Showing papers on "Histogram equalization published in 2011"

Contextual and Variational Contrast Enhancement

Abstract: This paper proposes an algorithm that enhances the contrast of an input image using interpixel contextual information. The algorithm uses a 2-D histogram of the input image constructed using a mutual relationship between each pixel and its neighboring pixels. A smooth 2-D target histogram is obtained by minimizing the sum of Frobenius norms of the differences from the input histogram and the uniformly distributed histogram. The enhancement is achieved by mapping the diagonal elements of the input histogram to the diagonal elements of the target histogram. Experimental results show that the algorithm produces better or comparable enhanced images than four state-of-the-art algorithms.

Survey of Contrast Enhancement Techniques based on Histogram Equalization

Abstract: This Contrast enhancement is frequently referred to as one of the most important issues in image processing. Histogram equalization (HE) is one of the common methods used for improving contrast in digital images. Histogram equalization (HE) has proved to be a simple and effective image contrast enhancement technique. However, the conventional histogram equalization methods usually result in excessive contrast enhancement, which causes the unnatural look and visual artifacts of the processed image. This paper presents a review of new forms of histogram for image contrast enhancement. The major difference among the methods in this family is the criteria used to divide the input histogram. Brightness preserving Bi-Histogram Equalization (BBHE) and Quantized Bi-Histogram Equalization (QBHE) use the average intensity value as their separating point. Dual Sub-Image Histogram Equalization (DSIHE) uses the median intensity value as the separating point. Minimum Mean Brightness Error Bi-HE (MMBEBHE) uses the separating point that produces the smallest Absolute Mean Brightness Error (AMBE). Recursive Mean-Separate Histogram Equalization (RMSHE) is another improvement of BBHE. The Brightness preserving dynamic histogram equalization (BPDHE) method is actually an extension to both MPHEBP and DHE. Weighting mean-separated sub-histogram equalization (WMSHE) method is to perform the effective contrast enhancement of the digital image.

Histogram Equalization Techniques For Image Enhancement

Abstract: Various enhancement schemes are used for enhancing an image which includes gray scale manipulation, filtering and Histogram Equalization (HE). Histogram equalization is one of the well known imaget enhancement technique. It became a popular technique for contrast enhancement because this method is simple and effective. In the latter case, preserving the input brightness of the image is required to avoid the generation of non-existing artifacts in the output image. Although these methods preserve the input brightness on the output image with a significant contrast enhancement, they may produce images with do not look as natural as the input ones. The basic idea of HE method is to re-map the gray levels of an image. HE tends to introduce some annoying artifacts and unnatural enhancement. To overcome these drawbacks different brightness preserving techniques are used which are covered in the literature survey. Comparative analysis of different enhancement techniques will be carried out. This comparison will be done on the basis of subjective and objective parameters. Subjective parameters are visual quality and computation time and objective parameters are Peak signalto-noise ratio (PSNR), Mean squared error (MSE), Normalized Absolute Error (NAE), Normalized Correlation, Error Color and Composite Peak Signal to Noise Ratio (CPSNR).

A Novel 3-D Color Histogram Equalization Method With Uniform 1-D Gray Scale Histogram

Abstract: The majority of color histogram equalization methods do not yield uniform histogram in gray scale. After converting a color histogram equalized image into gray scale, the contrast of the converted image is worse than that of an 1-D gray scale histogram equalized image. We propose a novel 3-D color histogram equalization method that produces uniform distribution in gray scale histogram by defining a new cumulative probability density function in 3-D color space. Test results with natural and synthetic images are presented to compare and analyze various color histogram equalization algorithms based upon 3-D color histograms. We also present theoretical analysis for nonideal performance of existing methods.

