Showing papers on "Histogram equalization published in 1997"

Image indexing using color correlograms

Abstract: We define a new image feature called the color correlogram and use it for image indexing and comparison. This feature distills the spatial correlation of colors, and is both effective and inexpensive for content-based image retrieval. The correlogram robustly tolerates large changes in appearance and shape caused by changes in viewing positions, camera zooms, etc. Experimental evidence suggests that this new feature outperforms not only the traditional color histogram method but also the recently proposed histogram refinement methods for image indexing/retrieval.

Contrast enhancement using brightness preserving bi-histogram equalization

Abstract: Histogram equalization is widely used for contrast enhancement in a variety of applications due to its simple function and effectiveness. Examples include medical image processing and radar signal processing. One drawback of the histogram equalization can be found on the fact that the brightness of an image can be changed after the histogram equalization, which is mainly due to the flattening property of the histogram equalization. Thus, it is rarely utilized in consumer electronic products such as TV where preserving the original input brightness may be necessary in order not to introduce unnecessary visual deterioration. This paper proposes a novel extension of histogram equalization to overcome such a drawback of histogram equalization. The essence of the proposed algorithm is to utilize independent histogram equalizations separately over two subimages obtained by decomposing the input image based on its mean with a constraint that the resulting equalized subimages are bounded by each other around the input mean. It is shown mathematically that the proposed algorithm preserves the mean brightness of a given image significantly well compared to typical histogram equalization while enhancing the contrast and, thus, provides a natural enhancement that can be utilized in consumer electronic products.

Comparing images using color coherence vectors

Abstract: Color histograms are used to compare images in many applications. Their advantages are efficiency, and insensitivity to small changes in camera viewpoint. However, color histograms lack spatial information, so images with very different appearances can have similar histograms. For example, a picture of fall foliage might contain a large number of scattered red pixels; this could have a similar color histogram to a picture with a single large red object. We describe a histogram-based method for comparing images that incorporates spatial information. We classify each pixel in a given color bucket as either coherent or incoherent, based on whether or not it is part of a large similarly-colored region. A color coherence vector (CCV) stores the number of coherent versus incoherent pixels with each color. By separating coherent pixels from incoherent pixels, CCV’s provide finer distinctions than color histograms. CCV’s can be computed at over 5 images per second on a standard workstation. A database with 15,000 images can be queried for the images with the most similar CCV’s in under 2 seconds. We show that CCV’s can give superior results to color his∗To whom correspondence should be addressed tograms for image retrieval.

A Comparison of the Multiscale Retinex With Other Image Enhancement Techniques

Abstract: The multiscale retinex with color restoration (MSRCR) has shown itself to be a very versatile automatic image enhancement algorithm that simultaneously provides dynamic range compression, color constancy, and color rendition. A number of algorithms exist that provide one or more of these features, but not all. In this paper we compare the performance of the MSRCR with techniques that are widely used for image enhancement. Specifically, we compare the MSRCR with color adjustment methods such as gamma correction and gain/offset application, histogram modification techniques such as histogram equalization and manual histogram adjustment, and other more powerful techniques such as homomorphic filtering and ''burning and dodging''. The comparison is carried out by testing the suite of image enhancement methods on a set of diverse images. We find that though some of these techniques work well for some of these images, only the MSRCR performs universally well on the test set.

Color clustering techniques for color-content-based image retrieval from image databases

Abstract: Image retrieval based on color content is an auxiliary function for traditional text-annotated image databases. Most color-based image retrieval systems adopt color histograms as the feature of color content. One of the most important steps in these systems is to reduce histogram dimensions with the least loss in color content. A good clustering technique is vital for this purpose. This paper examines the color conservation property by applying different clustering techniques in perceptually uniform color spaces and different images. For studying color spaces, the perceptual uniform spaces, such as Mathematical Transformation to Munsell system (MTM) and C.I.E. L*a*b*, are investigated. For evaluating clustering techniques, the equalized quantization approach, the hierarchical clustering approach, and the Color-Naming-System (CNS) supervised clustering approach are studied. For analyzing color loss, the error bound, the quantized error in color space conversion, and the average quantized error of 400 color images are explored. An image retrieval application based on color content is shown to demonstrate the difference in applying these clustering techniques. These simulation results suggest that good clustering techniques usually lead to more effective retrieval.

