Showing papers on "Histogram equalization published in 2012"

Depth Video Enhancement Based on Weighted Mode Filtering

Abstract: This paper presents a novel approach for depth video enhancement. Given a high-resolution color video and its corresponding low-quality depth video, we improve the quality of the depth video by increasing its resolution and suppressing noise. For that, a weighted mode filtering method is proposed based on a joint histogram. When the histogram is generated, the weight based on color similarity between reference and neighboring pixels on the color image is computed and then used for counting each bin on the joint histogram of the depth map. A final solution is determined by seeking a global mode on the histogram. We show that the proposed method provides the optimal solution with respect to L1 norm minimization. For temporally consistent estimate on depth video, we extend this method into temporally neighboring frames. Simple optical flow estimation and patch similarity measure are used for obtaining the high-quality depth video in an efficient manner. Experimental results show that the proposed method has outstanding performance and is very efficient, compared with existing methods. We also show that the temporally consistent enhancement of depth video addresses a flickering problem and improves the accuracy of depth video.

Fast multi-exposure image fusion with median filter and recursive filter

Abstract: This paper proposes a weighted sum based multi-exposure image fusion method which consists of two main steps: three image features composed of local contrast, brightness and color dissimilarity are first measured to estimate the weight maps refined by recursive filtering. Then, the fused image is constructed by weighted sum of source images. The main advantage of the proposed method lies in a recursive filter based weight map refinement step which is able to obtain accurate weight maps for image fusion. Another advantage is that a novel histogram equalization and median filter based motion detection method is proposed for fusing multi-exposure images in dynamic scenes which contain motion objects. Furthermore, the proposed method is quite fast and thus can be directly used for most consumer cameras. Experimental results demonstrate the superiority of the proposed method in terms of subjective and objective evaluation.

Content Based Image Retrieval using Color and Texture

Abstract: The increased need of content based image retrieval technique can be found in a number of different domains such as Data Mining, Education, Medical Imaging, Crime Prevention, Weather forecasting, Remote Sensing and Management of Earth Resources. This paper presents the content based image retrieval, using features like texture and color, called WBCHIR (Wavelet Based Color Histogram Image Retrieval).The texture and color features are extracted through wavelet transformation and color histogram and the combination of these features is robust to scaling and translation of objects in an image. The proposed system has demonstrated a promising and faster retrieval method on a WANG image database containing 1000 general-purpose color images. The performance has been evaluated by comparing with the existing systems in the literature.

Power-Constrained Contrast Enhancement for Emissive Displays Based on Histogram Equalization

Abstract: A power-constrained contrast-enhancement algorithm for emissive displays based on histogram equalization (HE) is proposed in this paper. We first propose a log-based histogram modification scheme to reduce overstretching artifacts of the conventional HE technique. Then, we develop a power-consumption model for emissive displays and formulate an objective function that consists of the histogram-equalizing term and the power term. By minimizing the objective function based on the convex optimization theory, the proposed algorithm achieves contrast enhancement and power saving simultaneously. Moreover, we extend the proposed algorithm to enhance video sequences, as well as still images. Simulation results demonstrate that the proposed algorithm can reduce power consumption significantly while improving image contrast and perceptual quality.

Tumor detection using threshold operation in MRI brain images

Abstract: Medical Image Processing is a complex and challenging field nowadays Processing of MRI images is one of the parts of this field This paper proposes a strategy for efficient detection of a brain tumor in MRI brain images The methodology consists of the following steps: preprocessing by using sharpening and median filters, enhancement of image is performed by histogram equalization, segmentation of the image is performed by thresholding This approach is then followed by the further application of morphological operations Finally the tumor region can be obtained by using the technique of image subtraction

Extraction of image feature data from images

Abstract: An apparatus and method for obtaining image feature data of an image are disclosed herein. A color histogram of the image is extracted from the image, the extraction of the color histogram including performing one-dimensional sampling of pixels comprising the image in each of a first dimension of a color space, a second dimension of the color space, and a third dimension of the color space. An edge map corresponding to the image is analyzed to detect a pattern included in the image. In response to a confidence level of the pattern detection being below a pre-defined threshold, extracting from the image an orientation histogram of the image. And identify a dominant color of the image.