Short Communication: Histogram Modified Local Contrast Enhancement for mammogram images

Abstract: Early detection of breast cancer in the mammograms is very essential in the field of medicine Contrast enhancement of mammograms based on Histogram Equalization (HE) is presented Histogram equalization is an effective and simple technique for contrast enhancement The standard histogram equalization (HE) usually results in excessive contrast enhancement because of lack of control on the level of enhancement The Histogram Modified Local Contrast Enhancement (HM-LCE) is introduced in this paper to adjust the level of contrast enhancement, which in turn gives the resultant image a strong contrast and also brings the local details present in the original image for more relevant interpretation It incorporates a two stage processing both histogram modifications as an optimization technique and a local contrast enhancement technique This method is tested for Mias mammogram images The performance of this method is determined using three parameters like Enhancement Measure (EME), Absolute Mean Brightness Error (AMBE) and Discrete Entropy (H) for all 22 numbers of Mias mammogram images with microcalcification It's enhancement potential is also tested by sobel and otsu methods for the detection of microcalcification in the mammogram image From the subjective and quantitative measures it is interesting that this proposed technique provides optimum results by giving better contrast enhancement and preserving the local information of the original mammogram images in the Mias data base and the method has increased the detectability of micro calcifications present in the given mammogram image

A Linear Programming Approach for Optimal Contrast-Tone Mapping

Abstract: This paper proposes a novel algorithmic approach of image enhancement via optimal contrast-tone mapping. In a fundamental departure from the current practice of histogram equalization for contrast enhancement, the proposed approach maximizes expected contrast gain subject to an upper limit on tone distortion and optionally to other constraints that suppress artifacts. The underlying contrast-tone optimization problem can be solved efficiently by linear programming. This new constrained optimization approach for image enhancement is general, and the user can add and fine tune the constraints to achieve desired visual effects. Experimental results demonstrate clearly superior performance of the new approach over histogram equalization and its variants.

Automatic Exact Histogram Specification for Contrast Enhancement and Visual System Based Quantitative Evaluation

Abstract: Histogram equalization, which aims at information maximization, is widely used in different ways to perform contrast enhancement in images. In this paper, an automatic exact histogram specification technique is proposed and used for global and local contrast enhancement of images. The desired histogram is obtained by first subjecting the image histogram to a modification process and then by maximizing a measure that represents increase in information and decrease in ambiguity. A new method of measuring image contrast based upon local band-limited approach and center-surround retinal receptive field model is also devised in this paper. This method works at multiple scales (frequency bands) and combines the contrast measures obtained at different scales using Lp-norm. In comparison to a few existing methods, the effectiveness of the proposed automatic exact histogram specification technique in enhancing contrasts of images is demonstrated through qualitative analysis and the proposed image contrast measure based quantitative analysis.

A Variational Model for Histogram Transfer of Color Images

Abstract: In this paper, we propose a variational formulation for histogram transfer of two or more color images. We study an energy functional composed by three terms: one tends to approach the cumulative histograms of the transformed images, the other two tend to maintain the colors and geometry of the original images. By minimizing this energy, we obtain an algorithm that balances equalization and the conservation of features of the original images. As a result, they evolve while approaching an intermediate histogram between them. This intermediate histogram does not need to be specified in advance, but it is a natural result of the model. Finally, we provide experiments showing that the proposed method compares well with the state of the art.

Histogram Specification: A Fast and Flexible Method to Process Digital Images

Abstract: Histogram specification has been successfully used in digital image processing over the years. Mainly used as an image enhancement technique, methods such as histogram equalization (HE) can yield good contrast with almost no effort in terms of inputs to the algorithm or the computational time required. More elaborate histograms can take on problems faced by HE at the expense of having to define the final histograms in innovative ways that may require some extra processing time but are nevertheless fast enough to be considered for real-time applications. This paper proposes a new technique for specifying a histogram to enhance the image contrast. To further evidence our faith on histogram specification techniques, we also discuss methods to modify images, e.g., to help segmentation approaches. Thus, as advocates of these techniques, we would like to emphasize the flexibility of this image processing approach to do more than enhancing images.

An Automated Blood Vessel Segmentation Algorithm Using Histogram Equalization and Automatic Threshold Selection

Abstract: This paper focuses on the detection of retinal blood vessels which play a vital role in reducing the proliferative diabetic retinopathy and for preventing the loss of visual capability. The proposed algorithm which takes advantage of the powerful preprocessing techniques such as the contrast enhancement and thresholding offers an automated segmentation procedure for retinal blood vessels. To evaluate the performance of the new algorithm, experiments are conducted on 40 images collected from DRIVE database. The results show that the proposed algorithm performs better than the other known algorithms in terms of accuracy. Furthermore, the proposed algorithm being simple and easy to implement, is best suited for fast processing applications.