Image enhancing method and circuit using mean separate/quantized mean separate histogram equalization and color compensation

Abstract: In an image enhancement method and a circuit therefor, a luminance signal is extracted from input color signals. The luminance signal, extracted in screen units, is divided into subimages according to the mean value of the luminance signal. The gray level histograms of the divided subimages are independently equalized, and an adjusted luminance signal is output. The color signals are varied on the basis of the adjusted luminance signal, and compensated color signals are output. Accordingly, an abrupt variation in brightness and an artifact are effectively reduced, image contrast is enhanced, and an undistorted color signal is provided at the same time. Also, in order to reduce the hardware of the circuit, an input luminance image is quantized, the quantized image is divided into a predetermined number of quantized subimages on the basis of the mean of the quantized image, and an independent histogram equalization is performed on each of the quantized subimages.

Image enhancing method using mean-separate histogram equalization and a circuit therefor

Abstract: An image enhancing method using a mean-separate histogram equalization method is disclosed. An input image signal is divided into two sub-images in a picture unit according to a mean level of the input image signal. Then, cumulative density functions for each of the sub-images are calculated. Afterwards, the input sample is mapped into two gray levels, each of which belongs to a first range and a second range, by using first and second transform functions defined by use of the cumulative density functions, respectively. Finally, one of two mapped levels is selected depending on the magnitude of the input sample. As a result, the brightness of the given image can be preserved while the contrast is enhanced.

Method for image enhancing using quantized mean-separate histogram equalization and a circuit therefor

Abstract: A histogram equalization method for enhancing an image is provided and includes the steps of: quantizing the level of input image signals; quantizing a mean level of quantized input image signals by a unit of a picture; splitting quantized input image signals into a predetermined number of quantized sub-images according to a quantized mean level; calculating a cumulative density function value of each quantized sub-image; interpolating the cumulative density function value of each sub-image according to input image signals and the cumulative density function value of each quantized sub-image; and independently histogram-equalizing each quantized sub-image according to the interpolated cumulative density function value of each quantized sub-image, to thereby enhance the contrast and preserve the overall brightness of a given image as well as simplify the circuit hardware.

Image enhancement circuit and method using mean matching/quantized mean matching histogram equalization and color compensation

Abstract: In an image enhancement method and circuit, a luminance signal is extracted from input color signals. Histogram equalization is performed using a cumulative density function of the extracted luminance signal which is input in a screen unit. A transform function is controlled to map a mean level of the extracted luminance signal to itself, so that an adjusted luminance signal is output. The color signals are varied according to the variation of the luminance signals to output compensated color signals. Thus, image contrast is enhanced, and an undistorted color signal is provided at the same time. In order to reduce the hardware of the circuit, an interpolated cumulative density function is obtained by interpolating a quantized cumulative density function of an input luminance image and is used as a transform function. The transform function is controlled to map a mean level of the input luminance image to itself.

Multiscale adaptive gain control of IR images

Abstract: IR imagery tends to have a higher dynamic range then typical display devices such as a CRT. Global methods such as stretching and histogram equalization improve the visibility of many images, but some information in the images stays hidden for a human operator. This paper reports about the possibility to represent more information by using local adaptive techniques. Based upon methods found in literature, a new multi-scale method is presented.

Image quality enhancing method using mean-matching histogram equalization and a circuit therefor

Abstract: A histogram-equalization method and circuit for enhancing an image signal represented by a predetermined number of gray levels is presented. The method calculates a cumulative density function and a mean level in a unit of a picture, and maps an input sample into a new gray level according to a transform function which is defined by use of the cumulative density function and the mean level. The method may also include a step of obtaining a compensated mean level by adding to the mean brightness level, so that the input sample is mapped into the compensated mean level according to the mean level. Thus, a brightness compensation may be carried out along with the contrast enhancement of the image.

