Showing papers on "Edge enhancement published in 1996"

Nonlinear unsharp masking methods for image contrast enhancement

Abstract: In the unsharp masking approach for image enhancement, a fraction of the highpass filtered version of the image is added to the original image to form the enhanced version The method is simple, but it suffers from two serious drawbacks First, it enhances the contrast in the darker areas perceptually much more strongly than that in the lighter areas Second, it enhances the noise and/or digitization effects, particularly in the darker regions, resulting in visually less pleasing enhanced images In general, noise can be suppressed with lowpass filters, which are associated with the blurring of the edges On the other hand, contrast can be enhanced with highpass filters, which are associated with noise amplification A reasonable solution, therefore, is to use suitable nonlinear filters which combine the features of both highpass and lowpass filters This paper outlines several new methods of unsharp masking based on the use of such nonlinear filters Computer simulations have verified the superior results obtained using these filters In addition, a new measure of contrast enhancement is introduced which quantitatively supports the improvement obtained using the proposed methods

Contrast enhancing illuminator

Abstract: An apparatus which enhances the visual contrast between bright and dark features of an object by projecting an image of the object onto the object. The features of the projected object image overlay the features of the object such that an observer of the enhanced object senses that the bright features are brighter and the dark features remain the same. A light source illuminates the object, a video imaging means, such as a video camera, creates a video signal representative of the object image, and a video projector receives the video signal from the video camera and projects a visual image of the object. A filter prevents video projector light from reaching the video imaging means, thereby eliminating positive feedback. A preferred apparatus includes a beam separator which causes the image projected from the video projector to illuminate the object from the same perspective that the video imaging means views the object. Preferably, the light source is an infrared light source, the filter transmits infrared light but not visible light, and the beam separator reflects visible light and transmits infrared light. Alternately, the invention visually enhances the edges of features of an object with an unsharp masking edge enhancement technique, thereby making the features of the object easier to distinguish.

Edge-enhanced image zooming

Abstract: A new adaptive algorithm for image interpolation with percep- tual edge enhancement is proposed. Here, perceptual means that edges are enhanced and interpolated in a visually pleasing way. Each pixel neighborhood is classified into one of three categories (constant, ori- ented, or irregular). In each case, an optimal interpolation technique finds the missing pixels without generating unpleasant artifacts such as aliasing or ringing effects. Furthermore, a quadratic Volterra filter is em- ployed to extract perceptually important details from the original image, which are then used to improve the overall sharpness and contrast. Both qualitative and quantitative simulation results clearly show the superiority of our method over standard low-pass interpolation algorithms such as bilinear, diamond-filter, or B-spline interpolation. © 1996 Society of Photo-Optical Instrumentation Engineers. Subject terms: image interpolation; zooming; quadratic Volterra filters; detail en- hancement; image enhancement; edge enhancement.

The computational eye

Abstract: Specialized cells in the eye work in parallel for unequaled image processing and computation. Millionfold swings in light intensity from the outside world, transformed to electrical signals, are processed in space and time for contrast and edge enhancement, as well as to detect motion.

A general framework for quadratic Volterra filters for edge enhancement

Abstract: An inherent problem in most image enhancement schemes is the amplification of noise, which, due to Weber's law, is mostly visible in the darker portions of an image. Using a special class of quadratic Volterra filters, we can adapt the enhancement process in a computationally efficient way to the local image brightness because these filters are approximately equivalent to the product of a local mean estimator and a highpass filter. We analyze and derive this subclass of quadratic Volterra filters by investigating the 1-D case first, and then we generalize the results to two dimensions. An important property of these filters is that they map sinusoidal inputs to constant outputs, which allows us to develop a new filter characterization that is more intuitive for our application than the 4-D frequency response. This description finally leads to a novel least-squares design methodology. Image enhancement results using our Volterra filters are superior to those obtained with standard linear filters, which we demonstrate both quantitatively and qualitatively.

