Simulation of the imaging pipeline is an important tool for the design and evaluation of imaging systems. One of
the most important requirements for an accurate simulation tool is the availability of high quality source scenes.
The dynamic range of images depends on multiple elements in the imaging pipeline including the sensor, digital
signal processor, display device, etc. High dynamic range (HDR) scene spectral information is critical for an
accurate analysis of the effect of these elements on the dynamic range of the displayed image. Also, typical digital
imaging sensors are sensitive well beyond the visible range of wavelengths. Spectral information with support
across the sensitivity range of the imaging sensor is required for the analysis and design of imaging pipeline
elements that are affected by IR energy. Although HDR scene data information with visible and infrared content
are available from remote sensing resources, there are scarcity of such imagery representing more conventional
everyday scenes. In this paper, we address both these issues and present a method to generate a database of
HDR images that represent radiance fields in the visible and near-IR range of the spectrum. The proposed
method only uses conventional consumer-grade equipment and is very cost-effective.
A practical and easy way to capture images of oil-paintings and estimate their spectral reflectance as a function of position was tested. For the image acquisition, a trichromatic digital camera was used in conjunction with an absorption filter producing six channels. From an a priori statistical analysis of common artist oil paints, spectral reflectance was estimated. These experiments showed that it is possible to estimate the spectral reflectance with an accuracy of average ΔE*94 of 1.7 and spectral reflectance rms error of 2.2%. Of particular interest is guidance towards the design of a universal calibration target for imaging paintings.
Efforts to construct end-to-end color reproduction systems based on the preservation of scene spectral data have been underway at the Munsell Color Science Laboratory. The goal is to present hardcopy results which are spectrally matched to original colors. The evaluated approach consists of capturing scenes through a trichromatic digital camera combined with multiple filterings followed by an image processing stage and then four-color printing. The acquisition end is designed to estimate original scene spectra on a pixel-by-pixel basis based on system characteristics which takes into account the camera sensitivities as modulated by the filterings followed by an image processing stage and then four-color printing. The acquisition end is designed to estimate original scene spectra on a pixel-by-pixel basis based on system characterizations which takes into account the camera sensitivities as modulated by the filterings an scene colorant make-up. The spectral-based printing used in this research is able to produce the least metameric reproduction to the original scene using a computationally feasible approach. Results show a system accuracy of mean (Delta) E*94 of 1.5 and spectral reflectance rms error of 0.9 percent.
KEYWORDS: Printing, Reflectivity, Image processing, Transform theory, Colorimetry, Image storage, Color management, CMYK color model, RGB color model, Color reproduction
Traditional image processing techniques used for 3- and 4- band images are not suited to the many-band character of spectral images. A sparse multi-dimensional lookup table with inter-node interpolation is a typical image processing technique used for applying either a known model or an empirically derived mapping to an image. Such an approach for spectral images becomes problematic because input dimensionality of lookup tables is proportional to the number of source image bands and the size of lookup table sis exponentially related to the number of input dimensions. While an RGB or CMY source images would require a 3D lookup table, a 31-band spectral image would need a 31-dimensional lookup table. A 31-dimensional lookup table would be absurdly large. A novel approach to spectral image processing is explored. This approach combines a low-cost spectral analysis followed by application of one from a set of low-dimensional lookup tables. The method is computationally feasible and does not make excessive demands on disk space or run-time memory.
The traditional techniques of image capture, scanning, proofing, and separating do not take advantage of colorimetry and spectrophotometry. For critical color-matching applications such as catalog sales, art-book reproductions, and computer-aided design, typical images, although pleasing, are unacceptable with respect to color accuracy. The limitations that lead to these errors have a well-defined theoretical basis and are a result of current hardware and software. This has led us to a re-examination of the traditional graphic reproduction paradigm. A research and development program has begun that will alleviate the theoretical limitations associated with traditional techniques. There are four main phases: (1) Multi-spectral image capture, (2) Spectral-based separation and printing algorithm development, (3) Implementation on press, and (4) Systems integration with data and image archives. This paper describes this new paradigm, summarizes recent research results, and considers implementation opportunities.
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