Showing papers on "Edge enhancement published in 2011"

IMAGE Resolution Enhancement by Using Discrete and Stationary Wavelet Decomposition

Abstract: In this correspondence, the authors propose an image resolution enhancement technique based on interpolation of the high frequency subband images obtained by discrete wavelet transform (DWT) and the input image. The edges are enhanced by introducing an intermediate stage by using stationary wavelet transform (SWT). DWT is applied in order to decompose an input image into different subbands. Then the high frequency subbands as well as the input image are interpolated. The estimated high frequency subbands are being modified by using high frequency subband obtained through SWT. Then all these subbands are combined to generate a new high resolution image by using inverse DWT (IDWT). The quantitative and visual results are showing the superiority of the proposed technique over the conventional and state-of-art image resolution enhancement techniques.

Edge Enhancement Algorithm Based on the Wavelet Transform for Automatic Edge Detection in SAR Images

Abstract: This paper presents a novel technique for automatic edge enhancement and detection in synthetic aperture radar (SAR) images. The characteristics of SAR images justify the importance of an edge enhancement step prior to edge detection. Therefore, this paper presents a robust and unsupervised edge enhancement algorithm based on a combination of wavelet coefficients at different scales. The performance of the method is first tested on simulated images. Then, in order to complete the automatic detection chain, among the different options for the decision stage, the use of geodesic active contour is proposed. The second part of this paper suggests the extraction of the coastline in SAR images as a particular case of edge detection. Hence, after highlighting its practical interest, the technique that is theoretically presented in the first part of this paper is applied to real scenarios. Finally, the chances of its operational capability are assessed.

Computer Aided Detection of Brain Tumor in Magnetic Resonance Images

Abstract: Brain tumor is an abnormal mass of tissue with uncoordinated growth inside the skull which may invade and damage nerves and other healthy tissues. Non-homogeneities of the brain tissues result in inaccurate detection of tumor boundaries with the existing methods for contrast enhancement and segmentation of magnetic resonance images (MRI).This paper presents an improved framework for computer aided detection of brain tumor. This involves enhancement of cerebral MRI features by incorporating enhancement approaches of both the frequency and spatial domain. The proposed method requires de-noising in wavelet domain followed by enhancement using a non-linear enhancement function. Further an iterative enhancement algorithm is applied for enhancing the edges using the morphological filter. Segmentation of the brain tumor is finally obtained by employing large sized structuring elements along with thresholding. Simulation results along with the estimates of quality metrics portray significant improvement of contrast, enhancement of edges along with detection of boundaries in comparison to other recently developed methods. comprehensive survey indicates the exponential increase in the magnitude of research going on in the medical world for brain cancer indicating the fatal traits of brain tumor. An efficient image contrast enhancement module followed by edge enhancement and segmentation is the primary requirement of any computer aided detection system employed for medical diagnosis. In this paper, a new method for computer aided detection of brain tumor is proposed which consists of contrast improvement of cerebral MRI features followed by segmentation of targeted region of interest (ROI). The proposed framework will aid in the accurate diagnosis of tumor patients. This paper is structured as follows: section I gives a brief introduction of brain tumor. Existing image enhancement techniques have been discussed in the section-III, while an overview of wavelet transform has been given in the third section. Section-IV explains the proposed method. The objective evaluation parameters have been described in the fifth section and the experimental results discussed under section-VI. Seventh section draws the conclusion, whereas the scope for future improvement is given under section VIII.

Mathematical morphological edge detection for remote sensing images

Abstract: Edge detection is a terminology in image processing and computer vision particularly in the areas of feature detection and extraction to refer to the algorithms which aims at identifying points in a digital image at which the image brightness changes sharply or more formally has discontinuities. The need of edge detection is to find the discontinuities in depth, discontinuities in surface orientation, changes in material properties and variations in scene illumination. Remote sensing images are generally corrupted from noise. Mathematical morphology is a new technique for edge detection. It is a theory and technique for analysis and processing of geometrical structures, based on set theory. Mathematical morphology was originally developed for binary images, and later extends to grey scale functions and images. Basically the noise can be easily suppressed by mathematical morphology. So by using mathematical morphology the image can be enhanced and the edges can be detected. The result of edge detection using mathematical morphology will be compared with Sobel edge detector, Prewitt edge detector, laplacian of gaussian edge detector and Canny edge detector.