Modified Contrast Limited Adaptive Histogram Equalization Based on Local Contrast Enhancement for Mammogram Images

Abstract: Optimal Contrast enhancement for detection of masses and micro calcification of mammogram images using Contrast Limited Adaptive Histogram Equalization (CLAHE) based on local contrast modification (LCM) is presented in this paper. The LCM-CLAHE is proposed to highlight the finer hidden details in mammogram images and to adjust the level of contrast enhancement. The proposed method is tested for mammographic images from MIAS database. The performance of the proposed method is obtained using Peak Signal to Noise Ratio (PSNR). The results are compared with other standard enhancement techniques such as Histogram Equalization, Unsharp Masking (USM) and CLAHE. The experimental results of proposed method show that this method provides better contrast enhancement with preserving all the local information of the mammogram images.

Visibility enhancement using an image filtering approach

Abstract: The misty, foggy, or hazy weather conditions lead to image color distortion and reduce the resolution and the contrast of the observed object in outdoor scene acquisition. In order to detect and remove haze, this article proposes a novel effective algorithm for visibility enhancement from a single gray or color image. Since it can be considered that the haze mainly concentrates in one component of the multilayer image, the haze-free image is reconstructed through haze layer estimation based on the image filtering approach using both low-rank technique and the overlap averaging scheme. By using parallel analysis with Monte Carlo simulation from the coarse atmospheric veil by the median filter, the refined smooth haze layer is acquired with both less texture and retaining depth changes. With the dark channel prior, the normalized transmission coefficient is calculated to restore fogless image. Experimental results show that the proposed algorithm is a simpler and efficient method for clarity improvement and contrast enhancement from a single foggy image. Moreover, it can be comparable with the state-of-the-art methods, and even has better results than them.

향상된 Histogram Equalization의 하드웨어 설계

Abstract: Histogram Equalization, the most typical algorithm for improving the image quality of algorithms that can effectively implement measures presented in this paper. Improved Histogram Equalization of quadratic forms brightness equalization process outside of the existing limited imaging Histogram Equalization to fit in the hardware design and implementation of the first order by the implementation. And our hardware was proved through the experimental performance

Design of Interpolation Functions for Subpixel-Accuracy Stereo-Vision Systems

Abstract: Traditionally, subpixel interpolation in stereo-vision systems was designed for the block-matching algorithm. During the evaluation of different interpolation strategies, a strong correlation was observed between the type of the stereo algorithm and the subpixel accuracy of the different solutions. Subpixel interpolation should be adapted to each stereo algorithm to achieve maximum accuracy. In consequence, it is more important to propose methodologies for interpolation function generation than specific function shapes. We propose two such methodologies based on data generated by the stereo algorithms. The first proposal uses a histogram to model the environment and applies histogram equalization to an existing solution adapting it to the data. The second proposal employs synthetic images of a known environment and applies function fitting to the resulted data. The resulting function matches the algorithm and the data as best as possible. An extensive evaluation set is used to validate the findings. Both real and synthetic test cases were employed in different scenarios. The test results are consistent and show significant improvements compared with traditional solutions.

Medical images enhancement by using anisotropic filter and CLAHE

Abstract: The purpose of image enhancement is to process an acquired image for better contrast and visibility of features of interest for visual examination as well as subsequent computer-aided analysis and diagnosis. Therefore, we have proposed an algorithm for medical images enhancement. In the study, we used top-hat transform, contrast limited histogram equalization and anisotropic diffusion filter methods. The system results are quite satisfactory for many different medical images like lung, breast, brain, knee and etc.

A robust multisource image automatic registration system based on the SIFT descriptor

Abstract: Image registration is an essential step in many remote-sensing RS applications. This article presents a study of a multisource image automatic registration system MIARS based on the scale-invariant feature transform SIFT, which has been demonstrated to be the most robust local invariant feature descriptor for automatically registering various RS images. The SIFT descriptor has two shortcomings: it is unsuitable for extremely large images and has an irregular distribution of feature points. Therefore, three steps are proposed for the MIARS: image division, histogram equalization and the elimination of false point matches by a subregion least squares iteration. Image division makes it possible to use the SIFT descriptor to extract control points from an extremely large RS image. Histogram equalization in prematching improves the contrast sensitivity of RS images. The subregion least squares iteration refines the registration accuracy. Images from multisensor systems, including Quickbird, IRS-P6, Landsat/TM, HJ-CCD, HJ-IRS, light detection and ranging LiDAR intensity images and aerial data, were selected to test the reliability of the MIARS. The results indicated that better registration accuracy was achieved, which will be very helpful in the future development of a registration model.

