Sharpness enhancement is widely used technique for improving the perceptual quality of an image by emphasizing its high-frequency component. In this paper, a psychophysical experiment is conducted by the 20 observers with simple linear unsharp masking for sharpness enhancement. The experimental result is extracted using z-score analysis and linear regression. Finally using this result we suggest observer preferable sharpness enhancement method for digital television.
KEYWORDS: 3D modeling, RGB color model, Colorimetry, Reflectivity, Virtual reality, Principal component analysis, Light sources, 3D image processing, Statistical analysis, Color imaging
The algorithm of combining a real image with a virtual model was proposed to increase the reality of synthesized images. Currently, synthesizing a real image with a virtual model facilitated the surface reflection model and various geometric techniques. In the current methods, the characteristics of various illuminants in the real image are not sufficiently considered. In addition, despite the chromatic adaptation plays a vital role for accommodating different illuminants in the two media viewing conditions, it is not taken into account in the existing methods. Thus, it is hardly to get high-quality synthesized images. In this paper, we proposed the two-phase image synthesis algorithm. First, the surface reflectance of the maximum high-light region (MHR) was estimated using the three eigenvectors obtained from the principal component analysis (PCA) applied to the surface reflectances of 1269 Munsell samples. The combined spectral value, i.e., the product of surface reflectance and the spectral power distributions (SPDs) of an illuminant, of MHR was then estimated using the three eigenvectors obtained from PCA applied to the products of surface reflectances of Munsell 1269 samples and the SPDs of four CIE Standard Illuminants (A, C, D50, D65). By dividing the average combined spectral values of MHR by the average surface reflectances of MHR, we could estimate the illuminant of a real image. Second, the mixed chromatic adaptation (S-LMS) using an estimated and an external illuminants was applied to the virtual-model image. For evaluating the proposed algorithm, experiments with synthetic and real scenes were performed. It was shown that the proposed method was effective in synthesizing the real and the virtual scenes under various illuminants.
In this paper, we propose an adaptive stereo matching algorithm to treat stereo matching problems in projective distortion regions. Since the disparities in the projective distortion region can not be estimated in terms of fixed- size block matching algorithm, an adaptive window warping method with hierarchical matching process is used to compensate perspective distortions. In addition, a probability model, based on the statistical distribution of matched errors and constraint functions, is adopted to handle the uncertainty of matching points. Since the proposed window warping process is based on a statistical window warping step with the reliability estimation of matching points, any relaxation process need not to use. As a result, overall processing time is reduced, compared with conventional stereo matching algorithm including a relaxation step, and improved matching results are obtained. Experimental results on both disparity map and 3D model view show that the proposed matching algorithm is effective for various images, even if the image has projective distortion regions and repeated patterns.
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