Selective edge enhancement using anisotropic vortex filter.

Abstract: In optical image processing, selective edge enhancement is important when it is preferable to emphasize some edges of an object more than others. We propose a new method for selective edge enhancement of amplitude objects using the anisotropic vortex phase mask by introducing anisotropy in a conventional vortex mask with the help of the sine function. The anisotropy is capable of edge enhancement in the selective region and in the required direction by changing the power and offset angle, respectively, of the sine function.

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.

Image Denoising And Enhancement Using Multiwavelet With Hard Threshold In Digital Mammographic Images

Abstract: Breast cancer continues to be a significant public health problem in the world.The diagnosing mammography method is the most effective technology for early detection of the breast cancer. However, in some cases, it is difficult for radiologists to detect the typical diagnostic signs, such as masses and microcalcifications on the mammograms . Dense region in digital mammographic images are usually noisy and have low contrast . And their visual screening is difficult to view for physicians. This paper describes a new multiwavelet methodfor noise suppression and enhancement in digital mammographic images. Initially the image is pre-processed to improve its local contrast and discriminations of subtle details.Image suppression and edge enhancement are performed based on the multiwavelet transform. At each resolution, coefficient associated with the noise is modelled and generalized by laplacian random variables. Multiwavel et can satisfy both symmetry and asymmetry which are very important characteristics in Digital image processing. The better denoising result depends on the degree of the noise, generally its energy distributed over low frequency band while both its noise and details are distributed over high frequency band and also applied hard threshold in different scale of frequency sub -bands to limit the image. This paper is proposed to indicat e the suitability of different wavelets and multiwavelet on the neighbourhood in the performance of image denoising algorithms in terms of PSNR. . Finally it compares the wavelet and multiwavelet techniques to produce the best denoised mammographic image using efficient multiwavelet algorithm with hard threshold based on the performance of image denoising algorithm in terms of PSNR values.

Image edge enhancement in optical microscopy with a Bessel-like amplitude modulated spiral phase filter

Abstract: We experimentally demonstrate that a Bessel-like amplitude modulated spiral phase filter can be used in a real-time spatial image edge enhancement system in optical microscopy for biological sample imaging. Compared with previous methods based on a conventional spiral phase filter, a dark-field spiral phase filter and the Laguerre–Gaussian modulated spiral phase filter, the proposed technique further reduces the imaging diffraction noise. Experimental verifications in edge enhancement are implemented by a phase-only spatial light modulator for realizing the amplitude modulated spiral phase. It is shown that the proposed technique is able to efficiently suppress the diffraction noise and achieve high quality edge enhancement images for biological samples.

Biomedical Image Volumes Denoising via the Wavelet Transform

Abstract: Image denoising represents a crucial initial step in biomedical image processing and analysis. Denoising belongs to the family of image enhancement methods (Bovik, 2009) which comprise also blur reduction, resolution enhancement, artefacts suppression, and edge enhancement. The motivation for enhancing the biomedical image quality is twofold. First, improving the visual quality may yield more accurate medical diagnostics, and second, analytical methods, such as segmentation and content recognition, require image preprocessing on the input. Gradually, noise reduction methods developed in other research fields find their usage in biomedical applications. However, biomedical images, such as images obtained from computed tomography (CT) scanners, are quite specific. Modelling noise based on the first principles of image acquisition and transmission is a too complex task (Borsdorf et al., 2009), and moreover, the noise component characteristics depend on the measurement conditions (Bovik, 2009). Additionally, noise reduction must be carried out with extreme care to avoid suppression of the important image content. For this reason, the results of biomedical image denoising should be consulted with medical experts.

Classification of Soil Textures Based on Laws Features Extracted from Preprocessing Images on Sequential and Random Windows

Abstract: Texture analysis has been used for recognizing synthetic and natural textures. Textures are one of the important features in computer vision for image classification and retrieval. An important approach to region description is to quantify its texture content. In this paper ,the Soil images has been analyzed using various image pre processing tasks such as Gray level thresholding, Low pass filter, Edge enhancement using Prewitt's Horizontal filtering and then Feature extraction using 3x3 Law's mask convolution. The features are constructed on preprocessed methods applied on the Soil texture image by considering different types of windows. These features offer a better classification rate. The experimental results on various Soil textures clearly demonstrate the efficiency of the proposed methods.