Weighted average multi segment histogram equalization for brightness preserving contrast enhancement

Abstract: Histogram equalization (HE) is one of the most effective method for contrast enhancement, but it fails to preserve the mean brightness of images. To overcome such drawback, several Bi- and Multi-histogram equalization methods have been proposed. Among them, Bi-HE methods may preserve the brightness, but they introduce some undesirable artifacts in the processed image. On the other hand, Multi-HE methods may not introduce undesirable artifacts in image but at the cost of either the brightness or its contrast. In this paper, we propose a weighted average multi segment HE method using Gaussian filter for contrast enhancement of natural images while preserving mean brightness. It also reduces noise present in the images. The proposed method first smooths the global histogram and decomposes it into multiple segments via optimal thresholds, and then HE is applied to each segment independently. Simulation results for several test images show that the proposed method enhances the contrast while preserving mean brightness.

Contrast Enhancement of Dark Images using Stochastic Resonance in Wavelet Domain

Abstract: In this paper, a dynamic stochastic resonance (DSR)-based technique in discrete wavelet transform (DWT) domain is presented for the enhancement of very dark grayscale and colored images. Generally in DSR, the performance of an input signal can be improved by addition of external noise. However in this paper, the intrinsic noise of an image has been utilized for the purpose of contrast enhancement. The DSR procedure iteratively tunes the DWT coefficients using bistable system parameters. The DSR-based technique significantly enhances the image without introducing any blocking, ringing or spot artifacts. The algorithm has been optimized and made adaptive. Performance of the given technique has been measured in terms of distribution separation measure (DSM), target-to- background enhancement measure based on standard deviation (TBEs) and target-to-background enhancement measure based on entropy (TBEe). When compared with the existing enhancement techniques such as histogram equalization, gamma correction, single-scale retinex, multi- scale retinex, modified high-pass filtering and Fourier-based DSR, the DWT-based DSR technique gives better performance in terms of visual information, color preservation and computational complexity of the enhancement process.

Color Palette Generation

Abstract: A method of extracting a color palette includes receiving initial color attribute values of corresponding image elements representing an image, transforming the initial color attribute values to lexical color classifiers of the corresponding image elements, clustering the image elements based on the lexical color classifiers into clusters of image elements, and generating a color palette having color regions, each color region corresponding to a color associated with a cluster of image elements.

Contrast enhancement using histogram equalization based on logarithmic mapping

Abstract: A widely used contrast enhancement method, the histogram equalization (HE) often produces images with unnatural appearances and visually disturbing artifacts because the HE compels the enhanced image to follow the uniform distribution. An adaptive histogram equalization using logarithmic mapping is presented, with a proposed algorithm based on a bin underflow and overflow method that achieves contrast enhancement by putting constraints on each histogram component differently. To incorporate characteristics of the human visual system, the logarithmic mapping function is used as constraint function, while the rate of contrast enhancement is controlled by determining the control parameters with the characteristics of the original image. The experimental results show that the proposed algorithm not only keeps the original histogram shape features, but also enhances the contrast effectively. Due to its simplicity, the proposed algorithm can be applied by simple hardware and processed in a real-time system.

Gaussian mixture modeling of histograms for contrast enhancement

Abstract: The current major theme in contrast enhancement is to partition the input histogram into multiple sub-histograms before final equalization of each sub-histogram is performed. This paper presents a novel contrast enhancement method based on Gaussian mixture modeling of image histograms, which provides a sound theoretical underpinning of the partitioning process. Our method comprises five major steps. First, the number of Gaussian functions to be used in the model is determined using a cost function of input histogram partitioning. Then the parameters of a Gaussian mixture model are estimated to find the best fit to the input histogram under a threshold. A binary search strategy is then applied to find the intersection points between the Gaussian functions. The intersection points thus found are used to partition the input histogram into a new set of sub-histograms, on which the classical histogram equalization (HE) is performed. Finally, a brightness preservation operation is performed to adjust the histogram produced in the previous step into a final one. Based on three representative test images, the experimental results demonstrate the contrast enhancement advantage of the proposed method when compared to twelve state-of-the-art methods in the literature.