Performance metrics for image contrast

Abstract: In this paper, contrast level of the images are quantified by the two proposed metrics. These metrics are Histogram Flatness Measure (HFM) and Histogram Spread (HS). Computation of these metrics is based on the shape of the histogram. Extensive simulation results reveal that HS is more meaningful than HFM. Low contrast images have low HS value, while high contrast images have higher value of HS. Thus HS metric can be used to distinguish between the images having different contrast level. Accuracy of the metric is also verified for natural and medical images. This metric has broad applications in image retrieval, image database management, visualization, rendering and image classification.

Efficient contrast enhancement using adaptive gamma correction and cumulative intensity distribution

Abstract: This paper proposes an efficient histogram modification method for contrast enhancement, which plays a significant role in digital image processing, computer vision, and pattern recognition. We present an automatic transformation technique to improve the brightness of dimmed images based on the gamma correction and probability distribution of the luminance pixel. Experimental results show that the proposed method produces enhanced images of comparable or higher quality than previous state-of-the-art methods.

Histogram based contrast enhancement for mammogram images

Abstract: Early detection of breast cancer in the mammograms is very essential in the field of medicine. Contrast enhancement for the detection of micro calcification of mammograms based on the Histogram Modified Contrast Limited Adaptive Histogram Equalization (HM-CLAHE) is presented. Histogram equalization is an effective and simple technique for contrast enhancement. The standard histogram equalization (HE) usually results in excessive contrast enhancement because of lack of control on the level of enhancement. The Histogram Modified Contrast Limited Adaptive Histogram Equalization (HM-CLAHE) is proposed in this paper to adjust the level of contrast enhancement, which in turn gives the resultant image a strong contrast and brings the local details for more relevant interpretation. It incorporates both histogram modifications as an optimization technique and Contrast Limited Adaptive Histogram Equalization. This method is tested for Mias mammogram images. The performance of this method is determined using the parameter like Enhancement Measure (EME). From the subjective and quantitative measures it is interesting that this proposed technique provides better contrast enhancement with preserving the local information of the mammogram images.

Content Based Color Image Classification using SVM

Abstract: We propose a novel approach for content based color image classification using Support Vector Machine (SVM). Traditional classification approaches deal poorly on content based image classification tasks being one of the reasons of high dimensionality of the feature space. In this paper, color image classification is done on features extracted from histograms of color components. The benefit of using color image histograms are better efficiency, and insensitivity to small changes in camera view-point i.e. translation and rotation. As a case study for validation purpose, experimental trials were done on a database of about 500 images divided into four different classes has been reported and compared on histogram features for RGB, CMYK, Lab, YUV, YCBCR, HSV, HVC and YIQ color spaces. Results based on the proposed approach are found encouraging in terms of color image classification accuracy.

Comparative Evaluation of Ultrasound Kidney Image Enhancement Techniques

Abstract: Evaluation have been done to different enhancement techniques applied to ultrasound kidney images to see which enhancement techniques is the most suitable techniques that can be applied to the kidney images before segmenting the edge of the kidney. Five common enhancement techniques have been used including the spatial domain filtering, frequency domain filtering, histogram processing, morphological filtering and wavelet filtering. The techniques applied were assessed by few methods which are the observer sensitivity, measuring the image quality by calculating the MSE and PSNR of the image and applying one of the segmentation techniques to the output images. In conclusion, for ultrasound kidney image, if the whole image were taken into consideration (by measuring MSE and PSNR), morphological filtering seems to be the best option in enhancing the image. If the evaluator is concerning more on the kidney edges, enhancement techniques that should be taken into consideration are median filtering and histogram equalization.

