Showing papers on "Edge enhancement published in 2009"

Essential Image Processing and GIS for Remote Sensing

Abstract: Overview of the Book. Part One Image Processing. 1 Digital Image and Display. 1.1 What is a digital image? 1.2 Digital image display. 1.3 Some key points. Questions. 2 Point Operations (Contrast Enhancement). 2.1 Histogram modification and lookup table. 2.2 Linear contrast enhancement. 2.3 Logarithmic and exponential contrast enhancement. 2.4 Histogram equalization. 2.5 Histogram matching and Gaussian stretch. 2.6 Balance contrast enhancement technique. 2.7 Clipping in contrast enhancement. 2.8 Tips for interactive contrast enhancement. Questions. 3 Algebraic Operations (Multi-image Point Operations). 3.1 Image addition. 3.2 Image subtraction (differencing). 3.3 Image multiplication. 3.4 Image division (ratio). 3.5 Index derivation and supervised enhancement. 3.6 Standardization and logarithmic residual. 3.7 Simulated reflectance. 3.8 Summary. Questions. 4 Filtering and Neighbourhood Processing. 4.1 Fourier transform: understanding filtering in image frequency. 4.2 Concepts of convolution for image filtering. 4.3 Low-pass filters (smoothing). 4.4 High-pass filters (edge enhancement). 4.5 Local contrast enhancement. 4.6 *FFT selective and adaptive filtering. 4.7 Summary. Questions. 5 RGB-IHS Transformation. 5.1 Colour coordinate transformation. 5.2 IHS decorrelation stretch. 5.3 Direct decorrelation stretch technique. 5.4 Hue RGB colour composites. 5.5 *Derivation of RGB-IHS and IHS-RGB transformations based on 3D geometry of the RGB colour cube. 5.6 *Mathematical proof of DDS and its properties. 5.7 Summary. Questions. 6 Image Fusion Techniques. 6.1 RGB-IHS transformation as a tool for data fusion. 6.2 Brovey transform (intensity modulation). 6.3 Smoothing-filter-based intensity modulation. 6.4 Summary. Questions. 7 Principal Component Analysis. 7.1 Principle of PCA. 7.2 Principal component images and colour composition. 7.3 Selective PCA for PC colour composition. 7.4 Decorrelation stretch. 7.5 Physical-property-orientated coordinate transformation and tasselled cap transformation. 7.6 Statistic methods for band selection. 7.7 Remarks. Questions. 8 Image Classification. 8.1 Approaches of statistical classification. 8.2 Unsupervised classification (iterative clustering). 8.3 Supervised classification. 8.4 Decision rules: dissimilarity functions. 8.5 Post-classification processing: smoothing and accuracy assessment. 8.6 Summary. Questions. 9 Image Geometric Operations. 9.1 Image geometric deformation. 9.2 Polynomial deformation model and image warping co-registration. 9.3 GCP selection and automation. 9.4 *Optical flow image co-registration to sub-pixel accuracy. 9.5 Summary. Questions. 10 *Introduction to Interferometric Synthetic Aperture Radar Techniques. 10.1 The principle of a radar interferometer. 10.2 Radar interferogram and DEM. 10.3 Differential InSAR and deformation measurement. 10.4 Multi-temporal coherence image and random change detection. 10.5 Spatial decorrelation and ratio coherence technique. 10.6 Fringe smoothing filter. 10.7 Summary. Questions. Part Two Geographical Information Systems. 11 Geographical Information Systems. 11.1 Introduction. 11.2 Software tools. 11.3 GIS, cartography and thematic mapping. 11.4 Standards, interoperability and metadata. 11.5 GIS and the Internet. 12 Data Models and Structures. 12.1 Introducing spatial data in representing geographic features. 12.2 How are spatial data different from other digital data? 12.3 Attributes and measurement scales. 12.4 Fundamental data structures. 12.5 Raster data. 12.6 Vector data. 12.7 Conversion between data models and structures. 12.8 Summary. Questions. 13 Defining a Coordinate Space. 