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Hybrid Resolution Spectral Imaging by Class-based Regression Method

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Abstract:

Hybrid resolution spectral imaging produces spectral images from high-resolution RGB images and corresponding low-resolution spectral data. Various methods have been proposed, whereas the low-resolution spectral data are regarded as the sample data of target scenes. However, this approach is not appropriate when each spectrum in the low-resolution data may be a mixture of spectra with different spectral features, and the original spectral feature is lost by averaging them. To solve this problem, class-based regression method for mixed low-resolution spectral data was proposed. In this method, the spectral estimation matrix for every class is derived using a regression approach, where the clustering results of the high-resolution RGB image are used to incorporate spectral unmixing. However, the method was tested only for small regions of images. In this paper, spectral images are estimated by the class-based regression method for three test spectral images, and the accuracy is compared with two conventional methods for hybrid resolution spectral imaging. Experiments confirm that the spectra are accurately reconstructed only by class-based regression method when they are observed as mixed spectra in the low-resolution data.

Document Type: Research Article

Publication date: 2011-01-01

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  • CIC is the premier annual technical gathering for scientists, technologists, and engineers working in the areas of color science and systems, and their application to color imaging. Participants represent disciplines ranging from psychophysics, optical physics, image processing, color science to graphic arts, systems engineering, and hardware and software development. While a broad mix of professional interests is the hallmark of these conferences, the focus is color. CICs traditionally offer two days of short courses followed by three days of technical sessions that include three keynotes, an evening lecture, a vibrant interactive (poster) papers session, and workshops. An endearing symbol of the meeting is the Cactus Award, given each year to the author(s) of the best interactive paper; there are also Best Paper and Best Student Paper awards.

    Please note: for Purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.

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