Phase-contrast digital tomosynthesis.

Abstract: Purpose: Phase-contrast (PC) edge enhancement occurs at the boundary between different tissues and is an interference effect that results from the differential phase-shifts that the x-rays acquire while traversing the two tissues. While observable in planar phase-contrast radiographs, the impact of digital tomosynthesis on this edge enhancement effect has not been previously reported. The purpose of this work is to demonstrate: (1) that phase-contrast digital tomosynthesis (PC-DTS) is possible with a conventional x-ray source, (2) that the reconstructedtomosynthesisimages demonstrate and retain edge enhancement as compared to planar phase-contrast radiographs and (3) tomosynthesis improves object contrast by reducing the effects of superimposed structures. Methods: An unmodified, commercially available cabinet x-ray system (Faxitron LX-60) was used. The system contains a tungsten anode x-ray tube that was operated at 24 kVp and 3 mAs for each PC radiographicimage taken, with a nominal focal spot size of 0.010 mm. The digital detector uses CsI/CMOS with a pixel size of 0.054 mm × 0.054 mm. Objects to be imaged were attached to a computer-controlled rotating motor and are rotated ± 25° about a central position in one degree increments. At each increment, three phase-contrast radiographs are taken and then averaged to reduce the effect of noise. These planar images are then used to reconstruct a series of 56 longitudinal tomographic images with an image offset increment of about 0.7 mm. Results: Tomographic z-plane resolution was measured to be approximately 4 mm. When compared to planar PC images, the tomosynthesisimages were shown to retain the PC boundary edge enhancement in addition to an improvement in object contrast. Conclusions: Our work demonstrates that PC digital tomosynthesis retains the edge-enhancement observed in planar PC radiograph and further improves soft-tissue conspicuity by reducing the effects of superimposed tissue structure.

An effecive night video enhancement algorithm

Abstract: Night video enhancement is important for video surveillance since many objects or activities of interest occur in a dark environment which cannot be seen easily without enhancement. In this paper, we discuss several problems of existing techniques for illumination-fusion based night video enhancement, which fuses video frames from day-time backgrounds and night-time video. We then present a simple enhancement algorithm without these problems. The algorithm uses an additive enhancement term with foreground object extraction and constrained low-passed object illumination to avoid light-inversion and sensitivity problems and to reduce ghost patterns. Experimental results show the effectiveness and robustness of the proposed algorithm.

Adaptive truncated Vector Median Filter

Abstract: In the paper an effective noise reducing and edge enhancing filter for color image enhancement is presented The proposed denoising technique is based on the concept of adaptively truncated vector median and its output is the color pixel which is centrally located in a cluster of most similar pixels belonging to the sliding filtering window The important feature of the proposed deign is its ability to reduce impulsive noise, while sharpening the edges of objects depicted in the image Therefore, the proposed filtering framework can be used in variety of applications in which simultaneous noise reduction and edge enhancement is required

Ultrasonic diagnostic apparatus and ultrasonic image processing apparatus

Abstract: According to one embodiment, an edge information calculation unit calculates edge information based on a generated ultrasonic image. An edge filter unit generates a filtered image from the ultrasonic image by applying a filter having filter characteristics corresponding to the calculated edge information to the ultrasonic image. An edge enhancement unit generates an enhanced image from the filtered image by increasing the brightness value, of the filtered image, which corresponds to the edge information. A high brightness suppression unit generates a composite image of the enhanced image and the ultrasonic image in accordance with a compositing ratio corresponding to the brightness value of the enhanced image.

ISAR enhancement technology based on compressed sensing

Abstract: Based on compressed sensing, an inverse synthetic aperture radar (ISAR) enhancement technology is proposed. The ISAR enhanced imaging system is constructed, and then utilising the sparsity of multi-radar signals, the optimum function for point enhancement and edge enhancement is established. Compared with spectral estimation methods, the new processing provides a higher quality ISAR image without the number estimation of scattering centres. Simulation results demonstrate the validity of the proposed processing.

