Showing papers on "Histogram equalization published in 2014"

Contrast limited adaptive histogram equalization based enhancement for real time video system.

Abstract: Contrast limited adaptive histogram equalization (CLAHE) is used for improve the visibility level of foggy image or video. In this paper we used CLAHE enhancement method for improving the video quality in real time system. Adaptive histogram equalization (AHE) is different from normal histogram equalization because AHE use several methods each corresponding to different parts of image and used them to redistribute the lightness value of the image and in case of CLAHE `Distribution' parameter are used to define the shape of histogram which produce the better quality result compare then adaptive histogram equalization (AHE). In this algorithm rayleigh distribution parameter are used which create bell shaped histogram. The drawback of AHE is work over homogeneous fog but CLAHE applied over both homogeneous and heterogeneous fog and single image and video system. And the second drawback of AHE is used `cumulation function' which applied over only gray level image but CLAHE used both images colored and graylevel.

A Contrast Enhancement Framework with JPEG Artifacts Suppression

Abstract: Contrast enhancement is used for many algorithms in computer vision. It is applied either explicitly, such as histogram equalization and tone-curve manipulation, or implicitly via methods that deal with degradation from physical phenomena such as haze, fog or underwater imaging. While contrast enhancement boosts the image appearance, it can unintentionally boost unsightly image artifacts, especially artifacts from JPEG compression. Most JPEG implementations optimize the compression in a scene-dependent manner such that low-contrast images exhibit few perceivable artifacts even for relatively high-compression factors. After contrast enhancement, however, these artifacts become significantly visible. Although there are numerous approaches targeting JPEG artifact reduction, these are generic in nature and are applied either as pre- or post-processing steps. When applied as pre-processing, existing methods tend to over smooth the image. When applied as post-processing, these are often ineffective at removing the boosted artifacts. To resolve this problem, we propose a framework that suppresses compression artifacts as an integral part of the contrast enhancement procedure. We show that this approach can produce compelling results superior to those obtained by existing JPEG artifacts removal methods for several types of contrast enhancement problems.

Gradient Histogram Estimation and Preservation for Texture Enhanced Image Denoising

Abstract: Natural image statistics plays an important role in image denoising, and various natural image priors, including gradient-based, sparse representation-based, and nonlocal self-similarity-based ones, have been widely studied and exploited for noise removal. In spite of the great success of many denoising algorithms, they tend to smooth the fine scale image textures when removing noise, degrading the image visual quality. To address this problem, in this paper, we propose a texture enhanced image denoising method by enforcing the gradient histogram of the denoised image to be close to a reference gradient histogram of the original image. Given the reference gradient histogram, a novel gradient histogram preservation (GHP) algorithm is developed to enhance the texture structures while removing noise. Two region-based variants of GHP are proposed for the denoising of images consisting of regions with different textures. An algorithm is also developed to effectively estimate the reference gradient histogram from the noisy observation of the unknown image. Our experimental results demonstrate that the proposed GHP algorithm can well preserve the texture appearance in the denoised images, making them look more natural.

Generalized Equalization Model for Image Enhancement

Abstract: In this paper, we propose a generalized equalization model for image enhancement. Based on our analysis on the relationships between image histogram and contrast enhancement/white balancing, we first establish a generalized equalization model integrating contrast enhancement and white balancing into a unified framework of convex programming of image histogram. We show that many image enhancement tasks can be accomplished by the proposed model using different configurations of parameters. With two defining properties of histogram transform, namely contrast gain and nonlinearity, the model parameters for different enhancement applications can be optimized. We then derive an optimal image enhancement algorithm that theoretically achieves the best joint contrast enhancement and white balancing result with trading-off between contrast enhancement and tonal distortion. Subjective and objective experimental results show favorable performances of the proposed algorithm in applications of image enhancement, white balancing and tone correction. Computational complexity of the proposed method is also analyzed.