13.1 Introduction. 13.2 Datums and projections. 13.3 How coordinate information is stored and accessed. 13.4 Selecting appropriate coordinate systems. Questions. 14 Operations. 14.1 Introducing operations on spatial data. 14.2 Map algebra concepts. 14.3 Local operations. 14.4 Neighbourhood operations. 14.5 Vector equivalents to raster map algebra. 14.6 Summary. Questions. 15 Extracting Information from Point Data: Geostatistics. 15.1 Introduction. 15.2 Understanding the data. 15.3 Interpolation. 15.4 Summary. Questions. 16 Representing and Exploiting Surfaces. 16.1 Introduction. 16.2 Sources and uses of surface data. 16.3 Visualizing surfaces. 16.4 Extracting surface parameters. 16.5 Summary. Questions. 17 Decision Support and Uncertainty. 17.1 Introduction. 17.2 Decision support. 17.3 Uncertainty. 17.4 Risk and hazard. 17.5 Dealing with uncertainty in spatial analysis. 17.6 Summary. Questions. 18 Complex Problems and Multi-Criteria Evaluation. 18.1 Introduction. 18.2 Different approaches and models. 18.3 Evaluation criteria. 18.4 Deriving weighting coefficients. 18.5 Multi-criteria combination methods. 18.6 Summary. Questions. Part Three Remote Sensing Applications. 19 Image Processing and GIS Operation Strategy. 19.1 General image processing strategy. 19.2 Remote-sensing-based GIS projects: from images to thematic mapping. 19.3 An example of thematic mapping based on optimal visualization and interpretation of multi-spectral satellite imagery. 19.4 Summary. Questions. 20 Thematic Teaching Case Studies in SE Spain. 20.1 Thematic information extraction (1): gypsum natural outcrop mapping and quarry change assessment. 20.2 Thematic information extraction (2): spectral enhancement and mineral mapping of epithermal gold alteration, and iron ore deposits in ferroan dolomite. 20.3 Remote sensing and GIS: evaluating vegetation and land-use change in the Nijar Basin, SE Spain. 20.4 Applied remote sensing and GIS: a combined interpretive tool for regional tectonics, drainage and water resources. Questions. References. 21 Research Case Studies. 21.1 Vegetation change in the three parallel rivers region, Yunnan province, China. 21.2 Landslide hazard assessment in the three gorges area of the Yangtze river using ASTER imagery: Wushan-Badong-Zogui. 21.3 Predicting landslides using fuzzy geohazard mapping an example from Piemonte, North-west Italy. 21.4 Land surface change detection in a desert area in Algeria using multi-temporal ERS SAR coherence images. Questions. References. 22 Industrial Case Studies. 22.1 Multi-criteria assessment of mineral prospectivity, in SE Greenland. 22.2 Water resource exploration in Somalia. Questions. References. Part Four Summary. 23 Concluding Remarks. 23.1 Image processing. 23.2 Geographical information systems. 23.3 Final remarks. Appendix A: Imaging Sensor Systems and Remote Sensing Satellites. A.1 Multi-spectral sensing. A.2 Broadband multi-spectral sensors. A.2.1 Digital camera. A.2.2 Across-track mechanical scanner. A.2.3 Along-track push-broom scanner. A.3 Thermal sensing and thermal infrared sensors. A.4 Hyperspectral sensors (imaging spectrometers). A.5 Passive microwave sensors. A.6 Active sensing: SAR imaging systems. Appendix B: Online Resources for Information, Software and Data. B.1 Software - proprietary, low cost and free (shareware). B.2 Information and technical information on standards, best practice, formats, techniques and various publications. B.3 Data sources including online satellite imagery from major suppliers, DEM data plus GIS maps and data of all kinds. References. General references. Image processing. GIS. Remote sensing. Part One References and further reading. Part Two References and further reading. Index.