Video signal decoding apparatus

Abstract: A video signal decoding apparatus according to the present invention includes a decoding unit for applying a variable length and run-length decoding to a compressed video signal to reconstruct blocks of quantized coefficients. An inverse quantizer is provided for inverse quantizing blocks of reconstructed quantized coefficients. An inverse DCT transforms the inverse quantized blocks of reconstructed quantized coefficients to produce a decoded block. An edge block detector determines whether decoded blocks are classified as an edge block. An adaptive edge enhancement filter removes the distortion in the decoded blocks when the edge block detector classifies decoded blocks as edge blocks. Thus, the video signal is decoded from the encoded video signal.

Resolution enhancement system for digital images

Abstract: An image processing system for providing edge enhancement of low resolution binary image files processes the low resolution data in sections or windows using a binary edge enhancement processor unit to determine whether an edge transition or "kink site" occurs within each window. The edge enhancement processor includes a decision matrix that identifies a pixel based on a calculated gradient magnitude and direction. In one operating mode, the original data in the window is replaced with edge enhanced gray-scale data stored in a look-up table if a kink site is identified. In a second operating mode original data is replaced with higher resolution binary data. To improve speed of operation of the processor, a set of criteria used for determining where enhancement pixels are to be generated has logic operators used for processing the criteria operated simultaneously with a bias provided favoring determinations having closer proximity towards a site likely to require image enhancements. In addition a raster line of pixels is separated into segments and each segment operated on in parallel.

Car Number Plate Detection With Edge Image Improvement

Abstract: In an automatic car number plate reading system, edge density can be used to successfully detect a number plate location for character recognition process due to the characteristics of the number plate. However the initial false detection rate will be increased along with the noise presented in a car image. A method is developed to improve the edge image by suppressing unwanted detail of a car image. It increases the Edge Density Discrimination in a car number plate location and reduces the initial false detection rate to optimize the detection performance.

Extended permutation filters and their application to edge enhancement

Abstract: Extended permutation (EP) filters are defined and analyzed. In particular, we focus on extended permutation rank selection (EPRS) filters. These filters are constrained to output an order statistic from an extended observation vector. This extended vector includes N observation samples and K statistics that are functions of the observation samples. The rank permutations from selected samples in this extended observation vector are used as the basis for selecting an order statistic output. We show that by including the sample mean in the extended observation vector, the filters exhibit excellent edge enhancement properties. We also show that several previously defined classes of rank-order-based edge enhancers (CS, LUM, and WMMR sharpeners) can be formulated as subclasses of EPRS filters. These sharpening subclasses are in addition to the smoothing subclasses, which include rank conditioned rank selection, permutation stack, and weighted order statistic filters. Thus, this novel class of filters provides a broad framework within which many rank-order-based smoothers and edge enhancers can be unified. Edge enhancement properties are developed and an L/sub n/ norm EPRS filter optimization procedure is presented. Finally, extensive computer simulation results are presented, comparing the performance of EPRS and other sharpening filters in edge enhancement applications.

Edge enhancement of infrared imagery by way of the anisotropic diffusion pyramid

Abstract: The problem of enhancing and detecting edges in infrared images is addressed in this paper. The proposed solution utilizes a multiresolution structure called the anisotropic diffusion pyramid (ADP), which is created by successive application of anisotropic diffusion and subsampling. A pyramid node linking process is used to segment the image and to detect edges. For the slowly varying sigmoidal edges in the IR imagery, it is shown that the diffusion process is effective in edge enhancement. Furthermore, system parameters are derived to maximize the edge sharpening ability of the ADP. The ADP avoids the "staircase" artifacts of single-resolution diffusion algorithms and avoids the localization and region merging problems of pyramids based on linear filters.

Multiresolution unsharp masking technique for mammogram image enhancement

Abstract: A multi-resolution unsharp masking (USM) technique is developed for image feature enhancement in digital mammogram images. This technique includes four processing phases: (1) determination of parameters of multi-resolution analysis (MRA) based on the properties of images; (2) multi-resolution decomposition of original images into sub-band images via wavelet transformation with perfect reconstruction filters; (3) modification of sub-band images with adaptive unsharp masking technique; and (4) reconstruction of image from modified sub- band images via inverse wavelet transformation. An adaptive unsharp masking technique is applied to the sub-band images in order to modify the pixel values based on the edge components at various frequency scales. Smoothing and gain factor parameters, employed in the unsharp masking, are determined according to the resolution, frequency, and energy content of the sub-band images. Experimental results show that this technique is able to enhance the contrast of region of interest (microcalcification clusters) in mammogram image.