Contrast enhancement of computed tomography images by adaptive histogram equalization-application for improved ischemic stroke detection

Abstract: The visualization of computed tomography brain images is basically done by performing the window setting, which stretches an image from the Digital Imaging and Communications in Medicine format into the standard grayscale format. However, the standard window setting does not provide a good contrast to highlight the hypodense area for the detection of ischemic stroke. While the conventional histogram equalization and other proposed enhanced schemes insufficiently enhance the image contrast, they also may introduce unwanted artifacts on the so-called “enhanced image.” In this article, a new adaptive method is proposed to excellently improve the image contrast without causing any unwanted defects. The method first decomposed an image into equal-sized nonoverlapped sub-blocks. After that, the distribution of the extreme levels in the histogram for a sub-block is eliminated. The eliminated distribution pixels are then equally redistributed to the other grey levels with threshold limitation. Finally, the grey level reallocation function is defined. The bilinear interpolation is used to estimate the best value for each pixel in the images to remove the potential blocking effect. © 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 153–160, 2012 © 2012 Wiley Periodicals, Inc.

Histogram partition based gamma correction for image contrast enhancement

Abstract: This paper presents an adaptive image contrast enhancement method. The proposed method is based on a local gamma correction piloted by histogram analysis. First, the image histogram is partitioned based on the local minima and the mean gray-level of each partition is calculated. Then, gamma correction is performed using the resulted mean gray-levels. By analyzing the partitioned histograms, the parameters are automatically adjusted. Experimental results show that both good contrast enhancement performance and image brightness preservation can be achieved by the proposed method.

Multi Segment Histogram Equalization for Brightness Preserving Contrast Enhancement

Abstract: Histogram equalization (HE) method proved to be a simple and most effective technique for contrast enhancement of digital images, but it does not preserve the brightness and natural look of images. To overcome this problem, several Bi- and Multi-histogram equalization methods have been proposed. Among them, the Bi-HE methods significantly enhance the contrast and may preserve the brightness, but they destroy the natural look of the image. On the other hand, Multi-HE methods are proposed to maintain the natural look of image at the cost of either the brightness or its contrast. In this paper, we propose a Multi-HE method for contrast enhancement of natural images while preserving its brightness and natural look. The proposed method decomposes the histogram of an input image into multiple segments, and then HE is applied to each segment independently. Simulation results for several test images show that the proposed method enhances the contrast while preserving brightness and natural look of the images.

Spectral Recognition Techniques and MLC of IRS P6 LISS III image for Coastal Landforms Extraction along South West Coast of Tamilnadu, India

Abstract: The Remote sensing technology is measured or observed reflected energy to construct an image of the landscape beneath the platform passage in a discrete pixel format. The geometric and radiometric characteristics of remotely sensed image provide information about earth's surface. In the present study, the primary data product obtained from IRS P6 satellite LISS - III images (23.5 m) are used to extract the landforms the South West coast of Tamil nadu, India. The study area comprises different types of landforms in nature. The selected image processing techniques are employed such as, geometric correction, radiometric correction for removal of atmospheric errors and noise from image and to identify spectral and spatial variations in structure, texture, pattern of objects in the image. Here, the spectral recognition statistics namely edge detection; edge enhancement; histogram equalization, principal component analysis and maximum likelihood classifier algorithms are applied for demarcate the coastal landforms. In the maximum likelihood classification process, the spectral properties (Digital Number) of an object (kn) in the image has been identified using mean and covariance of pixels in training set, then the probability function (Px) determines the distribution of that group of pixels (class) in the image. The coastal landforms are segmented as separate class from the image based on their spectral and spatial characteristics such as shoreline, beach, sand dunes, erosional and accretion lands, water body, river deltas, and manmade infrastructure with attribute of shape, area, location and spatial distribution.

An Efficient Method for Contrast Enhancement in Still Images using Histogram Modification Framework

Abstract: Problem statement: Image enhancement improves an image appearance by increasing dominance of some features or by decreasing ambiguity between different regions of the image. Histogram based image enhancement technique is mainly based on equalizing the histogram of the image and increasing the dynamic range corresponding to the image. Approach: Histogram Equalization is widely used in different ways to perform contrast enhancement in images. As a result, such image creates side-effects such as washed out appearance and false contouring due to the significant change in brightness. In order to overcome these problems, mean brightness preserving Histogram Equalization based techniques have been proposed. Generally, these methods partition the histogram of the original image into sub histograms and then independently equalize each sub histogram with Histogram Equalization. Results: The comparison of recent histogram based techniques is presented for contrast enhancement in low illumination environment and the experiment results are collected using low light environment images. Conclusion: The histogram modification algorithm is simple and computationally effective that makes it easy to implement and use in real time systems.