Processing method for space-occupying lesion ultrasonic images

Abstract: The invention discloses a processing method for space-occupying lesion ultrasonic images The processing method preprocesses the acquired ultrasonic image, comprising the following steps: the removal of the text information around the image, filtration, edge enhancement, the determination of an effective information region and the like; automatic location of a lesion region; determination of the rough contour of the space-occupying lesion, and extraction of the precise contour of the lesion region with the rough contour as an initial contour for an active contour model algorithm The processing method realizes the functions of automatically segmenting the space-occupying lesion ultrasonic image and automatically extracting the region of interest in order to automatically diagnose the space-occupying lesion, thus enhancing the objectivity and accuracy of clinical diagnosis, and therefore the processing method effectively assists the diagnosis of space-occupying lesions

Detail enhancement and noise reduction with true color image edge detection based on wavelet multi-scale

Abstract: To the problem of the existing multi-scale edge detection methods couldn't tackle de-noising and edge detail preservation of images, the article proposed a multi-scale edge detection algorithm which took soft threshold method to implement detail enhancement and noise reduction of the true color image Firstly, obtaining the true color images at different scales through wavelet multi-scale edge detection algorithm, then based on the improved soft threshold filter function, selecting appropriate threshold of the obtained image edges to perform noise reduction while enhance the edge details of the reservation; and finally, carrying out the weighted 2-norm fusion of edges of different-scale-image Experiment results show that the algorithm can make full use of color and gradient information of true color images to effectively suppress noise, enhance the image edge details

Edge enhancement for temporal scaling with metadata

Abstract: Methods for post-processing of non-key frames of an image are described. According to the methods, reconstructed non-key frames are updated with information of neighboring key frames. Methods to evaluate whether to update or not update the non-key frames are also described.

Muti-scale image enhancement based on properties of human visual system

Abstract: The logarithmic image processing (LIP) model is a mathematical framework which has been proved to be consistent with several laws and fit characteristics of the human visual system. In this paper, we both utilize this LIP model and consider characteristics of the human visual system (HVS) to propose a new multi-scale enhancement algorithm. Then a new measure of enhancement based on JND model (Just Noticeable Difference, JND) of human visual system is proposed and used as a tool for evaluating the performance of the enhancement technique. Finally, the proposed algorithm's performance is compared quantitatively to several popular image enhancement algorithms, and experimental results show that the propose algorithm can adjust the image dynamic range, enhance the image details and achieve a more pleasing and comfortable image.

The application of adaptive unsharp mask algorithm in medical image enhancement

Abstract: In order to improve medical image visual quality, this paper adopts an adaptive unsharp mask algorithm for contrast enhancement of medical image. Our objective to employ an adaptive filter is to controls the contribution of the sharpening path in such way that contrast enhancement occurs in high detail areas and little or no image sharpening occurs in smooth areas.

Optical processing of color images with incoherent illumination: orientation-selective edge enhancement using a modified liquid-crystal display.

Abstract: We present a novel optical method for edge enhancement in color images based on the polarization properties of liquid-crystal displays (LCD). In principle, a LCD generates simultaneously two color-complementary, orthogonally polarized replicas of the digital image used as input. The currently viewed image in standard LCD monitors and cell phone’s screens -which we will refer as the “positive image or true-color image”- is the one obtained by placing an analyzer in front of the LCD, in cross configuration to the back polarizer of the display. The orthogonally polarized replica of this image –the “negative image or complementary-color image”- is absorbed by the front polarizer. In order to generate the positive and negative replica with a slight displacement between them, we used a LCD monitor whose analyzer (originally a linear polarizer) was replaced by a calcite crystal acting as beam displacer. When both images are superimposed laterally displaced across the image plane, one obtains an image with enhanced first-order derivatives along a specific direction. The proposed technique works under incoherent illumination and does not require precise alignment, and thus, it could be potentially useful for processing large color images in real-time applications. Validation experiments are presented.