A new probabilistic relaxation scheme and its application to edge detection

Abstract: This paper presents a new scheme for probabilistic relaxation labeling that consists of an update function and a dictionary construction method. The nonlinear update function is derived from Markov random field theory and Bayes' formula. The method combines evidence from neighboring label assignments and eliminates label ambiguity efficiently. This result is important for a variety of image processing tasks, such as image restoration, edge enhancement, edge detection, pixel classification, and image segmentation. The authors successfully applied this method to edge detection. The relaxation step of the proposed edge-detection algorithm greatly reduces noise effects, gets better edge localization such as line ends and corners, and plays a crucial role in refining edge outputs. The experiments show that our algorithm converges quickly and is robust in noisy environments.

Detection of drainage channel networks on digital satellite images

Abstract: We present a technique for automatic detection of drainage channel networks on single digital images acquired by conventional remote sensing satellites such as Landsat and SPOT. Since these satellites are Sun-synchronous, the approximate local Sun angle at time or image acquisition is always known. Consequently, the spatial behaviour of shading with respect to channels and other topo-morphological features is known. This knowledge has been used to advantage in this work. We use a multi-level knowledge-based approach for this detection process. The first and lowest level deals with image processing: radiometric and edge enhancement, edge detection and consolidation, and skeletonization; resulting in a complex network of lines. The second (intermediate) level of knowledge for network extraction performs a categorization of line segments in order of resemblance to channel elements based on the strength and local disposition of shading. The highest level of knowledge that has been applied, reconstruc...

Video signal processing apparatus with noise reduction and edge enhancement

Abstract: A video signal processing circuit for processing a video signal including a noise is disclosed which comprises: an edge enhancement signal generation circuit for generating an edge enhancement signal from the video signal; a noise reduction circuit for reducing a level of the noise to generate a noise reduced video signal; and an adder for adding the edge enhancement signal to the noise reduction video signal to output a processed video signal. The apparatus may further comprise an input terminal for receiving the video signal, wherein the edge enhancement signal generation circuit and the noise reduction circuit coupled to the input terminal are in parallel. This apparatus may further comprise a first delay circuit for delaying the video signal by one horizontal line period to generate a 1-H delayed video signal and a second delay circuit responsive to the video signal for generating a 2-H delayed video signal, wherein the edge enhancement signal generation circuit generates the edge enhanced signal from the video signal, the 1-H delayed video signal, and the 2-H delayed video signal and the noise reduction circuit generates the noise reduced video signal from the video signal, the 1-H delayed video signal, and the 2-H-delayed video signal.

Video signal decoding apparatus with artifact reduction

Abstract: A video signal decoding apparatus (Dv) according to the present invention includes a decoding unit (141) for applying a variable length and run-length decoding to the compressed video signal (Sc) to reconstruct blocks of quantized coefficients. An inverse quantizer (143) is provided for inverse quantizing blocks of reconstructed quantized coefficients. An inverse DCT (145) transforms the inverse quantized blocks of reconstructed quantized coefficients to produce a decoded block. An edge block detector (153,149 and 158) determines whether said decoded block is classified as an edge block or not. An adaptive edge enhancement filter (162) removes the distortion in the decoded block when the edge block detector (153) determines as an edge block. Additionally, a ringing noise filter is disclosed for the same decoding apparatus.

Image recovery and segmentation using competitive learning in a layered network

Abstract: In this study, we have used the principle of competitive learning to develop an iterative algorithm for image recovery and segmentation. Within the framework of Markov random fields (MRFs), the image recovery problem is transformed to the problem of minimization of an energy function; A local update rule for each pixel point is then developed in a stepwise fashion and is shown to be a gradient descent rule for an associated global energy function. The relationship of the update rule to Kohonen's update rule is shown. Quantitative measures of edge preservation and edge enhancement for synthetic images are introduced. As compared to recently published results using mean field approximation, our algorithm shows consistently better performance in edge preservation and comparable performance in enhancing within the boundaries. These results are based on simulation experiments on a set of synthetic images corrupted by Gaussian noise and on a set of real images.

