Showing papers on "Edge enhancement published in 2013"

Efficient Contrast Enhancement Using Adaptive Gamma Correction With Weighting Distribution

Abstract: This paper proposes an efficient method to modify histograms and enhance contrast in digital images. Enhancement plays a significant role in digital image processing, computer vision, and pattern recognition. We present an automatic transformation technique that improves the brightness of dimmed images via the gamma correction and probability distribution of luminance pixels. To enhance video, the proposed image-enhancement method uses temporal information regarding the differences between each frame to reduce computational complexity. Experimental results demonstrate that the proposed method produces enhanced images of comparable or higher quality than those produced using previous state-of-the-art methods.

Color retinal image enhancement using CLAHE

Abstract: Common method in image enhancement that's often use is histogram equalization, due to this method is simple and has low computation load. In this research, we use Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance the color retinal image. To reduce this noise effect in color retinal image due to the acquisition process, we need to enhance this image. Color retinal image has unique characteristic than other image, that is, this image has important in green (G) channel. Image enhancement has important contribution in ophthalmology. In this paper, we propose new enhancement method using CLAHE in G channel to improve the color retinal image quality. The enhancement process conduct in G channel is appropriate to enhance the color retinal image quality.

Various Image Enhancement Techniques- A Critical Review

Abstract: Image Enhancement is one of the most important and difficult techniques in image research. The aim of image enhancement is to improve the visual appearance of an image, or to provide a "better transform representation for future automated image processing. Many images like medical images, satellite images, aerial images and even real life photographs suffer from poor contrast and noise. It is necessary to enhance the contrast and remove the noise to increase image quality. One of the most important stages in medical images detection and analysis is Image Enhancement techniques which improves the quality (clarity) of images for human viewing, removing blurring and noise, increasing contrast, and revealing details are examples of enhancement operations. The enhancement technique differs from one field to another according to its objective. The existing techniques of image enhancement can be classified into two categories: Spatial Domain and Frequency domain enhancement. In this paper, we present an overview of image enhancement processing techniques in spatial domain. More specifically, we categorise processing methods based representative techniques of Image enhancement. Thus the contribution of this paper is to classify and review image enhancement processing techniques, attempt an evaluation of shortcomings and general needs in this field of active research and in last we will point out promising directions on research for image enhancement for future research.

Non-Linear Direct Multi-Scale Image Enhancement Based on the Luminance and Contrast Masking Characteristics of the Human Visual System

Abstract: Image enhancement is a crucial pre-processing step for various image processing applications and vision systems. Many enhancement algorithms have been proposed based on different sets of criteria. However, a direct multi-scale image enhancement algorithm capable of independently and/or simultaneously providing adequate contrast enhancement, tonal rendition, dynamic range compression, and accurate edge preservation in a controlled manner has yet to be produced. In this paper, a multi-scale image enhancement algorithm based on a new parametric contrast measure is presented. The parametric contrast measure incorporates not only the luminance masking characteristic, but also the contrast masking characteristic of the human visual system. The formulation of the contrast measure can be adapted for any multi-resolution decomposition scheme in order to yield new human visual system-inspired multi-scale transforms. In this article, it is exemplified using the Laplacian pyramid, discrete wavelet transform, stationary wavelet transform, and dual-tree complex wavelet transform. Consequently, the proposed enhancement procedure is developed. The advantages of the proposed method include: 1) the integration of both the luminance and contrast masking phenomena; 2) the extension of non-linear mapping schemes to human visual system inspired multi-scale contrast coefficients; 3) the extension of human visual system-based image enhancement approaches to the stationary and dual-tree complex wavelet transforms, and a direct means of; 4) adjusting overall brightness; and 5) achieving dynamic range compression for image enhancement within a direct multi-scale enhancement framework. Experimental results demonstrate the ability of the proposed algorithm to achieve simultaneous local and global enhancements.