An artificial bee colony algorithm for image contrast enhancement

Abstract: Image Enhancement is a crucial phase in almost every image processing system It aims at improving both the visual and the informational quality of distorted images Histogram Equalization (HE) techniques are the most popular approaches for image enhancement for they succeed in enhancing the image and preserving its main characteristics However, using exhaustive approaches for histogram equalisation is an algorithmically complex task These HE techniques also fail in offering good enhancement if not so good parameters are chosen So, new intelligent approaches, using Artificial Intelligence techniques, have been proposed for image enhancement In this context, this paper proposes a new Artificial Bee Colony (ABC) algorithm for image contrast enhancement A grey-level mapping technique and a new image quality measure are used The algorithm has been tested on some test images, and the comparisons of the obtained results with the genetic algorithm have proven its superiority Moreover, the proposed algorithm has been extended to colour image enhancement and given very promising results Further qualitative and statistical comparisons of the proposed ABC to the Cuckoo Search (CS) algorithm are also presented in the paper; not only for the adopted grey-level mapping technique, but also with using another common transformation, generally called the local/global transformation

Histogram Equalization for Image Enhancement Using MRI Brain Images

Abstract: Medical image processing plays an essential role in providing information in wide area for such advanced images. Magnetic resonance imaging (MRI) is an advanced medical imaging technique providing rich information about the human soft tissue anatomy. MRI of the brain is an invaluable tool to help physicians to diagnose and treat various brain diseases including stroke, cancer, and epilepsy. The specific information to evaluate the diseases. Histogram equalization is one of the important steps in image enhancement technique for MRI. There are several methods of image enhancement and each of them is needed for a different type of analysis. In this paper study and compare different Techniques like Global Histogram Equalization (GHE), Local histogram equalization (LHE), Brightness preserving Dynamic Histogram equalization (BPDHE) and Adaptive Histogram Equalization (AHE) using different objective quality measures for MRI brain image Enhancement.

A fast and efficient color image enhancement method based on fuzzy-logic and histogram

Abstract: A new fuzzy logic and histogram based algorithm for enhancing low contrast color images has been proposed here. The method is computationally fast compared to conventional and other advanced enhancement techniques. It is based on two important parameters M and K , where M is the average intensity value of the image, calculated from the histogram and K is the contrast intensification parameter. The given RGB image is converted into HSV color space to preserve the chromatic information contained in the original image. To enhance the image, only the V component is stretched under the control of the parameters M and K . The proposed method has been compared with conventional contrast enhancement techniques as well as with advanced algorithms. All the above techniques were based on the principle of transforming the skewed histogram of the original image into a uniform histogram. The performance of the different contrast enhancement algorithms are evaluated based on the visual quality, Tenengrad, CII and the computational time. The inter comparison of different techniques was carried out on different low contrast color images. Based on the performance analysis, we advocate that our proposed Fuzzy Logic method is well suited for contrast enhancement of low contrast color images.

Fast Hue and Range Preserving Histogram: Specification: Theory and New Algorithms for Color Image Enhancement.

Abstract: Color image enhancement is a complex and challenging task in digital imaging with abundant applications. Preserving the hue of the input image is crucial in a wide range of situations. We propose simple image enhancement algorithms, which conserve the hue and preserve the range (gamut) of the R, G, B channels in an optimal way. In our setup, the intensity input image is transformed into a target intensity image whose histogram matches a specified, well-behaved histogram. We derive a new color assignment methodology where the resulting enhanced image fits the target intensity image. We analyze the obtained algorithms in terms of chromaticity improvement and compare them with the unique and quite popular histogram-based hue and range preserving algorithm of Naik and Murthy. Numerical tests confirm our theoretical results and show that our algorithms perform much better than the Naik-Murthy algorithm. In spite of their simplicity, they compete with well-established alternative methods for images where hue-preservation is desired.

Contact-free palm-vein recognition based on local invariant features.