Spiral phase filtering and orientation-selective edge detection/enhancement

Abstract: A spiral phase plate with an azimuthal structure exp[iϕ](0⩽ϕ<2π) has been used as a filter in a 4f system to achieve edge enhancement. Generally such edge-enhanced effect is isotropic, i.e., each edge of an input pattern is enhanced to the same degree regardless of its orientation. We found that one can achieve anisotropic edge enhancement by breaking down the symmetry of the filtering process. This can be done in two ways: first, by use of a fractional spiral phase filter (SPF) with a fractional topological charge and a controllable orientation of the edge discontinuity, and second, by the lateral shifting of the SPF. We interpret this process as a vortex formation due to the diffraction of the Fourier spectrum of the input pattern by a SPF with an integer and fractional topological charge. Optical experiments using a spatial light modulator were carried out to verify our proposal.

Detection and segmentation of cervical cell cytoplast and nucleus

Abstract: This article aims to develop a method for the detection and segmentation of a cytoplast and nucleus from a cervix smear image. First, the technique of equalization method with Gaussian filter is adopted to eliminate noise in the image. Second, a new edge enhancement technique is proposed to work out the coarseness of each pixel, which is later used as a determining characteristic of reinforced object images. A two-group object enhancement technique is then used to reinforce this object according to rough pixels. Third, the proposed detector enhances the gradients of the edges of the cytoplast and nucleus while suppressing the noise gradients, and then specifies the pixels with higher gradients as possible edge pixels. Finally, it picks out the two longest closed curves constructed by part of the edge pixels. Detection and segmentation performance of the proposed method is later compared with seed region growing feature extraction and level set method using 10 cervix smear images as example. Besides comparing the contour segment of the cytoplast and nucleus obtained by using different methods, we also compare the quality of the segmentation results. Experimental results show that the proposed detector demonstrates an impressive performance. © 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 260–270, 2009

A new edge recognition technology based on the normalized vertical derivative of the total horizontal derivative for potential field data

Abstract: Edge detection and enhancement techniques are commonly used in recognizing the edge of geologic bodies using potential field data. We present a new edge recognition technology based on the normalized vertical derivative of the total horizontal derivative which has the functions of both edge detection and enhancement techniques. First, we calculate the total horizontal derivative (THDR) of the potential-field data and then compute the n-order vertical derivative (VDRn) of the THDR. For the n-order vertical derivative, the peak value of total horizontal derivative (PTHDR) is obtained using a threshold value greater than 0. This PTHDR can be used for edge detection. Second, the PTHDR value is divided by the total horizontal derivative and normalized by the maximum value. Finally, we used different kinds of numerical models to verify the effectiveness and reliability of the new edge recognition technology.

Acousto-optic image processing

Abstract: Acousto-optic processing of images is based on the angular selectivity of acousto-optic interaction resulting in spatial filtration of the image spectrum. We present recent theoretical and experimental investigations carried out in this field. Much attention is given to the analysis of the acousto-optic cell transfer function form depending on the crystal cut, the geometry of acousto-optic interaction, and the ultrasound frequency. Computer simulation results of the two-dimensional acousto-optic spatial filtration of some elementary images are presented. A new method of phase object visualization is suggested and examined that makes it possible to separate amplitude and phase information contained in an optical image. The potentialities of the acousto-optic image processing are experimentally demonstrated by examples of edge enhancement and optical wavefront visualization effects.

A novel approach to image edge enhancement using Artificial Bee Colony optimization algorithm for hybridized smoothening filters

Abstract: In this Modern era, image transmission and processing plays a major role. It would not be possible to retrieve information from satellite and medical images without the help of Image processing techniques. Image edge Enhancement is the art of examining images for identifying objects and judging their significance. The proposed work uses the concept of Artificial Bee Colony Algorithm which proved to be the most powerful unbiased optimization technique for sampling a large solution space. Because of its unbiased stochastic sampling, it was quickly adapted in image processing and thus for image edge enhancement as well. This paper deals with the techniques that help in improvising the quality of the image edges and in solving various complex image processing tasks such as segmentation, feature extraction, classification and image generation. The edge enhancement is done using hybridized smoothening filters by The Artificial Bee Colony optimization algorithm and compared it with the genetic algorithm.

High-resolution investigations of edge effects in neutron imaging

Abstract: Edge enhancement is the main effect measured by the so-called inline or propagation-based neutron phase contrast imaging method. The effect has originally been explained by diffraction, and high spatial coherence has been claimed to be a necessary precondition. However, edge enhancement has also been found in conventional imaging with high resolution. In such cases the effects can produce artefacts and hinder quantification. In this letter the edge effects at cylindrical shaped samples and long straight edges have been studied in detail. The enhancement can be explained by refraction and total reflection. Using high-resolution imaging, where spatial resolutions better than 50 μm could be achieved, refraction and total reflection peaks – similar to diffraction patterns – could be separated and distinguished.