A Robust Polynomial Filtering Framework for Mammographic Image Enhancement From Biomedical Sensors

Abstract: This paper presents a non-linear framework employing a robust polynomial filter for accomplishing enhancement of mammographic abnormalities outcoming from biomedical instrumentation, i.e., X-rays instrumentation. The approach proposed in this paper uses a linear combination of Type-0 and Type-II polynomial filters as a generalized filtering solution to achieve enhancement of mammographic masses and calcifications irrespective of the nature of background tissues. A Type-0 filter provides contrast enhancement, suppressing the ill-effects of background noise. On the other hand, Type-II filter performs edge enhancement leading to preservation of finer details. Contrast improvement index is used as a performance measure to quantify the degree of improvement in contrast of the region-of interest. In addition, estimation of signal-to-noise ratio (in terms of PSNR and ASNR) is carried out to account for the suppression in background noise levels and over-enhancements of the processed mammograms. These measures are used as a mechanism to optimally select the filter parameters and also serve as a quantifying platform to compare the performance of the proposed filter with other non-linear enhancement techniques to be used for diverse biomedical image sensors.

Contourlet-based active contour model for PET image segmentation

Abstract: Purpose: PET-guided radiation therapy treatment planning, clinical diagnosis, assessment of tumor growth, and therapy response rely on the accurate delineation of the tumor volume and quantification of tracer uptake. Most PET image segmentation techniques proposed thus far are suboptimal in the presence of heterogeneity of tracer uptake within the lesion. This work presents an active contour model approach based on the method of Chan and Vese ["Active contours without edges," IEEE Trans. Image Process. 10, 266-277 (2001)] designed to take into account the high level of statistical uncertainty (noise) and to handle the heterogeneity of tumor uptake typically present in PET images. Methods: In the proposed method, the fitting terms in the Chan-Vese formulation are modified by introducing new input images, including the smoothed version of the original image using anisotropic diffusion filtering (ADF) and the contourlet transform of the image. The advantage of utilizing ADF for image smoothing is that it avoids blurring the object's edges and preserves the average activity within a region, which is important for accurate PET quantification. Moreover, incorporating the contourlet transform of the image into the fitting terms makes the energy functional more effective in directing the evolving curve toward the object boundaries due to the enhancement of the tumor-to-background ratio (TBR). The proper choice of the energy functional parameters has been formulated by making a clear consensus based on tumor heterogeneity and TBR levels. This cautious parameter selection leads to proper handling of heterogeneous lesions. The algorithm was evaluated using simulated phantom and clinical studies, where the ground truth and histology, respectively, were available for accurate quantitative analysis of the segmentation results. The proposed technique was also compared to a number of previously reported image segmentation techniques. Results: The results were quantitatively analyzed using three evaluation metrics, including the spatial overlap index (SOI), the mean relative error (MRE), and the mean classification error (MCE). Although the performance of the proposed method was analogous to other methods for some datasets, overall the proposed algorithm outperforms all other techniques. In the largest clinical group comprising nine datasets, the proposed approach improved the SOI from 0.41 +/- 0.14 obtained using the best-performing algorithm to 0.54 +/- 0.12 and reduced the MRE from 54.23 +/- 103.29 to 0.19 +/- 16.63 and the MCE from 112.86 +/- 69.07 to 60.58 +/- 18.43. Conclusions: The proposed segmentation technique is superior to other representative segmentation techniques in terms of highest overlap between the segmented volume and the ground truth/histology and minimum relative and classification errors. Therefore, the proposed active contour model can result in more accurate tumor volume delineation from PET images. (C) 2013 American Association of Physicists in Medicine.

Review of various image contrast enhancement techniques

Abstract: 2 Abstract: Image enhancement is a processing on an image in order to make it more appropriate for certain applications. It is used to improve the visual effects and the clarity of image or to make the original image more conducive for computer to process. Contrast enhancement changing the pixels intensity of the input image to utilize maximum possible bins. We need to study and review the different image contrast enhancement techniques because contrast losses the brightness in enhancement of image. By considering this fact, the mixture of global and local contrast enhancement techniques may enhance the contrast of image with preserving its brightness. There are many image contrast enhancement techniques such as HE, BBHE, DSIHE, MMBEBHE, RMSHE, MHE. BPDHE, RSWHE, GHE, LHE and LGCS. This paper focuses on the comparative study of contrast enhancement techniques with special reference to local and global enhancement techniques. Also proposed solution is identified to apply to this enhancement technique. This novel method will use in many fields, such as medical image analysis, remote sensing, HDTV, hyper spectral image processing, industrial X-ray image processing, microscopic imaging etc.

