Showing papers on "Histogram equalization published in 1989"

Method of highlighting subtle contrast in graphical images

Abstract: A display for medical diagnostic equipment produces an image of the subject under study and a histogram image which indicates the distribution of brightness levels of the image pixels. Using a trackball, the operator manipulates a contrast window which is displayed on the histogram and which enables the operator to select brightness ranges in the image for contrast enhancement.

A psychophysical comparison of two methods for adaptive histogram equalization.

Abstract: Adaptive histogram equalization (AHE) is a method for adaptive contrast enhancement of digital images. It is an automatic, reproducible method for the simultaneous viewing of contrast within a digital image with a large dynamic range. Recent experiments have shown that in specific cases, there is no significant difference in the ability of AHE and linear intensity windowing to display gray-scale contrast. More recently, a variant of AHE which limits the allowed contrast enhancement of the image has been proposed. This contrast-limited adaptive histogram equalization (CLAHE) produces images in which the noise content of an image is not excessively enhanced, but in which sufficient contrast is provided for the visualization of structures within the image. Images processed with CLAHE have a more natural appearance and facilitate the comparison of different areas of an image. However, the reduced contrast enhancement of CLAHE may hinder the ability of an observer to detect the presence of some significant gray-scale contrast. In this report, a psychophysical observer experiment was performed to determine if there is a significant difference in the ability of AHE and CLAHE to depict gray-scale contrast. Observers were presented with computed tomography (CT) images of the chest processed with AHE and CLAHE. Subtle artificial lesions were introduced into some images. The observers were asked to rate their confidence regarding the presence of the lesions; this rating-scale data was analyzed using receiver operating characteristic (ROC) curve techniques. These ROC curves were compared for significant differences in the observers' performances. In this report, no difference was found in the abilities of AHE and CLAHE to depict contrast information.

Digital Image Processing For Flow Visualization

Abstract: The digital image processing for flow visualization pictures, including air-bubble, smoke tunnel, schlieren and interference are described. The ways for calculating data quantitatively also provided. In this paper the histogram equalization, spatial convolution, exponential transform and Wallis transform are introduced.

Symbolic-substitution-based operations using holograms: multiplication and histogram equalization

Abstract: Holographic content-addressable memory is used in an optical symbolic-substitution-based system to realize an efficient and high speed optical multiplier. Such a system is also capable of performing image processing operations. Consequently, it is used to obtain a fast gray-level histogram and histogram equalization of an input image.

Histogram Equalization Utilizing Spatial Correlation For Image Enhancement

Abstract: Histogram equalization (HE) techniques are widely used for image enhancement due to their simplicity and effectiveness. Most of the existing HE techniques assume image pixels to be randomly distributed over the image space. In general, adjacent image pixels are highly correlated, it is more reasonable to design HE methods that utilize this correlation. In this paper, we present a method of HE that takes spatial correlation among pixels into account. The concurrence of the gray values of adjacent pixels is calculated to form conditional probabilities of each gray level with respect to other gray levels in the image. The HE is then obtained using these probabilities. We call this method Conditional Histogram Equalization (CHE). Experimental results show that the proposed method generates images that are visually more pleasant than the ones generated by conventional HE techniques. The results also show that this method avoids the problem of over stretching the contrast in images with highly peaked histograms.

Digital method of modifying an image contrast so as to retain imaging of small objects

Abstract: The method has for its object to enhance the contrast of an image, more specifically a low-contrast infrared image. The method is used in a system for putting the method into effect, more specifically an infrared camera operating in real time. The output image is structured with the aid of a transformation function (FI) one part of which (RCI) transforms the contrast of the warm points; the output histogram (HSI) has a higher contrast than the input histogram (HE). The point GO is determined for each image as a function of the characteristics of the input histogram (HE).

Color image processing apparatus with color ghost correction

Abstract: Disclosed is an image processing apparatus for processing a color image signal obtained by converting color image information on a document into an electric signal. In the apparatus, a color ghost is corrected from the color of the color image signal in accordance with a predetermined correction manner. When a marked region on the document with a coloring member different from the color of the document is detected and an image process is performed in relation to the marked region, the predetermined correction manner is changed.

Continuous Luminance Pseudocolor Mappings For Infrared Thermography

Abstract: A suitably-calibrated infrared thermogram contains both qualitative information about the temperature distribution and quantitative information about the numerical temperature valhes A grey-scale rendering shows the temperature distribution well for human interpretation but cannot be used to obtain numerical values Conversely, a density-sliced pseudocolor rendering permits semi-quantitative interpretation but often obscures fine details of the temperature distribution A single rendering which provides both would be useful Color perception includes the three distinct dimensions of brightness, hue, and saturation; these may vary independently It is thus possible to construct a pseudocolor mapping in which brightness varies continuously but hue and saturation vary stepwise If applied to a thermogram, the result is a grey-scale image with superimposed color tints organized into contours The grey-scale image shows the temperature distribution clearly, and the tints correspond to temperature contours for semi-quantitative interpretation The grey-scale image may be enhanced before temperature contours are applied Point operations such as contrast stretching or histogram equalization increase visibility of detail but retain a one-to-one correspondence between grey level and temperature Neighborhood operations such as unsharp masking or gradient filtering are also possible These operations destroy the one-to-one correspondence so temperature contouring becomes essential to portray even semi-quantitative temperature information

Picture contrast processor

Abstract: PURPOSE:To automatically perform contrast processing so as to make a picture easiest to see by adjusting automatically histogram equalization. CONSTITUTION:When a histogram is compressed in the direction of the number of picture elements by a compressor 2, and the difference of the integrated values of the histogram before and after compression is obtained by a subtracter 3, the total number of compressed picture elements is obtained. The mean value of them is obtained, and by adding it by an adder 4 by biasing it equally to the histogram after the compression, the new histogram is generated. When the histogram equalization of the picture is performed by this new histogram, only a usual histogram equalization is performed in case of the picture where no peak of the histogram is present. On the contrary, when the histogram is only the peak linear gradation conversion is performed, and in case of intermediate, the gradation conversion intermediate between the usual histogram equalization and the linear conversion is performed corresponding to its degree.

Colormap Based Image Display Enhancement

Abstract: A number of well developed image enhancement techniques, basically categorized into global image processing and adaptive image processing, [1, 2] have been widely used in NDE applications, which help to present high quality images conveying information on voids, cracks and inclusions in samples. These enhancements apply certain algorithms on image data and change the values to generate particular effects. Since the visual effect of an image is determined by two factors, i.e. image data (pixel value) and colormap which assign color display for each intensity covering the whole range, the enhancement of the display of an image can be achieved by working on the colormap for a pseudo-color display without actually affecting the image data. A grey scale display can be treated as a special case of pseudo-color. With the advent of high quality color monitors and color output devices available for the scientific workstations whose colormaps can be easily redefined by users, it often is more efficient to manipulate the colormaps to achieve the desired visual effect. Since a typical image file with 8 bits/pixel data can easily contain 250,000 bytes while a colormap for the display of such image requires only 768 bytes for 24 bits/color (corresponding to 16.7 million different shades of colors), the gain in speed obtained by adjusting the colormap rather than the data can be very dramatic.