New edge detection algorithms using alpha weighted quadratic filter

Abstract: In this paper, we introduce two novel edge detection algorithms based on a negative alpha weighted quadratic filter. The goal of this work is to utilize the characteristics of the nonlinear filter to preserve and enhance edges for the purpose of edge detection. Unlike traditional edge detection algorithms, which detect edges by using derivatives, the proposed algorithms operate on local regions and modify the color tones of uniform regions while preserving the original edges. We also incorporate the luminance mas2king feature of the Human Visual System by masking the gradient image before edge labeling. Experimental simulations show that the proposed algorithms can extract fine edge information from images contaminated by noise and affected by non-uniform illumination; the obtained edge maps are more consistent to the edges perceived by the human eye. Comparison with existing algorithms will be also presented.

Adaptive edge enhancing technique of impulsive noise removal in color digital images

Abstract: In this paper a novel class of noise attenuating and edge enhancing filters for color image processing is introduced and analyzed. The proposed adaptive filter design is minimizing the cumulative dissimilarity measure of a cluster of pixels belonging to a sliding filtering window and outputs the most centrally located pixel. The proposed filter is computationally efficient, easy to implement and very effective in suppressing impulsive noise, while preserving image details and enhancing its edges. Therefore it can be used in any application in which simultaneous denoising and edge enhancement is a prerequisite for further steps of the color image processing pipeline.

A modified unsharp-masking technique for image contrast enhancement

Abstract: Unsharp masking is a popular and simple technique for contrast enhancement and sharpening in digital images. The basic idea in this technique is to emphasize edges and discontinuities in the image by adding the edge information back to the original image. In order to achieve higher contrast level in the processed image, the edge information can be scaled prior addition to the original image. However, there are no guidelines on how to specify the scaling values. In this paper, we propose a novel technique to scale the edge information automatically. The underlying principle in the proposed technique is to apply the well-known histogram equalization technique on the edge histogram of the image. Equalizing the edge histogram is performed on selected regions of the edge histogram in order to reduce noise amplification in smooth regions and edge ringing artifacts. The proposed technique has been tested on a large set of images qualitatively and quantitatively and is proven to produce satisfactory results.

A developed unsharp masking method for images contrast enhancement

Abstract: In this paper, we propose a developed unsharp masking process for contrast image enhancement. The main idea here is to enhance the dark and bright area in the same way which matches the response of human visual system well. Then in order to reduce the noise effect, a mean weighted high pass filter is used for edge extraction. The proposed method gives satisfactory results for wide range of low contrast images compared with others known approaches.

Image Compression and Resizing for Retinal Implant in Bionic Eye

Abstract: One field where computer-related Image processing technology shows great promise for the future is bionic implants such as Cochlear implants, Retinal implants etc.. Retinal implants are being developed around the world in hopes of restoring useful vision for patients suffering from certain types of diseases like Age-related Macular Degeneration (AMD) and Retinitis Pigmentosa (RP). In these diseases the photoreceptor cells slowly degenerated, leading to blindness. However, many of the inner retinal neurons that transmit signals from the photoreceptors to the brain are preserved to a large extent for a prolonged period of time. The Retinal Prosthesis aims to provide partial vision by electrically activating the remaining cells of the retina. The Epi retinal prosthesis system is composed of two units, extra ocular unit and intraocular implant. The two units are connected by a telemetric inductive link. The Extraocular unit consists of a CCD camera, an image processor, an encoder, and a transmitter built on the eyeglass. High-resolution image from a CCD camera is reduced to lower resolution matching the array of electrodes by image processor, which is then encoded into bit stream. Each electrode in an implant corresponds to one pixel in an image. The bit stream is modulated on a 22 MHz carrier and then transmitted wirelessly to the inside implant. This paper mainly discusses two approaches in image processing which reduces the size of the image without loss of the object detection rate to that of the original image. One is about the related image processing algorithms include image resizing, color erasing, edge enhancement and edge detection. Second one is to generate the saliency map for an image.

Method for enhancing image edge

Abstract: Disclosed is a method for enhancing an image edge. An edge filter is applied to a photographed image, and the determination is performed with respect to whether pixels having edge possibility are noise or form an edge. The determination for the noise and the edge is performed once more with respect to pixels having vagueness for the noise and the edge by using a 3×3 mask. An edge enhancement process is performed through a simple algorithm.