A Polynomial filtering model for enhancement of mammogram lesions

Abstract: This paper presents a preliminary analysis of a class of non-linear filters for enhancement of mammogram lesions. A non-linear filtering approach employing polynomial model of non-linearity is designed by second order truncation of Volterra series expansion. The proposed filter response is a linear combination of Type-0 and Type-II Volterra filters. Type-0 filter provides contrast enhancement, suppressing the ill-effects of background noise. On the other hand, Type-II filter employs edge enhancement. The objective analysis of the proposed filter is carried out by estimating values of quality parameters like CEM and PSNR on mammograms from MIAS and DDSM databases.

Edge Detection Techniques in Processing Digital Images: Investigation of Canny Algorithm and Gabor Method

Abstract: Edge detection is a primary function in image processing. It is the name for a set of mathematical methods which aim at identifying points in a digital image at which the image brightness changes sharply or, more formally, has discontinuities. In this paper we are going to have a short introduction to edge detection basic concepts and continue with 2 popular methods: Canny edge detection and Gabor method.

Exposing image forgery through the detection of contrast enhancement

Abstract: In this paper, a novel forensic method of exposing cut-and-paste image forgery through detecting contrast enhancement is proposed. We reveal the inter-channel correlation introduced by color image interpolation, and show how a linear or nonlinear contrast enhancement can disturb this natural inter-channel dependency. We then construct a metric to measure these correlations, which are useful in distinguishing the original and contrast enhanced images. The effectiveness of the proposed algorithm is experimentally validated on natural color images captured by commercial cameras. Finally, its robustness against some anti-forensic algorithms is also discussed.

A New Blood Vessel Extraction Technique Using Edge Enhancement and Object Classification

Abstract: Diabetic retinopathy (DR) is increasing progressively pushing the demand of automatic extraction and classification of severity of diseases. Blood vessel extraction from the fundus image is a vital and challenging task. Therefore, this paper presents a new, computationally simple, and automatic method to extract the retinal blood vessel. The proposed method comprises several basic image processing techniques, namely edge enhancement by standard template, noise removal, thresholding, morphological operation, and object classification. The proposed method has been tested on a set of retinal images. The retinal images were collected from the DRIVE database and we have employed robust performance analysis to evaluate the accuracy. The results obtained from this study reveal that the proposed method offers an average accuracy of about 97 %, sensitivity of 99 %, specificity of 86 %, and predictive value of 98 %, which is superior to various well-known techniques.

A reconfigurable real-time morphological system for augmented vision

Abstract: There is a significant number of visually impaired individuals who suffer sensitivity loss to high spatial frequencies, for whom current optical devices are limited in degree of visual aid and practical application. Digital image and video processing offers a variety of effective visual enhancement methods that can be utilised to obtain a practical augmented vision head-mounted display device. The high spatial frequencies of an image can be extracted by edge detection techniques and overlaid on top of the original image to improve visual perception among the visually impaired. Augmented visual aid devices require highly user-customisable algorithm designs for subjective configuration per task, where current digital image processing visual aids offer very little user-configurable options. This paper presents a highly user-reconfigurable morphological edge enhancement system on field-programmable gate array, where the morphological, internal and external edge gradients can be selected from the presented architecture with specified edge thickness and magnitude. In addition, the morphology architecture supports reconfigurable shape structuring elements and configurable morphological operations. The proposed morphology-based visual enhancement system introduces a high degree of user flexibility in addition to meeting real-time constraints capable of obtaining 93 fps for high-definition image resolution.

An Evaluation of Edge Detection Algorithms for Mammographic Calcifications

Abstract: Edge detection is an important module in medical imaging for diagnostic detection and extraction of features. The main limitation of the existing evaluation measures for edge detection algorithms is the requirement of a reference image for comparison. Thus, it becomes difficult to assess the performance of edge detection algorithms in case of mammographic features. This paper presents a new version of reconstruction estimation function for objective evaluation of edge enhanced mammograms containing microcalcifications. It is a non-reference approach helpful in selection of most appropriate algorithm for edge enhancement of microcalcifications and also plays a key role in selecting parameters for performance optimization of these algorithms. Simulations are performed on mammograms from MIAS database with different category of background tissues; the obtained results validate the efficiency of the proposed measure in precise assessment of mammograms (edge-maps) in accordance with the subjectivity of human evaluation.