Image process apparatus and method

Abstract: There is provided an image processing method for generating a color image by using a color palette table, comprising the steps of a restriction step of restricting colors capable of being registered in the palette table, on the basis of gamut data of an image output means for outputting the color image, a formation step of forming the color palette table on the basis of color restriction obtained in the restriction step, and a generation step of generating the color image by using the colors registered in the color palette table, whereby excellent color matching between the color on a monitor and a color on a printer can be obtained.

Histogram-equalization-based adaptive image sensor for real-time vision

Abstract: Image sensing and coding are essential to vision machines. This paper presents an adaptive image sensor by using in situ histogram equalization. With this histogram-equalization-based adaptation, this image sensor can adapt automatically to different lighting conditions without frame delay and provides a constant image signal excursion range. A CMOS prototype chip of 64/spl times/64-pixel is presented where each pixel contains ten transistors and occupies 50/spl times/50 /spl mu/m/sup 2/ in a classic 1-/spl mu/m double-metal single-poly process. The experimental results confirm an adaptation over illumination variation of 1:5000.

Image enhancement method and circuit using quantized mean-matching histogram equalization

Abstract: An image enhancement circuit and a method therefor including the steps of quantizing the level of an input image signal, obtaining a cumulative density function on the basis of a gray level distribution of a screen unit with respect to the quantized image signal, outputting a cumulative density function value interpolated on the basis of the quantized cumulative density function value, calculating the mean level of the input image signal in a screen unit, and controlling a transform function to map the input image signal to a gray level and map the mean level to itself, by using the interpolated cumulative density function as the transform function. In addition, the image enhancement method includes the steps of outputting a compensated mean level by adding a corrected value depending on a predetermined correction function to the mean level, and controlling the interpolated cumulative density function as a transform function to map an input image signal to a new gray level on the basis of the interpolated cumulative density function value and to map the mean level to the compensated mean level. Thus, contrast is enhanced, and the mean brightness of a given image is continuously maintained.

Shape preserving local contrast enhancement

Abstract: A novel approach for shape preserving contrast enhancement is presented. Contrast enhancement is achieved by means of a local histogram equalization algorithm which preserves the level-sets of the image. This basic property is violated by common local schemes, thereby introducing spurious objects and modifying the image information. The scheme is based on equalizing the histogram in all the connected components of the image, which are defined based on the image grey-values and spatial relations between its pixels. Following mathematical morphology, these constitute the basic objects in the scene. We give examples for both grey-valued and color images.

Image enhancing method using lowpass filtering and histogram equalization and a device therefor

Abstract: In an image enhancing method, an input image signal is lowpass filtered and then histogram equalized to become a contrast-enhanced signal. Then, the lowpass filtered signal is subtracted from the input image signal. Afterwards, the subtracted value is added to the contrast enhanced signal, and the added result is output as an image enhanced output signal. Thus, the contrast of a given image signal is improved without an increase in background noise.

Histogram equalization apparatus for contrast enhancement of moving image and method therefor

Abstract: A histogram equalization apparatus for contrast enhancement of a moving image includes a CDF calculator for counting the number of pixels having a gray level from the minimum to the maximum or less with respect to an input image in a frame unit to calculate a cumulative distribution function (CDF) value of each gray level, and a look-up table for updating histogram-equalized level corresponding to each gray level of the input moving image in a frame unit based on the CDF value of each gray level, and outputting a corresponding histogram-equalized level according to a level of the input moving image. Therefore, the histogram equalization apparatus can perform real-time histogram equalization on a moving image with a simple hardware, without using a frame memory, a probability density function (PDF) calculator and dividers for a cumulative density function (CDF) calculation.

System and method for scanning medical images using adjustable exposure time and brightness

Abstract: A system and method for diagnosis of living tissue diseases is described. The system includes a computer device for controlling its operation. An operator control device is coupled to the computer device. A viewing screen is coupled to the computer device for displaying digitized images of the living tissue. The operator, using the control device, selects desired portions of the digitized image for further image enhancement according to a desired image enhancement feature selectable from a plurality of image enhancement features. The image enhancement features include any combination of grey scale stretching, contrast enhancement based on logarithmic histogram equalization, spot enhancement and magnification. The system further includes means for visualization and quantification of micro-calcifications, and means for visualization and quantification of mass spiculations.