Abstract: Contact-free palm-vein recognition is one of the most challenging and promising areas in hand biometrics. In view of the existing problems in contact-free palm-vein imaging, including projection transformation, uneven illumination and difficulty in extracting exact ROIs, this paper presents a novel recognition approach for contact-free palm-vein recognition that performs feature extraction and matching on all vein textures distributed over the palm surface, including finger veins and palm veins, to minimize the loss of feature information. First, a hierarchical enhancement algorithm, which combines a DOG filter and histogram equalization, is adopted to alleviate uneven illumination and to highlight vein textures. Second, RootSIFT, a more stable local invariant feature extraction method in comparison to SIFT, is adopted to overcome the projection transformation in contact-free mode. Subsequently, a novel hierarchical mismatching removal algorithm based on neighborhood searching and LBP histograms is adopted to improve the accuracy of feature matching. Finally, we rigorously evaluated the proposed approach using two different databases and obtained 0.996% and 3.112% Equal Error Rates (EERs), respectively, which demonstrate the effectiveness of the proposed approach.

Particle swarm optimized multi-objective histogram equalization for image enhancement

Abstract: Histogram Equalization (HE) is a simple and effective technique for enhancing the contrast of the input image However, it fails to preserve the brightness while enhancing the contrast due to the abrupt mean shift during the process of equalization Many HE based methods have been developed to overcome the problem of mean shift But, they suffered from over-enhancement In this paper, a multi-objective HE model has been proposed in order to enhance the contrast as well as to preserve the brightness The central idea of this technique is to first segment the histogram of the input image into two using Otsu's threshold A set of optimized weighing constraints are formulated and applied on both the sub-images Then, the sub-images are equalized independently and their union produces the contrast enhanced, brightness preserved output image Here, Particle Swarm Optimization (PSO) is employed to find the optimal constraints This technique is proved to have an edge over the other contemporary methods in terms of entropy and contrast improvement index

Fast K-means algorithm based on a level histogram for image retrieval

Abstract: The level histogram is used with the K-means algorithm for clustering data.The fast K-means algorithm was effectively applied to image database sets.The fast K-means algorithm improved the efficiency of the traditional K-means algorithm.The fast K-means algorithm processed images efficiently as images increases.The selection of the initial cluster centers affected the performance. In image retrieval, the image feature is the main factor determining accuracy; the color feature is the most important feature and is most commonly used with a K-means algorithm. To create a fast K-means algorithm for this study, first a level histogram of statistics for the image database is made. The level histogram is used with the K-means algorithm for clustering data. A fast K-means algorithm not only shortens the length of time spent on training the image database cluster centers, but it also overcomes the cluster center re-training problem since large numbers of images are continuously added into the database. For the experiment, we use gray and color image database sets for performance comparisons and analyzes, respectively. The results show that the fast K-means algorithm is more effective, faster, and more convenient than the traditional K-means algorithm. Moreover, it overcomes the problem of spending excessive amounts of time on re-training caused by the continuous addition of images to the image database. Selection of initial cluster centers also affects the performance of cluster center training.

Modified Histogram based contrast enhancement using Homomorphic Filtering for medical images

Abstract: In medical image processing, low contrast image analysis is a challenging problem. Low contrast digital images reduce the ability of observer in analyzing the image. Histogram based techniques are used to enhance contrast of all type of medical images. They are mainly used for all type of medical images such as for Mias-mammogram images, these methods are used to find exact locations of cancerous regions and for low-dose CT images, these methods are used to intensify tiny anatomies like vessels, lungs nodules, airways and pulmonary fissures. The most effective method used for contrast enhancement is Histogram Equalization (HE). Here we propose a new method named “Modified Histogram Based Contrast Enhancement using Homomorphic Filtering” (MH-FIL) for medical images. This method uses two step processing, in first step global contrast of image is enhanced using histogram modification followed by histogram equalization and then in second step homomorphic filtering is used for image sharpening, this filtering if followed by image normalization. To evaluate the effectiveness of our method we choose two widely used metrics Absolute Mean Brightness Error (AMBE) and Entropy. Based on results of these two metrics this algorithm is proved as a flexible and effective way for medical image enhancement and can be used as a pre-processing step for medical image understanding and analysis.