Volterra Filter design for edge enhancement of mammogram lesions

Abstract: Non-linear filters have the property to enhance and preserve edges of lesion. Detection of edges of tumour is required in application such as to evaluate effectiveness of breast cancer treatment. Proposed algorithm uses polynomial filtering technique to enhance the contrast of lesion while preserving its edges with effective suppression of background noise. This algorithm successfully detected edges of lesion in digital mammograms taken from DDSM mammogram database. Also different metrics were used to establish effectiveness of enhancement and noise suppression in mammograms.

Image retrieval based on improved Canny edge detection algorithm

Abstract: The edge detection algorithm is the key problem of image processing. The tradition Canny edge detection algorithm is analyzed and evaluated. In order to avoid the influence of defects coming from Canny algorithm, an improved Canny edge detection algorithm is proposed in this paper. The algorithm takes use of Gaussian filter to eliminate the noise. It calculated preserving the more edge information while filtering the noise. And the algorithm could be applied in image retrieval. The simulation results show that the method not only can detect more detail of the edge, but also can improve the image retrieval performance.

Retinal blood vessel segmentation using curvelet transform and morphological reconstruction

Abstract: Digital images are obtained from the retina and graded by trained professionals. However, a significant shortage of professional observers has prompted computer assisted monitoring. Assessment of blood vessels network plays an important role in a variety of medical disorders. Manifestations of several vascular disorders, such as diabetic retinopathy and hypertensive retinopathy depend on detection of the blood vessels network. The novel vessel segmentation method starts with the contrast adjustment of the green channel image representation to increase the dynamic range of the gray levels, so that the vessels will appear brighter than the background. A multi-scale method for retinal image contrast enhancement based on the Curvelet transform is employed on the contrast adjusted image. The Curvelet transform has better performance in representing edges than wavelets for its anisotropy and directionality, and is therefore well-suited for multi-scale edge enhancement. The Curvelet coefficients in corresponding subbands are modified via a nonlinear function and take the noise into account for more precise reconstruction and better visualization. The morphological operators are used to smoothen the background, allowing vessels, to be seen clearly and to eliminate the non-vessel pixels. The described techniques in this work are applied on images from eye hospital. The proposed algorithm being simple and easy to implement, is best suited for fast processing applications.

Transform domain measure of enhancement — TDME — For security imaging applications

Abstract: Today, many security applications rely on imaging sensors. However, the quality of the captured image is highly susceptible to environmental lighting conditions such as poor or non-uniform illumination. Security imaging systems rely on efficient real-time image enhancement. For autonomous systems, determining and producing the best visually enhanced image output as perceived by the human visual system remains a challenge. In this paper, we present a metric for autonomous evaluation that will enable security systems to automatically determine the best human visual quality image. To achieve this, we have established a “no parameter no reference” metric that can determine the best visually pleasing image. The metric is algorithm independent such that it can be utilized for a diversity of enhancement algorithms. We present our DCT transform domain measure of enhancement (TDME). Unlike spatial domain measure of enhancement methods, the proposed measure is independent of image attributes and does not require any parameter selections to operate. The proposed measure is applicable to compressed and non-compressed images and can be used as an enhancement metric in conjunction with many different image enhancement methods.

Comparison between Various Edge Detection Methods on Satellite Image

Abstract: In this paper the comparative analysis of different edge detection algorithms is presented. The edge is the basic characteristic of image. In an image, an edge is a curve that follows a path of rapid change in image intensity. Edges are often associated with the boundaries of objects in a scene. Edge detection is used to identify the edges in an image and significantly reduces the amount of data and filter out useless information, while preserving the important structural features in an image. This research paper presents a brief study of the fundamental concepts of the edge detection operation, theories behind different edge detectors and compares different image edge detection algorithms including Roberts, Sobel, Prewitt, and Canny with MATLAB tool.