Contrast Enhancement by Multiscale and Nonlinear Operators

Abstract: Image enhancement techniques have been widely used in elds such as radiology, where the subjective quality of images is important for human interpretation (diagnosis). Contrast is an important factor in any subjective evaluation of image quality. Many algorithms for accomplishing contrast enhancement have been developed and applied to problems in medical imaging. A comprehensive survey of existing methods can be found in [1]. Among them, histogram modi cation and edge enhancement techniques have been most commonly used along with traditional methods of image processing. Histogram modi cation techniques [2, 3] are attractive due to their simplicity and speed, and have achieved acceptable results for some applications. In general, a transformation function is derived from a desired histogram and the histogram of an input image. In general, the transformation function is almost always nonlinear. For continuous functions, a

Conservative image transformations with restoration and scale-space properties

Abstract: Many image processing applications require one to solve problems such as denoising with edge enhancement, preprocessing for segmentation, or the completion of interrupted lines. This may be accomplished by applying a suitable nonlinear anisotropic diffusion process to the image. Its diffusion tensor is adapted to the differential structure of the underlying image. Although being image enhancement tools, filters of this class are well-posed. The temporal evolution of the diffusion process creates a scale-space whose causality properties can be understood in a deterministic, stochastic, information theory based and Fourier based way. The well-posedness and scale-space results carry over to the discrete setting, which gives rise to reliable algorithms preserving all properties of the continuous framework.

Real-time optical image subtraction and edge enhancement using ferroelectric liquid-crystal devices based on speckle modulation

Abstract: We carried out real-time optical image subtraction and edge enhancement based on a speckle modulation technique by using ferroelectric liquid-crystal polarization switches and a ferroelectric liquid-crystal spatial light modulator. A ferroelectric liquid-crystal spatial light modulator is employed as a real-time and multiple-exposure optical device, and successful results are obtained from three-exposure images modulated by speckles. Thus, image subtraction and edge enhancement are realized in real time. The whole operation is performed within several milliseconds with modest operating conditions. Because the spatial light modulator has a high resolution of greater than 100 line pairs/mm and can store fine speckle patterns, the image qualities we obtained are quite satisfactory.

Method and apparatus for producing an enhanced two-grayscale image

Abstract: An image enhancement method and an image enhancement apparatus is provided, wherein a multiple-gray-level converter of a grayscale converter converts an original two-grayscale image, an image enhancement device enhances the resultant image so that gray values for the outline of the image are smoothly changed, and a two-gray-level converter converts the enhanced image to a two-grayscale image by referring to a specific value that is set in advance. The image enhancement method and the image enhancement apparatus of the present invention resolve the conventional problem of jaggies that appear in the outline of an enhanced image and make the image appear unattractive, and they thus produce a high quality enhanced two-grayscale image that has a smoother outline.

Nonlinear regularization using constrained edges in image reconstruction

Abstract: This paper deals with edge-preserving regularization for image reconstruction. We use a non-quadratic regularization term involving a /spl phi/-function applied on the intensity gradient modulus. During the process, small gradients are smoothed while high gradients are preserved. In order to take into account the noise more specifically, we propose to use the explicit version of the regularization term involving the edge variable. We add a nonlinear constraint on this edge variable, in order to remove the noise. It allows edge enhancement while smoothing the noise, even in the case where the edge and noise generate the same high gradient modulus. We have previously proposed a model composed of two coupled partial differential equations (PDE) on the image intensity and image edges. In this paper, we show that, for a particular regularization intensity function, the two coupled PDE can be slightly modified in order to correspond to the Euler equations associated with the minimization of a global criterion. This new criterion contains a nonlinear regularization term on both the intensity and the edges. We use convergence towards Mumford and Shah (1989) functional to improve our results.

Frequency Domain Image Analysis for Detecting Stress Cracks in Corn Kernels

Abstract: A corn kernel classification procedure was developed in the frequency domain using a two-dimensional Fourier Transform for inspection of stress cracks. Investigations were also conducted to define suitable conditions and optimum image resolution for viewing stress cracks in corn kernels using a computer vision system. A pre-processing procedure included contrast enhancement, edge enhancement, and kernel edge elimination to improve stress crack recognition. A Fast Fourier Transform algorithm was applied to the pre-processed images, and the transformation results were condensed into 33 feature signatures representing position or orientation invariant morphological features. A multi-variate discriminant analysis and multiple regression analysis were used to develop classification criteria for stress crack inspection. Both methods were able to detect stress cracks satisfactorily with an average success ratio above 96%.

