calibration for a set of images in an Internet photo collection. By incorporating the consistency of scene reflectance for corresponding pixels in multiple images, the proposed method estimates radiometric response functions of all the images using a rank minimization framework. Our calibration aligns all response functions in an image set up to the same exponential ambiguity in a robust manner. Quantitative results using both synthetic and real data show the effectiveness of the proposed method.

Related Material


[pdf] [poster] [video]
[bibtex]
@InProceedings{Mo_2017_CVPR,
author = {Mo, Zhipeng and Shi, Boxin and Yeung, Sai-Kit and Matsushita, Yasuyuki},
title = {Radiometric Calibration for Internet Photo Collections},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}