Video processing device, video processing method, television receiver, program, and recording medium

Abstract: An edge histogram creation unit (130) of a video-processing device (100) determines the luminance difference between adjacent pixels for each pixel forming a frame, and determines a first percentage, which is the percentage of the pixels for which the luminance difference between adjacent pixels is equal to or greater than a first threshold value. In addition, an edge enhancement processing unit (140) performs edge enhancement whereby the shoot component is made smaller for frames for which the first percentage is larger.

A Reconstruction Based Measure for Assessment of Mammogram Edge-Maps

Abstract: Performance evaluation of for mammogram edge-maps is difficult because of the absence of reference image for comparison. This paper presents a novel approach for assessment of edge enhanced mammograms containing microcalcifications. It is a non-reference approach helpful in selection of most appropriate algorithm for edge enhancement of microcalcifications. Experiments results validate the efficiency of the proposed evaluation method in precise assessment of mammograms in accordance to human evaluation.

Super-resolution through non-linear enhancement filters

Abstract: We proposed a super-resolution system that combines a total variation (TV) filter, shock filter, and learning-based method. In this paper, we ask “what super-resolution should consist of for the texture component of an image?” and propose a new solution that utilizes a TV filter for image decomposition, an improved shock filter for structure component enhancement, and a new non-linear pulse-sharpening filter for texture component enhancement. We obtain good results in terms of picture quality and computational time. We consider this system to be a practical solution, especially for the super-resolution of video signal for devices such as HDTV receivers and PCs with 4K display panels.

A novel approach for automatic number plate recognition

Abstract: In this paper, innovative method is proposed for number plate recognition. It uses series of image manipulations to recognize number plates. It uses 4-6 algorithms in order to do the same. For plate localization, several traditional images processing techniques are used. Techniques such as image enhancement, unsharp masking, edge detection, filtering and component analysis each plays a role in the extraction process. For character segmentation, connected components are extracted as individual number plate characters. Template Matching is in charge of the Optical Character Recognition.

Space charge dynamics investigation based on Kerr electro-optic measurements and processing of CCD images

Abstract: The Kerr electro-optic technique is used to investigate electric field and space charge effects using large Kerr constant propylene carbonate as the dielectric liquid with Kerr constant B≈1.22×10-12 m/V2. However, improved accuracy of this technique to measure space charge distributions remains an important goal. This paper describes inversion algorithm research on obtaining accurate space charge distributions from Kerr measurements. The original charge-coupled device (CCD) image of light intensity was converted into grayscale values for improved calculation. The grayscale CCD image was transformed into the frequency domain through a two-dimensional Fourier transformation. Butterworth filtering techniques were employed for edge enhancement algorithms. The most important parameter for calculation of space charge distributions are the integer fringe numbers n in the sequence of light and dark fringes measured by the CCD camera. Comparison of the improved algorithm and traditional methods for calculating electric field and space charge distributions between a pair of stainless steel parallel plate electrodes was investigated. The space charge dynamics were also calculated after processing the CCD images with some improvements. The results showed the electric field was essentially uniform at early times of pulsed high voltages. This uniformity indicated the absence of space charge during the early transient interval, while a bipolar charge injection was found in the central region between electrodes at later times. For longer times the electric field and space charge distributions slowly became uniform again. Grayscale processing and Butterworth filtering played an important role in the improvement of the algorithm for better image quality and edge enhancement.

Edge detection in gravity field of the Gheshm sedimentary basin

Abstract: Edge detection and edge enhancement techniques play an essential role in interpreting potential field data. This paper describes the application of various edge detection techniques to gravity data in order to delineate the edges of subsurface structures. The edge detection methods comprise analytic signal, total horizontal derivative (THDR), theta angle, tilt angle, hyperbolic of tilt angle (HTA), normalised total horizontal gradient (TDX) and normalised horizontal derivative (NTHD). The results showed that almost all filters delineated edges of anomalies successfully. However, the capability of these filters in edge detection decreased as the depth of sources increased. Of the edge enhancement filters, normalized standard deviation filter provided much better results in delineating deeper sources. The edge detection techniques were further applied on a real gravity data from the Gheshm sedimentary basin in the Persian Gulf in Iran. All filters specified a northeast-southwest structural trend. The THDR better outlined the structural morphology and trend. Moreover, it indicated the salt plugs much better than other filters. Analytic signal and THDR successfully enhanced the edges of the shorter wavelength residual structures. Normalized standard deviation (NSTD), TDX and hyperbolic of tilt angle (HTA) filters highlighted the likely fault pattern and lineaments, with a dominant northeast-southwest structural trend. This case study shows that the edge detection techniques provides valuable information for geologists and petroleum engineers to outline the horizontal location of geological sources including salt plugs and stand out buried faults, contacts and other tectonic and geological features.