Computed tomographic imaging protocol for the canine cervical and lumbar spine

Abstract: Computed tomography (CT) has been applied previously for assessment of canine spinal disease using a multitude of different technical imaging parameters. The purpose of this study was to establish an optimized imaging protocol for the cervical and lumbar canine spine using a single-detector-row helical CT unit. Thin slice thickness (1-2mm), low pitch (axial scan mode, helical pitch <2), and medium-frequency image reconstruction algorithm significantly improved the visibility of the intervertebral disk and spinal cord. Tube current, helical reconstruction interval, and the use of an additional edge enhancement filter had no significant effect on the visibility of the intervertebral disk and spinal cord. There was also no interaction between the use of an additional edge enhancement filter and image reconstruction algorithm. Use of an additional edge enhancement filter introduced a double ring artifact in the periphery of the spinal canal lumen that did not correspond to the spinal cord or pachymeningeal margin.

Edge extraction apparatus, surveying equipment, and program

Abstract: An edge extraction device can reduce detected noise other than a contour of an article, and can improve the operability. The edge extraction device includes: an edge detection section which calculates edge strength from an image and detects an edge; a labeling processing section which performs labeling processing on the edge detected by the edge detection section and calculates a length of the edge; an edge enhancement processing section which performs edge enhancement processing by using a value corresponding to the length of the edge, which is calculated by the labeling processing section, and the edge strength, which is calculated by the edge detection; and an edge extraction section which performs binarization processing on a value of the image, which is enhanced by the edge enhancement processing section, by using an adjustable threshold value, and extracts a predetermined edge.

Computer Assistance Image Processing Spores Counting

Abstract: To monitor plant disease which caused by spores, it is needed to obtain the spores image exterior outline characteristic, prepares for spores type analysis and automatic counting. This paper uses computer assistance digital image processing, realized image pretreatment, determined the threshold value reasonably, and transformed the gray image to binary image. Images was captured by CCD and input into computer for process. A kind of gray images research method has been proposed. In order to remove low frequency components, the input gray image is preprocessed by edge enhancement using the Median filter and canny edge algorithm. In this foundation using the imagery processing technology carries on morphology analyses, feature extraction and track. Its goal is obtains the spore image exterior outline characteristic, completes the spores type analysis, counting and distinguish.

Edge enhancement and image equalization by unsharp masking using self-adaptive photochromic filters.

Abstract: A new method for real-time edge enhancement and image equalization using photochromic filters is presented. The reversible self-adaptive capacity of photochromic materials is used for creating an unsharp mask of the original image. This unsharp mask produces a kind of self filtering of the original image. Unlike the usual Fourier (coherent) image processing, the technique we propose can also be used with incoherent illumination. Validation experiments with Bacteriorhodopsin and photochromic glass are presented.

Depth image-based rendering with low resolution depth

Abstract: This paper proposes a new approach for depth image-based rendering (DIBR) with low resolution depth using the 3D propagation algorithm. Our novel depth edge enhancement method efficiently corrects and sharpens the depth edges in the propagated depth image using available high resolution color information. Experimental results show that only with 4% depth information kept for low resolution depth image, the proposed method can provide comparable rendering quality to that of the high resolution case. Furthermore, the proposed work is developed to match the fine-grain parallelism of general-purpose graphics processing units (GPGPUs) and hence can be accelerated to nearly real-time operations in low cost DIBR systems.

Apparatus, method and program for processing image

Abstract: PROBLEM TO BE SOLVED: To increase the image quality of an output image when an input image is converted into an output image having further increased resolution. SOLUTION: A super-resolution processing unit 21 converts the input image into an SR image having a higher resolution by super-resolution processing using the input image, and the other input image including the same subject as that of the input image. A diagonal interpolation processing unit 22 performs edge enhancement processing so as to convert the input image into an enhancement image having a higher resolution. A weight computing unit 43 computes weight using a motion mask showing an area of the moving subject on the input image and a weight map in which lines showing the subject on the input image are displayed. An adder unit 44 performs weight addition of the SR image and the enhancement image using the computed weight to generate the output image. This technology is applicable to an image conversion device. COPYRIGHT: (C)2010,JPO&INPIT