Quantized bi-histogram equalization

Abstract: Histogram equalization is a widely used scheme for contrast enhancement in a variety of applications due to its simple function and effectiveness. One possible drawback of the histogram equalization is that it can change the mean brightness of an image significantly as a consequence of histogram flattening. Clearly, this is not a desirable property when preserving the original mean brightness of a given image is necessary. As an effort to overcome such drawback for extending the applications of the histogram equalization in consumer electronic products, bi-histogram equalization has been proposed by the author which is capable of preserving the mean brightness of an image while it performs contrast enhancement. The essence of the bi-histogram equalization is to utilize independent histogram equalizations separately over two subimages obtained by decomposing the input image based on its mean. A simplified version of the bi-histogram equalization is proposed, which is referred to as the quantized bi-histogram equalization. The proposed algorithm provides a much simpler hardware (H/W) structure than the bi-histogram equalization since it is based on the cumulative density function of a quantized image. Thus, the realization of bi-histogram equalization in H/W is feasible, which leads to versatile applications in the field of consumer electronics.

Image segmentation method

Abstract: A method of producing a simulated 3-dimensional image, the method comprising: assimilating 2-dimensional image data, e.g. from MR image slices; assigning a feature, e.g. grey scale value to each datum; reducing the resolution of each grey scale value of the data; generating a histogram of the reduced resolution grey scale data; performing a fast fuzzy c-means clustering on the histogram data. The resolution of each pixel object in the images may be reduced from 12 bit to 8 bit resolution, and the histogram may be generated from these. Subsequently a value may be assigned for each entry in the histogram, each entry value being equal to the number of objects of any given feature (e.g. grey scale) in the reduced resolution (8 bit) image. The image may be displayed using a novel colour blending technique. A 3D image is produced quickly and without supervisory intervention and can be used in endoscopic surgery and in diagnostic methods as well as in understanding healthy anatomical features better.

Image enhancement method using histogram equalization

Abstract: In an image enhancement method is disclosed using a histogram equalization for an input image expressed in a predetermined number of gray levels. While calculating the probability density function of the gray levels of the input image, for use in a histogram equalization, the number of occurrences of each gray level are constrained not to exceed a predetermined value. Then a histogram equalization is performed on the input image based on the calculated probability density (or distribution) function. As a result, the mean brightness of the input image does not change significantly by the histogram equalization. Additionally, noise is prevented from being greatly amplified.

Video-image histogram equalization circuit and method therefor

Abstract: A histogram equalization circuit includes a calculator and a mapper. The calculator calculates cumulative distribution function (CDF) values for the respective gray levels with respect to an input image corresponding to an area signal of a predetermined period. The mapper maps the input image of a predetermined period to a new gray level based on the CDF values of the respective gray levels. The present invention obtains a CDF value by selecting a field period or a frame period as a CDF calculation area and performs a histogram equalization the image signal of the selected period based on the CDF value. Calculation of the CDF is performed by direct comparison of the pixels of the image, thus eliminating the need to calculate a probability density function for the gray levels. Therefore, it is possible to obtain a high correlation between the input data and the equalized data and to reduce the quantity of hardware.

Adaptive contrast enhancement for color images

Abstract: Although the human eye can discern thousands of color shades and intensities compared to about two dozen shades of gray, there is a need for the enhancement of color images In this paper an extension of the adaptive contrast enhancement method of Dash and Chatterji (1991) for colour images using RGB components is described The algorithm is given and results are presented

Image file, generation method thereof, and image display apparatus

Abstract: An image file for recording natural images, a generation method of the image file and an image display apparatus for displaying image data from this image file. The image file includes image data obtained by digitizing an original, and conversion tables for separating the color tones of the original into R, G and B and converting the changes of the color tones to input luminance-v-output luminance. The image display apparatus controls a color look-up table by the conversion tables read out from the image file, and displays the image data read out from the image file. The generation method of the image file includes reading development data by reading the first original by a scanner, generating a histogram between luminance and appearance frequency for each of R, G and B, integrating the appearance frequency to obtain a first cumulative histogram, generating similarly the second cumulative histogram for each of R, G and B of the final original to obtain conversion tables for the second histogram with the first cumulative histogram being the reference, and continuously displaying the color tones of a still image by using the image data and the conversion tables of the first original.

