Go Tanaka

Image enhancement based on multiple parametric sigmoid functions

In a digital still camera system, a dynamic range is usually compressed linearly in logarithmic space. Therefore, the images taken by the digital still camera tend to be low contrast, and give us a flat impression without the liveliness in comparison with the conventional silver halide photographs. In this paper, we propose a new digital image enhancement method by using multiple parametric sigmoid functions. In the proposed method, intensity conversions by using parametric sigmoid functions are carried out based on the information of local regions with various sizes. And, by synthesizing each conversion result, the fine image enhancement is achieved. The effectiveness of the proposed method is verified by applying it to some images.

Minimum spanning tree-based random-valued impulse noise detection for a switching median filter

A switching median filter is effective for impulse noise elimination while preserving edges and details of an image. In the switching median filter an impulse noise detector is employed before filtering, and the detection result is used to control whether a pixel should be filtered or not. However, the conventional impulse detector tends to misjudge noise-free pixels constructing line structures to be the noises. We propose a new random-valued impulse noise detector based on the minimum spanning tree, and it is applied to the switching median filtering to eliminate the impulse noise effectively even for the image including line structures. Through the experiments, the effectiveness of the proposed random-valued impulse noise detector is illustrated.

Color removal method preserving local contrast

A usual color-to-gray conversion only considers lightness components, and differences of colors are sometimes not reflected in monochrome images because three-dimension data which is RGB color components is converted in one-dimensional gray level. Gooch et al. have proposed a color removal method that considers the difference of colors which is named as the Color2Gray algorithm. Although the Colo2Gray algorithm can obtain good results in many cases, we can still improve it. In this paper, we propose a new color-to-gray conversion which attaches importance to the local contrast improvement. The proposed method evaluates the local contrast by computing the local variance of pixel values in an input color image and using it, high contrast monochrome image is obtained. Experiments show the effectiveness of the proposed method.

Noise reduction based on iterative hybrid vector filter and detail correction employing correlation among RGB components

Abstract One of the important image processing tasks is to effectively reduce a noise from a digital image while keeping its features intact. In this paper, a new noise removal method for color images corrupted by the mixture of the impulsive and Gaussian noises is proposed. In the proposed method, firstly, a tentative output image, in which the noise is removed almost perfectly, is obtained by using the iterative robust switching vector median-based vector e - filter , which is realized by hybridizing the robust switching vector median filter and the vector e - filter and is newly proposed here. Then the residual components between the input and the tentative output images are calculated, and image components constituting edges, corner and other image details are extracted from the residual components by using the correlation characteristic in RGB components. Consequently, a final output is obtained by adding the extracted image components into the tentative output image. The effectiveness and the validity of the proposed method are verified by some experiments using the natural color images.

Visibility improvement of undiscriminatable colors by modification of yellow-blue components for protanopia and deuteranopia

Needless to say, the color information plays an important role in the human visual information processing system. However, the color is not be always perceived equally by all the people. There are some people with color vision deficiency, such as dichromacy. They feel the difficulty to recognize and distinguish certain color combinations. In this paper, a visibility improvement method realizing the barrier-free color vision is proposed especially for the protanopia and deuteranopia. The effectiveness of the proposed method is verified by some experiments.

Invertible color-to-monochrome transformation based on clustering with lightness constraint

To generate a monochrome image from a color one, usually, lightness components of the color image are extracted and used. This transformation is generally not invertible and it is impossible to reconstruct the original color information from a yielded monochrome image. In this paper, we propose an invertible color-to-monochrome transform method obtaining the monochrome image which contains the color information and enables us to reconstruct a color image. In the proposed method, colors in an input image are clustered by k-means-based algorithm with a lightness constraint, and the output monochrome image is obtained using the clustering result.

An analysis of color removal method based on a signed color distance and its calculation cost reduction

In order to convert a color image into a monochrome one, luminance components of the pixels have been used as gray-levels for the representation of the monochrome image in HDTV standard. However, saliencies of the image embedded only in the chrominance components are disappeared in the monochrome image converted by using luminance components. To cope with this problem, A.A. Gooch et al. have proposed the salience-preserving color removal method called ldquoColor2Gray.ldquo In this paper, at first, Color2Gray is analyzed and its mathematical property is revealed. Then, fast Color2Gray algorithm is proposed by use of the mathematical property. Finally, some experiments prove the effectiveness of the proposed algorithm.

Color removal method based on signed color distance and nonlinear projection

In the typical photo re-touching software, in order to convert a color image into a monochrome one, only luminance components of the color image are extracted, and are used to represent the monochrome image. This luminance extraction is useful and enough for obtaining a monochrome image in most cases. However, saliencies of the image included only in the chrominance components are not preserved at all. In this study, we propose a new color removal method that takes difference of the chrominance into account in addition to luminance of an image. The effectiveness of the proposed method is verified by applying it to some images.

Examination of effects of chromatic adaptation on color discrimination threshold in dichromatic color appearance

It is important to know the color appearance for dichromats to improve their visual environment generally. To represent the color appearance, the Brettel model has been used. A comparison of the Brettel model and a model that is modified by considering chromatic adaptation is carried out in this study. The compassion was carried out using combinations of colors with a very small difference to determine the color discrimination threshold. Through experiments in which a dichromat and trichromats participated, we conclude that there is no essential difference between the two models.

A pixel value restoration considerng line stucture for impulse noise removal

In this paper, a pixel value restoration method for images corrupted by impulse noise is proposed. For example, the median filter is a well-known solution for impulse noise removal. However, it cannot process well at line structures in images. We therefore propose an effective method in the view point of line structure restoration. The proposed method considers line structures in four angles. The key point of the method is to consider the two adjacent lines of the line which pass through the target pixel.