Extraocular image processing for retinal prosthesis based on DSP

Abstract: Simulations of artificial vision suggest that thousands of electrodes may be required to restore vision for ones with diseases of the outer retina. With the development of MEMS fabrication process for the stimulation electrode array, extraocular image processing is becoming more and more important for the retinal prosthesis systems. A Digital Signal Processor (DSP) based extraocular image processing system (EIPS) for a retinal prosthesis has been developed in this paper. The system mainly consists of a CMOS image sensor and a DSP processing system, which provides the capability of implmenting the real-time image processing with low power consumption. Furthermore, this system offers the flexibility of realizing various image processing algorithms with different specification requirements on the DSP by programming, such as different frame rate, resolution and throughput data rate. The related image processing algorithms include the image resizing, color erasing, edge enhancement and edge detection. Finally, the speed of different DSPs in the market has been evaluated and compared for achieving better performance.

New Method for Image Denoising while Keeping Edge Information

Abstract: Edge information is the most important high- frequency information of an image, so we should try to maintain more edge information while denoising. In order to preserve image details as well as canceling image noise, we present a new image denoising method: image denoising based on edge detection. Before denoising, image's edges are first detected, and then the noised image is divided into two parts: edge part and smooth part. We can therefore set high denoising threshold to smooth part of the image and low denoising threshold to edge part. The theoretical analyses and experimental results presented in this paper show that, compared to commonly-used wavelet threshold denoising methods, the proposed algorithm could not only keep edge information of an image, but also could improve signal-to-noise ratio of the denoised image.

Multiscale deformable registration for dual-energy x-ray imaging.

Abstract: Dual-energy (DE) imaging of the chest improves the conspicuity of subtle lung nodules through the removal of overlying anatomical noise. Recent work has shown double-shot DE imaging (i.e., successive acquisition of low- and high-energy projections) to provide detective quantum efficiency, spectral separation (and therefore contrast), and radiation dose superior to single-shot DE imaging configurations (e.g., with a CR cassette). However, the temporal separation between high-energy (HE) and low-energy (LE) image acquisition can result in motion artifacts in the DE images, reducing image quality and diminishing diagnostic performance. This has motivated the development of a deformable registration technique that aligns the HE image onto the LE image before DE decomposition. The algorithm reported here operates in multiple passes at progressively smaller scales and increasing resolution. The first pass addresses large-scale motion by means of mutual information optimization, while successive passes (2-4) correct misregistration at finer scales by means of normalized cross correlation. Evaluation of registration performance in 129 patients imaged using an experimental DE imaging prototype demonstrated a statistically significant improvement in image alignment. Specific to the cardiac region, the registration algorithm was found to outperform a simple cardiac-gating system designed to trigger both HE and LE exposures during diastole. Modulation transfer function (MTF) analysis reveals additional advantages in DE image quality in terms of noise reduction and edge enhancement. This algorithm could offer an important tool in enhancing DE image quality and potentially improving diagnostic performance.

Extrapolation and Correlation (EXTRACT): A New Method for Motion Compensation in MRI

Abstract: A postprocessing technique is proposed for the correction of both translational and rotational motion artifacts in magnetic resonance imaging (MRI) The method consists of two steps: 1) k-space extrapolation to generate a motion-free reference, followed by 2) correlation with actual data to estimate motion In this paper, two different extrapolation methods were investigated for the purpose of motion estimation: edge enhancement and finite-support solution It was found that finite-support solution performs better near the k-space center, while the edge enhancement method is superior in the outer k-space regions Therefore, a combination of the two methods was employed to generate a motion-free reference, whose correlations with the acquired data can subsequently determine the object motion Motion compensation was demonstrated in simulation and in vivo MR experiments The technique is shown to be robust against noise and various types of motion

Image edge enhancing method

Abstract: The invention is applicable to the field of digital image processing and provides an image edge enhancing method. The method comprises: according to the two-step gradients of central pixel point in different directions, judging the edge orientation of an image; carrying out the interpolation of the central pixel point and calculating the lost color component of pixel points; and on the basis of Bayer data, enhancing the edge of the image in an interpolation template according to the color of the original component of the central pixel point and the edge orientation of the image. The embodiment of the invention takes the influences of green component values of different pixel points around the central pixel point into full consideration while carrying out edge enhancement of the image, adopts an adaptive edge enhancement algorithm, eliminates the possible influences of noises on the edge, achieves uniform image edge and helps the processed images to achieve good effect. Meanwhile, due to the edge enhancement processing in the interpolation template on the basis of Bayer data, the method saves SRAM required by single edge enhancement processing, as well as saves large areas of elements and costs.