Phase retrieval from differential phase contrast imaging

Abstract: Embodiments of methods and apparatus are disclosed for obtaining differential phase contrast imaging system and methods for same. Method and apparatus embodiments can provide regularized phase contrast retrieval that can address noise reduction and/or edge enhancement. Certain exemplary embodiments can suppress stripe artifacts occurring in the process of integration of noisy differential phase data. Further, certain exemplary embodiments can use transmission images and/or dark-field images to improve or restore phase contrast images affected by noise edges.

Analysis of edge detection algorithms for feature extraction in satellite images

Abstract: In field of image processing and pattern recognition, the use of edges as a feature is significant for feature extraction owing to its simplicity and accuracy. Its areas of application vary from object recognition to satellite based terrain recognition. There are many edge detection techniques like the canny edge detector and Sobel edge detector etc. However, the quality of an efficient algorithm depends on its capability to generate well localized edges of real images. Noise is inherent in all real images. To reduce its effect, various smoothing low pass filters are used prior to edge detection. This work, compares edge detection based on bilateral filtering with canny edge detection technique for satellite Images. Bilateral filtering based edge detection not only generates well localized edges but also simultaneously reduces considerable noise from real life images. The results show that the bilateral filtering based edge detection provide better edge maps than other comparable techniques.

Method and device for removing CT (computed tomography) image noises

Abstract: The invention discloses a method and a device for removing CT (computed tomography) image noises, and relates to the technical field of image noise removal. The method comprises the following steps of: estimating the tissue weight of an image, estimating the noise level of the image, calculating a noise removing parameter, carrying out anisotropic diffusion filtering on the image, carrying out edge enhancement on the image subjected to filtering output, enhancing details of the image and correcting the contrast ratio, cutting the image, and outputting the result. The device comprises a module for estimating the tissue weight of the image, a module for estimating the noise level of the image, a module for calculating the noise removing parameter, a module for carrying out anisotropic diffusion filtering on the image, a module for carrying out edge enhancement on the image subjected to filtering output, a module for performing detail enhancement and contrast ratio rectification on the image, and a module for cutting images and outputting the result. The method and the device can maintain the image edge and the original contrast ratio of the image while effectively removing the high-frequency noises of the CT images.

Comparison of Edge Detection Technique in Image Processing Techniques

Abstract: Edge detection refers to the process of identifying and locating sharp discontinuities in an image. The discontinuities are abrupt changes in pixel intensity scene. Traditional method of edge detection involves convolving the image with an operator (2D filter) which is constructed to be sensitive to large gradients. Edge detectors form a collection of very important local i mage processing method to locate sharp changes in the intensity function. Edge detection i s an important technique in many image processing applications such as object recognition, motion analysis, pattern recognition, medical image processing etc. This paper shows the comparison of edge detection techniques under different conditions showing a dvantages and disadvantages of the selected algorithms. This was done under Matlab. Further work would be to develop a novel algorithm using the working on the disadvantages and advantages of the existing one to create a novel edge detector. .

An automatic contrast enhancement method based on stochastic resonance

Abstract: Visual quality enhancement plays a vital role in low cost imaging systems, machine vision, industrial applications, remote sensing, face recognition systems and medical image interpretation etc. Growth of low cost image processing applications require image preprocessing which enhances details of an image. Most of the contrast enhancement papers apply desired contrast enhancement technique directly to enhance the given input image having poor contrast or contrast at any other undesired level. It is important to predict whether the contrast enhancement is needed for an image, to avoid the artifacts due to enhancement on the good image. In this paper, an algorithm to model images using its local contrast measure has been proposed, to classify and distinguish between the images having different contrast level. The input image is classified either as low contrast or high contrast image using the model. If the classified image is low contrast it will be enhanced using the Stochastic Resonance principle. The results show that the proposed automated procedure enhances the low contrast image better than the conventional enhancement methods.