Particle image velocimetry using wavelet-matched filters

Abstract: A novel method for improving measurement abilities of a particle image velocimetry by using wavelet-matched filtering is proposed. Based on an edge enhancement and multiscaling properties of a wavelet transform, an optical production of a sharp autocorrelation peak from a doubly exposed particle image is demonstrated.

Four-plane space-variant Fresnel-transform optical processor with a random phase encoder

Abstract: We introduce a four-plane Fresnel-transform space-variant optical processor consisting of an input plane and two filter planes. One filter mask is programmable with a spatial light modulator. The second filter mask is a fixed random binary phase array with a known pseudorandom distribution of pixels. The order of the masks can be interchanged, giving different output characteristics. In one case the Horner efficiency of the correlator increases dramatically. In the other case the edge enhancement of the output image is removed. We discuss the theory for this general processor and its implementation with phase-only masks. We present experimental results when a binary magneto-optic spatial light modulator was used.

A prototype system of three-dimensional non-contact measurement

Abstract: A prototype 3D measurement system is proposed in this paper which consists of a 1D laser displacement sensor, a 2D image system and a servo controlX,Y-table. The laser sensor and CCD camera are installed on theZ-axis perpendicular to theX,Y-table. The image-processing system employs the adaptive resonant theory (ART) neural networks to classify the outer shape of the measured object. The edge values of the object are then obtained by using the image processing procedures of sliding, stretching, edge enhancement and binary disposal. The 2D dimensions of the object are searched by employing the Hough theory based upon the edge values. The 3D dimensions of the object are measured and assembled by combining theX,Y-coordinates of the table and the scanning results of the 1D laser for the height of theZ-axis. A 3D plaster model is chosen as the specimen for non-contact measurement to verify the feasibility of this approach. The limitations and resolution of the 3D measurement of this system are discussed.

Selective sampling and edge enhancement in bar code laser scanning

Abstract: This paper describes the basic design principles for a new series of bar code scanners from Symbol Technologies Traditional bar code scanners include an edge detector which has several innate limitations We propose replacing this edge detector with a selective sampling circuit While the superiority of decoding the analog signal has been demonstrated, its implementation is too costly because of the need for considerable additional memory Selective sampling achieves most of the advantages of analog decoding at a cost comparable to that of conventional decoders Instead of sampling the signal periodically, it is only sampled when a certain event (eg an edge) is detected At each edge two data values are produced: the edge time and the sampled value, often referred to as the edge strength This strength value gives a measure of the intensity of the edge Using selective sampling the new scanners can read poorly printed and noisy bar codes that cannot be read by traditional scanners Another innate limitation of bar code laser scanners is the density of bar code that can be read This limitation is due to the blurring of the bar code when scanned by a laser beam with a finite spot size We propose the addition of an edge enhancement filter to the scanner, which compensates for the finite width of the optical beam The proposed filter is designed to enhance the edges of the bar code so that for a given optical focusing it is possible to read higher density bar codes© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering Downloading of the abstract is permitted for personal use only

Three-Dimensional Modeling and Effects on Still Images

Abstract: Designers and creative artists use computer grapics and image processing effects on still photographs in application areas such as advertising, entertainment, broadrastinq and the arts. Most of the effects available in research and commercial work are two-dimensional in nature, for example image processing filters (blur, edge enhancement) and creative effects (tilings, reflections). There is almost no usage of information taken from the 3-D world in which the objects appearing in the image are located. In this paper we present a novel method for creating 3-D effects on photographs, or in general on any image created by rendering a 3-D world. The artist interacts with the image using a set of intuitive direct manipulation interface objects. These objects let the user define a 3-D model, display it, and manipulate it in a 3-D space which is correlated with that of the input image. The generated model can be an arbitrarily complex 3-D polyhedron. Any texture, including texture taken from the input photograph, can be mapped into any of its faces and used for special effects. We discuss and show examples for effects such as copy and paste, motion blur, model editing and deformations, lighting effects, and shadows.