A theory of color barcodes

Abstract: There is increased interest in the use of color barcodes to encode more information per area unit than regular, black-and-white barcodes. For example, Microsoft's HCCB technology uses 4 or 8 colors per patch. Unfortunately, the observed color of a surface depends as much on the illuminant spectrum (and other viewing parameters) as on the surface reflectivity, which complicates the task of decoding the content of the barcode. A popular solution is to append to the barcode a “palette” with the reference colors. In this paper, we propose a new approach to color barcode decoding, one that does not require a reference color palette. Our algorithm decodes groups of color bars at once, exploiting the fact that joint color changes can be represented by a low-dimensional space. Decoding a group of bars (a “barcode element”) is thus equivalent to searching for the nearest subspace in a dataset. We also propose algorithms to select subsets of barcode elements that can be decoded with low error probability. Our experimental results show that our barcode decoding algorithm enables substantial information rate increase with respect to system that display a color palette, at a very low decoding error rate.

Image enhancement technique based on improved PSO algorithm

Abstract: In this paper, image enhancement is considered as an optimization problem and improved PSO(Particle Swarm Optimization) is used to solve it. Also in it, parameterized transformation function is used, which uses global and local information of the image. And an objective criterion for measuring image enhancement — taking fully consideration of the entropy of the image and edge information — is proposed. The main job of this paper tries to achieve the best image enhancement function with the help of improved PSO — parameters optimization. Results have been proved out better compared with other enhancement techniques, such as HE(Histogram Equalization), LCS(Linear Contrast Stretching) and GAIE(Genetic Algorithm based on Image Enhancement).

Palm vein recognition based on three local invariant feature extraction algorithms

Abstract: In contrast to minutiae features, local invariant features extracted from infrared palm vein have properties of scale, translation and rotation invariance. To determine how they can be best used for palm vein recognition system, this paper conducted a comprehensive comparative study of three local invariant feature extraction algorithms: Scale Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF) and Affine-SIFT (ASIFT) for palm vein recognition. First, the images were preprocessed through histogram equalization, then three algorithms were used to extract local features, and finally the results were obtained by comparing the Euclidean distance. Experiments show that they achieve good performances on our own database and PolyU multispectral palmprint database.

Underwater image enhancement: using wavelength compensation and image dehazing (WCID)

Abstract: Underwater environments often cause color scatter and color cast during photography. Color scatter is caused by haze effects occurring when light reflected from objects is absorbed or scattered multiple times by particles in the water. This in turn lowers the visibility and contrast of the image. Color cast is caused by the varying attenuation of light in different wavelengths, rendering underwater environments bluish. To address distortion from color scatter and color cast, this study proposes an algorithm to restore underwater images that combines a dehazing algorithm with wavelength compensation (WCID). Once the distance between the objects and the camera was estimated using dark channel prior, the haze effects from color scatter were removed by the dehazing algorithm. Next, estimation of the photography scene depth from the residual energy ratios of each wavelength in the background light of the image was performed. According to the amount of attenuation of each wavelength, reverse compensation was conducted to restore the distortion from color cast. An underwater video downloaded from the Youtube website was processed using WCID, Histogram equalization, and a traditional dehazing algorithm. Comparison of the results revealed that WCID simultaneously resolved the issues of color scatter and color cast as well as enhanced image contrast and calibrated color cast, producing high quality underwater images and videos.

Objective quality evaluation of visible and infrared color fusion image

Abstract: An evaluation for objectively assessing the quality of visible and infrared color fusion image is proposed. On the basis of the consideration that human perception is most sensitive to color, sharpness, and contrast when assessing the quality of color image, we propose four objective metrics: image sharpness metric (ISM), image contrast metric (ICM), color colorfulness metric (CCM), and color naturalness metric (CNM). The ISM is evaluated by image gradient information. The ICM is defined based on both gray and color histogram characteristics. A color chroma metric, as well as a color variety metric based on a color difference gradient, is proposed, respectively, to define the CCM. The CNM is defined by measuring the color distribution's similarity between the fusion image and nature image, which are of the same scene. All the color attributions are computed in the CIELAB color space. Experimental results show that the proposed objective metrics are meaningful and effective on color fusion image evaluation because they correspond well to subjective evaluation.