Artificial Bee Colony-based satellite image contrast and brightness enhancement technique using DWT-SVD

Abstract: In this article, a new contrast enhancement approach is presented for quality enhancement of low-contrast satellite images. The proposed technique is based on the Artificial Bee Colony (ABC) algorithm using Discrete Wavelet Transform and Singular Value Decomposition (DWT-SVD). The method employs the ABC technique to learn the parameters of the adaptive thresholding function required for optimum enhancement. In this approach, the input image is primarily decomposed into four sub-bands through DWT, and then each sub-band of DWT is optimized through the ABC algorithm. After that, a singular value matrix of the low–low thresholded sub-band image is estimated and, finally, the enhanced image is constructed by applying inverse DWT. The results obtained through this method reveal that the proposed methodology gives better performance in terms of peak signal-to-noise ratio (PSNR), mean square error (MSE), and mean and standard deviation as compared to General Histogram Equalization (GHE), Discrete Cosine Transfor...

Automatic inspection system of LED chip using two-stages back-propagation neural network

Abstract: This study proposed an automatic LED defect detection system to investigate the defects of LED chips. Such defects include fragment chips, scratch marks and remained gold on the pad area, scratch marks on the luminous zone, and missing luminous zone respectively. The system was based on positioning and image acquisition, appearance feature recognition, and defect classification. The normalized correlation coefficient method was used to locate the chip and acquire its image, the K-means clustering method was used to distinguish the appearance, pad area, and luminous zone of chips. In terms of pad area detection, histogram equalization was used to enhance the pad image contrast, and statistical threshold selection and morphological closing were applied to modify the impure points in the pad. Feature values of the pad area were then calculated. The optimal statistical threshold separated the luminous zone and background from the substrate. After processed with closing operation, features of the luminous zone were extracted. Finally, features of each part were clarified by an efficient two-step back-propagation neural network, where a designed appearance classifier and an internal structure classifier were used for recognition. From experiments, total recognition rate of this study achieved 97.83 %, proving that the detection method proposed by this study can efficiently detect LED chip defects.

An analysis of x-ray image enhancement methods for vertebral bone segmentation

Abstract: Image enhancement is a critical component in getting a good segmentation, especially for X-ray images. Magnification of the contrast and sharpness of the image will increase the accuracy of the subsequent modules for an autonomous disease diagnosis system. In this paper, we analyze various methods of preprocessing techniques for vertebral bone segmentation. Three methods are considered which are histogram equalization (HE), gamma correction (GC) and contrast limited adaptive histogram equalizer (CLAHE). This work aims to compare and quantify the precision and accuracy of the techniques that are used to enhance the image quality. Experimental results of the system yield favorable results where the most accurate technique is CLAHE, followed by GC and HE.

Computer Visionimage Enhancement for Plant Leaves Disease Detection

Abstract: Enhanced images have high quality and clarity than original captured images. Computer vision image enhancement (Color conversion and Histogram equalization) is used in different real time applications such as remote sensing, medical image analysis and plant leaves disease detection. Original captured images are RGB images. RGB images are combination of primary colors (Red, Green and Blue). It is difficult to implement the applications because of the range of this color is 0 to 255. Grayscale images have only the range between 0 and 1. So it is easy to implement many applications. Histogram equalization is used to increase the images clarity. Grayscale conversion and histogram equalization is used in plant leaves disease detection.

Particle swarm optimized bi-histogram equalization for contrast enhancement and brightness preservation of images

Abstract: A novel technique, Optimized Bi-Histogram Equalization (OBHE), is proposed in this paper for preserving brightness and enhancing the contrast of any input image. The central idea of this technique is to first segment the histogram of the input image into two, based on its mean and then weighting constraints are applied to each of the sub-histograms separately. Those two histograms are equalized independently and their union produces a brightness-preserved and contrast-enhanced output image. While formulating the weighting constraints, Particle Swarm Optimization (PSO) is employed to find the optimal constraints in order to maximize the degree of brightness preservation and contrast enhancement. This technique is found to have an edge over the other contemporary methods in terms of Entropy and Absolute Mean Brightness Error.