Noise Reduction and Edge Enhancement Based on Band-Pass Epsilon-filter

Abstract: Band-pass bilateral filter is an improved bilateral filter which does not have low-pass characteristics but has band-pass characteristics to enhance image contrast around edges. However, the computation time is relatively large due to Gaussian calculation in all pixels. To reduce the calculation cost, we look to a nonlinear filter called epsilon-filter and propose an advanced epsilon-filter which does not have low-pass charactersitics but has band-pass characteristics to enhance the image contrast around edge, namely band-pass epsilon-filter. Due to its simple design, the calculation cost is relatively small the same as epsilon-filter. To show the effectiveness of the proposed method, we also report the results of some comparative experiments concerning the filter characteristics and computational cost.

Video display device

Abstract: PROBLEM TO BE SOLVED: To provide a display device in which even when an outside scene (outside world) is changed, concentration of a user is hardly direct to the change, while avoiding a state where the outside scene looks dark SOLUTION: When a CPU 8 receives signals from a timer circuit 11 at prescribed intervals, or when the CPU 8 receives the signal that an operating section 4 is operated (for example, a switch is depressed), the CPU 8 sends the signal to an image processing section 9 The video signal inputted to the image processing section 9 from a media player section 3 via a frame memory 10 is subjected to image processing by the image processing section 9 to impart variation to the video signal As a method of imparting the variation, at least one among luminance, chromaticity, contrast, γ correction value, and edge enhancement are changed for 1 to 2 seconds for the entire part or a part of the video to be displayed As a result, the user feels the change in the circumstances of the display screen and concentrates the attention to the display picture COPYRIGHT: (C)2009,JPO&INPIT

Solid-state image-capturing apparatus, camera, and method of processing signal

Abstract: A solid-state image-capturing apparatus that converts light, which is reflected from a subject, into an electrical signal, includes an image processing unit that performs edge enhancement on a digital video signal that is generated based on an analog video signal, which is obtained from the light captured by an image-capturing device and amplified with a predetermined analog gain, based on position information on the image-capturing device and the analog gain.

A Fast and Robust Super Resolution Method for Intima Reconstruction in Medical Ultrasound

Abstract: Accurate measurement of intima-media thickness (IMT) in ultrasound images is clinically meaningful but also difficult because of low resolution. This paper proposes an approach to reconstruct a resolution-enhanced intima from a sequence of acquired images using a maximum a-posteriori framework. Anisotropic diffusion is used to reduce speckle with edge enhancement during reconstruction. For real time application in an ultrasound system, a fast and robust approach is employed in the presence of outliers. Finally, an iterative process is used to achieve a single high-resolution image with a better defined intima boundary than the original images. Our approach may be a valuable preprocessing step to image segmentation and IMT measurement.

Split edge enhancement architecture

Abstract: A system and method for enhancing the detail edges and transitions in an input video signal. This enhancement may be accomplished by enhancing small detail edges before up-scaling and enhancing large amplitude transitions after up-scaling. For example, detail edge enhancement (detail EE) may be used to enhance the fine details of an input video signal. An edge map may be used to prevent enhancing the large edges and accompanying mosquito noise with the detail enhancement. Noise may additionally be removed from the signal. After the fine details are enhanced, the signal may be up-scaled. Luminance transition improvement (LTI) or chrominance transition improvement (CTI) may be used to enhance the large transitions of the input video signal post scaler.

Hybrid filtering and enhancement of high-resolution adaptive-optics retinal images.

