This document considers the planar motions of camera, that is, the rotation, and the horizontal and vertical translations in
the image plane. The approach based on projection including both Cartesian coordinate system and polar coordinate
system can estimate the three parameters comparably quickly with simple calcuation. The potential applications cover
motion deblurring, noise reduction, super-resolution, image fusion, high dyanmic range image processing, EDOF, 3D
imaging or those techniques which require global or local registration.
KEYWORDS: Cameras, Digital imaging, Image quality, Control systems, Camera shutters, Statistical modeling, Automatic exposure, Automatic control, Data analysis, Photography
The automatic exposure control (AEC) for a camera phone is typically a simple function of the brightness of the image.
This brightness, or intensity, value generated from a frame is compared to a predefined target. If the intensity value is
less than a specified target, the exposure is increased. If the value is greater, exposure will be decreased.
Is using an intensity target statistic a good model for AEC? In order to answer this question, we conducted
psychophysical experiments to understand subjective preferences. We used a high-end DSLR to take 64 different
outdoor and indoor scenes. Each scene was captured using five different exposure values (EV), from EV-1 to EV+1 with
half EV increments. Subjects were shown the five exposures for each scene and asked to rank them based on their
preferences.
The collected data were analyzed along different dimensions: preferences as a function of the subjects, EV levels, image
quality scores, and the images themselves. Our data analysis concludes that a dynamic intensity target is needed to match
the exposure preferences collected from our subjects.
KEYWORDS: Digital imaging, Visual system, Cameras, High dynamic range imaging, CRTs, LCDs, Color difference, Contrast sensitivity, Printing, Image processing
High dynamic compression of natural scenes and nonlinearity compensation of output devices are demanded as the
output devices have limited dynamic range and color gamut, and devices such as Standard-RGB compliant displays and
printers have different nonlinear responses to linear inputs. This paper describes a general framework of nonlinear curve
optimization in digital imaging and its application to the determination of gamma look up table through curve
parameterization. Three factors are considered, that is, the color appearance fidelity, the feature preservation, and the
suppression of noise propagation.
Multi-channel imaging gains more and more applications because of its advantage in better color reproduction and better spectral representation to avoid metamerism problem. Illuminant estimation for multi-channel images is not widely studied because most illuminant estimation methods are applied to trichromatic images. In this paper, some common illuminant estimation methods such as gray world and maximum RGB are extended to multi-channel images. Five methods are evaluated for multi-channel images including gray world, maximum RGB, Maloney-Wandell method, modified illuminant detection in linear space and reflectance constraint illuminant detection. The methods are evaluated in terms of illuminant detection efficiency through estimating the illuminant correlated color temperature. Among them, the method of reflectance constraint illuminant detection has the best efficiency. In addition, the former three methods, which were only used in illuminant estimation for three-channel images before, are attempted in illuminant spectral recovery. The recovery efficiencies are evaluated through comparing the difference between the recovered spectral distributions and the original ones. Maloney-Wandell method has large efficiency improvement when the number of channels increases from three to four. It also has the best spectral recovery among the three tested methods when the channel number is more than three.
KEYWORDS: Image quality, Colorimetry, Imaging systems, Image quality standards, Data modeling, Digital cameras, Image analysis, CRTs, Color reproduction, Digital imaging
Image noise is one of the important image quality metrics for evaluation and optimization in color reproduction system. While the noise evaluation in psychometric lightness L* works pretty well in black-and-white images, it is insufficient for color images. A perceptual color noise evaluation equation was derived to extend noise evaluation in CIELAB color space, incorporating chromatic noise components. Psychophysical experiments were designed to evaluate color noise subjectively. In the first step, Weber's Law and Fechner's Law were used to generate a standard ruler with equal perceptual interval steps, which served to anchor a numerical image quality rating scale for the subjective rating experiment in the second step. An objective noise evaluation equation that incorporated the noise sensitivity functions modeled from the experimental data was found to better correlate with subjective evaluation. A more robust noise evaluation equation will be derived based on the psychophysical experiment techniques and experimental data in the future.
Reproduction of more pleasing colors is one of the efficient methods to improve image quality of color imaging devices. A psychophysical experiment was completed to investigate the preferred colors for three main categories: human skin, blue sky and green grass. A new experimental technique, the cube-selection method, was developed to adjust lightness, chroma and hue to find out observer’s preference in CIELAB color space. It is a fast and accurate multi-dimensional adjustment technique superior to the conventional method of adjustment. Several potential influence factors for image color preference including image content, capturing illuminant, object background and culture difference were studied by comparing the observers’ preference. Applicable conclusions were drawn from the analysis of the experiment results that help better understand the influence of these factors. It showed that capturing illuminant and image content had significant influence for human skin and grass color reproduction preference respectively. The results from this paper show the way for further research on influence factors of color preference in photographic color reproduction.
The phenomenon has long been noticed that colors are more vivid under white illuminants. A new illuminant estimation method, maximum color separation, is based on the assumption that image gamut reaches its maximum when under white illuminants. It was also found out that when the image gamut reaches its maximum through the diagonal transformation in (r, g) space, the centroid of the gamut locates at (1/3, 1/3), which makes the method to have strong connection with the most widely used gray world method. In this paper, the above consequence is proven to be true when the representation of gamut is extended from triangle to polygon. In addition, the basic assumption is modified for application use. One modification is to do maximum color separation at each lightness level, and another modification is the adjustment of the reference illuminant. The method is proved to be an effect illuminant estimation method through testing on real images.
For a long time, the constraints on surface spectral reflectances are the range of 0 to 1, smooth and low frequency. Those constraints are tested to be too loose in practical use, typically for illuminant estimation with spectral recovery. The proposal of linear model and PCA decomposition made it possible to effectively reconstruct spectral reflectances with small numbers of parameters. Based on that, a new constraint on surface spectral reflectance is proposed to have better limitation and description of their characteristics. It is defined as a two-dimensional histogram of the coefficients for the spectral reflectances in the real world. The variables in the two dimensions are the ratios of the parameters from PCA, which describe the “saturation” property of reflectances. There are differences between the application of gamut and histogram in illuminant estimation. Histogram is preferred to gamut when the color space is composed of relative values. Based on that, the original color by correlation method is modified to have better performance especially on real images. The proposed constraint is applied to illuminant detection with spectral recovery. In the method, the recovered surface reflectances are examined by the constraint, and the scene illuminant is detected through possibility comparison. The proposed method is tested to have good efficiency compared with others, both on synthetic and real images.
The spectral characterization of digital imaging devices overcomes the drawbacks of conventional colorimetric characterization by determining the sensor spectral sensitivity functions. Direct measurement of the sensitivities requires expensive instruments and takes long time. A “quick and easy” yet accurate enough estimation of those functions are desired in some circumstances. The estimation is realized by imaging some selected set of reflectance samples. In this paper, some primary available approaches to the sensor sensitivity estimation are reviewed, followed by the description of the proposed iterative multiscale basis functions method. The performance of the new method is compared with some of the available approaches. The implementation of the new method is relatively simple and the results show that it is superior by offering more degrees of freedoms and yielding nonnegative, smooth, and close approximation under either noiseless or noisy condition.
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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.