In current printing technique, the Color Management System uses the ICC profiles of monitor and printer to perform
color matching. Unfortunately the ICC profile cannot capture all of the monitor color reproduction characteristics,
because such features change when the user acts on the color temperature, brightness and contrast controls, and they also
depend on the kind of backlighting and lifetime of LCD monitor. As a result there is usually an unwanted color
difference between an image displayed on the user monitor and its printed version. Yet, once we are able to produce an
ICC profile that matches the user's monitor characteristics by measuring, then the CMS becomes able to correctly
perform color matching. However, this method is of difficult application, because in general the measuring equipment is
not available and, even then, it takes a long time and new measurements according to monitor color temperature,
brightness and contrast. In this paper we propose a color matching technique based on estimate of the user's environment
through the simple visual test with an output image on monitor and its printed image. The estimated characteristic of
monitor is stored in new ICC profile and applied to color conversion process. Consequently the proposed method
reduced the color difference between image displayed on user monitor and its printed image.
In this paper, subpixel shift estimation method using phase correlation with local region is proposed for registration of
noisy images. Commonly, phase correlation based on the Fourier shift property is used to estimate the shift between
images. Subpixel shift of images can be estimated by the analysis for the phase correlation of downsampled images.
However, in case of images with noise or aliasing artifacts, the error in estimation is increased. Thus, we consider a
small region in a corner of an image instead of the whole, because flat regions with noise and regions with aliasing
induce the error of estimation. In addition, to improve accuracy, the local regions are inversely shifted by varying the
subpixel shift values, and obtaining the peak value of phase correlation between the images. Then, the subpixel shift
value corresponding to the maximum of the peak values is selected. Real-time implementation of this process is possible
because only a local region is used, thereby reducing the process time. In experiments, the proposed method is
compared with conventional methods using several fitting functions, and it is applied for the task of super resolution
imaging. The proposed method shows higher accuracy in registration than other methods, also, edge-sharpness in superresolved
images is improved.
This paper proposes a method of colorimetric characterization based on the color correlation between the distributions of colorant amounts in a CMYKGO printer. In colorimetric characterization beyond three colorants, many color patches with different combinations of colorant amounts can be used to represent the same tri-stimulus value. Therefore, choosing the proper color patches corresponding each tri-stimulus value is important for a CMYKGO printer characterization process. As such, the proposed method estimates the CIELAB value for many color patches, then selects certain color patches while considering high fidelity and the extension of the gamut. The selection method is divided into two steps. First, color patches are selected based on their global correlation, i.e. their relation to seed patches on the gray axis, and become the reference for correlation. However, even though a selected color patch may have a similar overall distribution to the seed patch, if the correlation factor is smaller than the correlation factors for neighboring patches, the color patch needs to be reselected. Therefore, in the second step, the color patch is reselected based on the local correlation with color patches that have a lower correlation factor with the seed patch. Thus, to reselect the color patch, the seed patch is changed to the average distribution of eight neighboring selected color patches, and the new color patch selected considering the new correlation factor. Consequently, the selected color patches have a similar distribution to their neighboring color patches. The selected color patches are then measured for accuracy, and the relation between the digital value and the tristimulus value for the color patches stored in a lookup table. As a result of this characterization, the gamut is extended in the dark regions and the color difference reduced compared to conventional characterization methods.