Non-linear grayscale image enhancement based on firefly algorithm

Abstract: The principal objective of enhancement is to improve the contrast and detail an image so, that the result is more suitable than the original image for a specific application. The enhancement process is a non-linear optimization problem with several constraints. In this paper, an adaptive local enhancement algorithm based on Firefly Algorithm (FA) is proposed. FA represents a new approach for optimization. The FA is used to search the optimal parameters for the best enhancement. In the proposed method, the evaluation criterion is defined by edge numbers, edge intensity and the entropy. The proposed method is demonstrated and compared with Linear Contrast Stretching (LCS), Histogram Equalization (HE), Genetic Algorithm based image Enhancement (GAIE), and the Particle Swarm Optimization based image enhancement (PSOIE) methods. Experimental results presented that proposed technique offers better performance.

Method for splicing video in real time based on multiple cameras

Abstract: The invention discloses a method for splicing a video in real time based on multiple cameras, which comprises the following steps of: acquiring synchronous multi-path video data; preprocessing frame images at the same moment; converting a color image into a grayscale image; enhancing the image, and expanding the dynamic range of grayscale of the image by a histogram equalization method; extracting the characteristic points of corresponding frames by using a speeded up robust features (SURF) algorithm; solving matched characteristic point pairs among corresponding frame images of the video by using a nearest neighbor matching method and a random sample consensus matching algorithm; solving an optimal homography matrix of initial k frames of the video; determining splicing overlapping regions according to the matched characteristic point pairs; taking a homography matrix corresponding to a frame with highest overlapping region similarity as the optimal homography matrix, and splicing subsequent video frame scenes; and outputting the spliced video. The method can reduce the calculated amount of splicing the video frame single-frame image, improves the splicing speed of traffic monitoring videos and achieves real-time processing effect.

Region Based Adaptive Contrast Enhancement of Medical X-Ray Images

Abstract: Medical Imaging is one of the most important application areas of digital image processing. Processing of various medical images is very much helpful to visualize and extract more details from the image. Many techniques are available for enhancing the quality of medical image. For enhancement of medical images, Contrast Enhancement is one of the most acceptable methods. Different contrast enhancement techniques i.e. Linear Stretch, Histogram Equalization, Convolution mask enhancement, Region based enhancement, Adaptive enhancement are already available. Choice of Method depends on characteristics of image. This paper deals with contrast enhancement of X-Ray images and presents here a new approach for contrast enhancement based upon Adaptive Neighborhood technique. A hybrid methodology for enhancement has been presented. Comparative analysis of proposed technique against the existing major contrast enhancement techniques has been performed and results of proposed technique are promising.

Comparative Analysis of Various Image Enhancement Techniques

Abstract: Image Enhancement is simple and most appealing area among all the digital image processing techniques. The main purpose of image enhancement is to bring out detail that is hidden in an image or to increase contrast in a low contrast image. Histogram equalization is one of the well known image enhancement technique. HE becomes a popular technique for contrast enhancement because this method is simple and effective. This paper represents review of some techniques in the area of image enhancement for brightness preservation as brightness preservation is in great demand in the consumer electronics field, when the image is effectively enhanced. Comparisons with the best available results are given in order to illustrate the best possible technique that can be used as powerful image enhancement. The performance of several established image enhancement techniques is presented in terms of different parameters like Absolute mean brightness error (AMBE), Peak signal to noise ratio (PSNR), Normalized absolute error (NAE), contrast, correlation and visual quality to make real-time image-processing applications more feasible and easier.

Contrast enhancement dynamic histogram equalization for medical image processing application

Abstract: Image processing requires an excellent image contrast-enhancement technique to extract useful information invisible to the human or machine vision Because of the histogram flattening, the widely used conventional histogram equalization image-enhancing technique suffers from severe brightness changes, rendering it undesirable Hence, we introduce a contrast-enhancement dynamic histogram-equalization algorithm method that generates better output image by preserving the input mean brightness without introducing the unfavorable side effects of checkerboard effect, artefacts, and washed-out appearance The first procedure of this technique is; normalizing input histogram and followed by smoothing process Then, the break point detection process is done to divide the histogram into subhistograms before we can remap the gray level allocation Lastly, the transformation function of each subhistogram is constructed independently © 2011 Wiley Periodicals, Inc Int J Imaging Syst Technol, 21, 280-289, 2011; (W Z Wan Ismail is a lecturer in the Multimedia University, Malaysia Currently, she is working in many projects under Dr K S Sim, also she is pursuing her PhD in image processing area)