Contrast enhancement of IRS-1C LISS data

Abstract: Histogram modification technique is regularly applied to improve contrast and feature discrimination. The spectral components of any feature are mutually dependent and any histogram overlooking this aspect will not provide scope for multispectral data utilisation. The remote sensing satellite IRS-1C has three sensors on board, namely, Wide Field Imaging Sensor (WIFS), LISS-III and PAN Sensor, with varying spatial and spectral resolutions. The maintenance of relative intensity levels is a major casuality in the case of histogram equalization of low contrast images of high resolution sensor LISS-III. This results in some sort of defocussing of image for low contrast original data. An approach that is possible to apply without causing any image with patchiness is described. Basically, the idea underlying this approach is to perform three independent transformations on the gray level of any input pixel and manipulate the histogram while performing the histogram equalization. Thus the approach adopted in the study is supposed not to alter the spectral signature as may be possible in automated histogram equalization.

Directional detail histogram for image retrieval

Abstract: The authors propose a new indexing technique, for image retrieval, which calculates the histogram of the directional detail content in a given image. A multiresolution analysis is applied to extract directional information which is then mapped into three-dimensional vectors and presented as a histogram. This allows the use of histogram query techniques to be readily applied for retrieval.

Method for enhancing image using quantized mean-separate histogram equalization and circuit therefor

Abstract: A histogram equalization method for enhancing an image is provided and includes the steps of: quantizing the level of input image signals; quantizing a mean level of quantized input image signals by unit of a picture; splitting quantized input image signals into a predetermined number of quantized sub-images according to a quantized mean level; calculating a cumulative density function value of each quantized sub-image; interpolating the cumulative density function value of each sub-image according to input image signals and the cumulative density function value of each quantized sub-image; and independently histogram-equalizing sub-images according to the interpolated cumulative density function value of each sub-image, to thereby enhance the contrast and preserve the entire brightness of a given image as well as simplify the circuit hardware.

Video camera equipment, video signal processing unit and gradation conversion method for video signal

Abstract: PROBLEM TO BE SOLVED: To use effectively a limited dynamic range by compressing a gradation area not in use with priority. SOLUTION: An accumulated histogram is normalized with respect to signal level distribution information (histogram table) detected for a valid video period of an input video signal to obtain the normalized accumulation distribution, that is, the amplitude transmission characteristic of complete histogram equalization (conversion information denoting cross reference between a level of an input video signal and a level of an output video signal). Then the amplitude transmission characteristic (solid lines (a) in figure (d)) is adjusted to obtain the amplitude transmission characteristic (solid lines (c) in figure (d)) by adjusting the histogram equalization. Then an offset BOF of a black level code is subtracted from the entire characteristic to store the black level code (broken lines (d) in figure (e)). Then peak storage processing is conducted so that a luminance level of input luminance A is unchanged regardless of conversion to obtain the amplitude transmission characteristic (solid lines (e) of figure (f)) for adaptive gradation conversion by histogram equalization. The amplitude transmission characteristic is used for a gain of luminance to apply luminance conversion to the input video signal.

Adaptive gamma corrector using integration look-up table

Abstract: PROBLEM TO BE SOLVED: To decide gamma correction from a complicated distribution histogram in real time without expanding the scale of a hardware by providing a matrix part, histogram extraction part, histogram integration part, integration look-up table(LUT) and gamma decision part as specified ones respectively. SOLUTION: A matrix part 60 receives the R, G and B signals of an image signal and outputs luminance signals. While using the luminance signals outputted from the matrix part 60, a histogram extraction part 62 extracts the distribution histogram of luminance signals. While using the histogram outputted from the histogram extraction part 62, a histogram integration part 64 integrates it. While using the output of the histogram integration part 64, an integration LUT 66 corrects the inputted image signal into an image suitable for a current image. A gamma decision part 68 decides the gamma from the integration LUT 66 and outputs the corrected image.