Abstract
Mobile phone cameras are often significantly more useful than professional digital single-lens reflex (DSLR) cameras. Knowledge of the camera spectral sensitivity function is important in many fields that make use of images. In this study, methods for measuring and estimating spectral sensitivity functions for mobile phone cameras are developed. In the direct measurement method, the spectral sensitivity at each wavelength is measured using monochromatic light. Although accurate, this method is time-consuming and expensive. The indirect estimation method is based on color samples, in which the spectral sensitivities are estimated from the input data of color samples and the corresponding output RGB values from the camera. We first present an imaging system for direct measurements. A variety of mobile phone cameras are measured using the system to create a database of spectral sensitivity functions. The features of the measured spectral sensitivity functions are then studied using principal component analysis (PCA) and the statistical features of the spectral functions extracted. We next describe a normal method to estimate the spectral sensitivity functions using color samples and point out some drawbacks of the method. A method to solve the estimation problem using the spectral features of the sensitivity functions in addition to the color samples is then proposed. The estimation is stable even when only a small number of spectral features are selected. Finally, the results of the experiments to confirm the feasibility of the proposed method are presented. We establish that our method is excellent in terms of both the data volume of color samples required and the estimation accuracy of the spectral sensitivity functions.
Highlights
In recent years, mobile phones have become widespread and part of our daily lives
The principal component analysis (PCA) results in the previous section suggest that the spectral sensitivity functions of mobile phone cameras can be approximated by linear combinations of a small number of principal components
The spectral sensitivity functions of the iPhone were removed from the original database, and Singular-value decomposition (SVD) was applied to the remaining dataset to obtain the from the original database, SVD was applied to the remaining dataset to obtain the principal components uRi, uand
Summary
Mobile phones have become widespread and part of our daily lives (e.g., see [1]). The combination of mobility, telecommunication, and photography enabled by integrating cameras into mobile devices has transformed our lifestyles. In this respect, mobile phone cameras are far more useful than professional digital single-lens reflex (DSLR) cameras. Beyond imaging, mobile sensing technologies using mobile phone cameras have emerged and are rapidly finding applications in many fields, such as smartphone spectroscopy, medical diagnosis, food quality inspection, and environmental monitoring [2,3,4]. Knowledge of the camera spectral sensitivity function is important in many fields that deal with images, such as imaging science and technology, computer vision, medical imaging, and applications involving cultural heritage or artwork. The function plays the role of mapping the spectral information in a scene to the RGB response values of the camera [5,6]
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