Color Retinal Image Enhancement by Rayleigh Contrast-Limited Adaptive Histogram Equalization

Abstract: Retinal fundus image is important for ophthalmologist to identify and detect many vision-related diseases, such as diabetes and hypertension. From an acquisition process, retinal images often have low gray level contrast and low dynamic range. This paper proposes a method using improved nonlinear hue-saturation-intensity color model(iNHSI) to preserve color information of the retinal images. The intensity component is enhanced by Rayleigh transformation in contrast-limited adaptive histogram equalization (Rayleigh CLAHE) algorithm. This algorithm help to increase the contrast and improve the overall appearance. The proposed algorithm was tested by using standard public database for benchmarking diabetic retinopathy detection from digital image. The proposed method can preserve the original hue component unchanged; because, the hue information of the input images is important to ophthalmologist in diagnosis process.

HSV color space based segmentation of region of interest in satellite images

Abstract: In this paper, a new method for the extraction of the necessary object or region from the satellite image is proposed. This color based method is based on HSV color space and histogram threshold. Usually the multispectral images received from satellite are in RGB color space. But human eye is more sensitive to intensity than color information (hue or saturation). In the proposed approach, the satellite image in RGB color space is transformed into HSV color space and then the transformed satellite image is split into three different components (channels or images) based on intensity and color. This HSV color space is designed to approximate the human vision. In the next step, the histogram for all three components (hue, saturation and value) is computed and plotted. Then the threshold value is separately applied to all three components. Finally the morphological operations like masking, filtering, smoothening is performed for the extraction of the desired region. The effectiveness of the proposed approach has been demonstrated by number of experiments. Experimental result demonstrates that the proposed method is more efficient in the extraction and segmentation of a desired object or region in the satellite images.

Various Image Enhancement Techniques - A Critical Review

Abstract: Image Enhancement is very essential and important technique used in image processing. The role of image enhancement is to improve the content visibility of an image. Images in different fields like medical, satellite images, aerial images and even real life pictures suffer from poor contrast and high noise. It is important to only enhance the contrast and reduce the noise to increase image quality. The enhancement technique differs according to various aspects and they can be broadly classified into two categories: Spatial Domain and Frequency domain based techniques. This paper presents a review of image enhancement processing techniques in spatial domain. Also we have categorized processing methods based representative techniques of Image enhancement. Thus this paper helps to evaluate various image enhancement techniques.

YIQ color space based satellite image segmentation using modified FCM clustering and histogram equalization

Abstract: The images received from the satellite contains huge amount of data for further processing in image analysis. An efficient and effective segmentation method is essential to retrieve or extract the necessary information from the satellite images. The images received from satellite are usually in RGB color space. This color space is not preferred for image segmentation because this space is not perceptually uniform and all components should be quantized with the same precision. In this paper, efficient satellite image segmentation based on YIQ color space and Modified Fuzzy C Means clustering is proposed. The YIQ color space takes the advantage of the information that our human eye is very sensitive to intensity changes than changes to saturation or hue and the intensity component can be stored with greater accuracy. In YIQ color space, intensity information is separated from color data. So the same image can easily be analyzed by using both color and intensity information. In the proposed approach, the satellite image in RGB color space is transformed into YIQ color space and then the transformed satellite image is split into three different components (channels) based on luminance and chrominance. Histogram equalization is performed on the luminance component. Subsequently Fuzzy based segmentation is applied for efficient segmentation. This proposed approach is applied to analyze the satellite images of various format and size. The experimental result shows that the proposed method is efficient for extracting information from the satellite images.

Review of Different Local and Global Contrast Enhancement Techniques for a Digital Image

Abstract: Image enhancement is one of the challenging issues in low level image processing. The main aim of image enhancement is to enhance quality of the image so that visual appearance can be improved. Contrast enhancement is an important factor for image enhancement. Histogram based techniques are one of the most important image processing techniques that are used for enhancement tasks. Histogram equalization is a very effective approach to contrast enhancement. However, histogram equalization tends to change the brightness of the image. The present paper describes a review of different local and global contrast enhancement techniques for a digital image.