Abstract: Adaptive optics flood-illuminated imaging technology has been successfully used to correct the wavefront aberration of human eyes to obtain high-resolution retinal images. However, because of the pollution of various types of noise and the degradation caused by residual aberration, the noisy images are not very clear and weak edges are difficult to discern. To reveal the abundant detail hidden by large-scale noise and to enhance low-contrast edges, a hybrid filtering and enhancment method is proposed combining bilateral filtering, coherence diffusion, and edge enhancement. Results show that it is effective to improve the visual quality of retinal cell images. (C) 2009 Optical Society of America

Image Enhancement Based on Nonsubsampled Contourlet Transform

Abstract: Based on nonsubsampled contourlet transform and image statistical property, an image enhancement method is presented. A noise variance estimate algorithm is given using nonsubsampled Laplace Pyramid decompose, and a wake edge enhancement algorithm using directional local neighborhood is proposed. This paper estimates the variance in each decomposed direction, and the directional local neighborhood statics is used to distinguish the strong edge, wake edge and noise. The wake edge is enhanced and the speckled noise is reduced. Experimental results show that the method represents better performance in wake edges information enhancement and speckle reduction.

Enhancement of optical images using hybrid edge detection technique

Abstract: Edge structures which are boundaries of object surfaces are essential image characteristic in computer vision and image processing. As a result, edge detection becomes part of the core feature extraction in many object recognition and digital image applications. This paper presents a new hybrid edge detector that combines the advantages of Prewitt, Sobel and optimized Canny edge detectors to perform edge detection while eliminating their limitations. The optimum Canny edges are obtained through varying the Gaussian filter standard deviation and the threshold value. Simulation results show that the proposed hybrid edge detection method is able to consistently and effectively produce better edge features even in noisy images. Compared to the other three edge detection techniques, the hybrid edge detector has demonstrated its superiority by returning specific edges with less noise.

An Edge Detection and Filtering Mechanism of Two Dimensional Digital Objects Based on Fuzzy Inference

Abstract: The general idea behind the filter is to average a pixel using other pixel values from its neighborhood, but simultaneously to take care of important image structures such as edges. The main concern of the proposed filter is to distinguish between any variations of the captured digital image due to noise and due to image structure. The edges give the image the appearance depth and sharpness. A loss of edges makes the image appear blurred or unfocused. However, noise smoothing and edge enhancement are traditionally conflicting tasks. Since most noise filtering behaves like a low pass filter, the blurring of edges and loss of detail seems a natural consequence. Techniques to remedy this inherent conflict often encompass generation of new noise due to enhancement. In this work a new fuzzy filter is presented for the noise reduction of images corrupted with additive noise. The filter consists of three stages. (1) Define fuzzy sets in the input space to computes a fuzzy derivative for eight different directions (2) construct a set of IFTHEN rules by to perform fuzzy smoothing according to contributions of neighboring pixel values and (3) define fuzzy sets in the output space to get the filtered and edged image. Experimental results are obtained to show the feasibility of the proposed approach with two dimensional objects. Keywords—Additive noise, edge preserving filtering, fuzzy image filtering, noise reduction, two dimensional mechanical images.

Edge enhancement for in-phase focal modulation microscope

Abstract: In-phase focal modulation microscopy (IPFMM) with single photon excited fluorescence is presented. Optical transfer functions and images of thin and thick fluorescent edges in IPFMM are investigated. The results show that, compared with confocal microscopy, using IPFMM can result in a sharper image of the edge, and the edge gradient can be increased up to 75.4% and 58.9% for a thick edge and a thin edge, respectively. Signal level is also discussed, and the results show that, to obtain high transverse resolution with IPFMM, the normalized detector pinhole radius should not exceed 2.8.

Systems and methods for enhancing medical images

Abstract: A system and method is provided for enhancing a region of interest in a medical image to improve its visibility. A region of interest is first identified in the medical image, such as identifying a breast region in a mammography image. The identified region of interest is then enhanced using an image processing technique, for example by adjusting the intensity or contrast, or by performing edge enhancement. Other regions of the medical image outside the region of interest remain unaltered, or may be diminished, such that the clarity of the region of interest is improved in comparison with the other regions of the medical image. A user viewing the enhanced image is less distracted by the non-enhanced regions and is not required to adjust the image on his or her own. The user can more quickly and effectively review the medical image to identify abnormalities and diagnose disease.