Edge enhancement

The joint transform correlator (JTC) is one of two main optical image processing architectures which provide us with a highly effective way of comparing images in a wide range of applications. Traditionally an optical correlator is used to compare an unknown input scene with a pre-captured reference image library, to detect if the reference occurs within the input. There is a new class of application for the JTC where they are used as image comparators, not having a known reference image, rather frames from a video sequence form both the input and reference. The JTC input plane is formed by combining the current frame with the previous frame in a video sequence and if the frames match, then there will be a correlation peak. If the objects move then the peaks will move (tracking) and if something has changed in the scene, then the correlation between the two frames is lost. This forms the basis of a very powerful application for the JTC in Defense and Security. Any change in the scene can be recorded and with the inherent shift invariance property of the correlator, any movement of the objects in the scene can also be detected. A major limitation of the JTC is its intolerance to rotation and scale changes in input compared to the reference images. The strength of the correlation signal decreases as the input object rotates or varies in scale relative to the reference object. We have designed binary phase only filters using the direct binary search algorithm for rotation invariant pattern recognition for a 1/f JTC. Simulation and experimental results are included. If the relative alignment of the images in the input plane is known then the desirable fringes in the resulting joint power spectrum (JPS) can be selectively enhanced during the binarisation process. This can have a highly beneficial effect on the resulting correlation intensities. For the input plane in which input and reference images are placed side by side we develop the vertical edge enhancement (VEE) technique that concentrate solely on the vertical components of the JPS during the binarisation process. Simulation and experiments proves that VEE enhances the correlation intensities and suppresses the zero order noise.

The purpose of this study was to evaluate the effects of system parameters (focal spot size, tube voltage, geometry, detector resolution, and image noise) and object characteristics (edge gradient/ shape, composition, thickness, and overlying attenuating material) upon the edge enhancement effect in phase-contrast radiography. Each variable of interest was adjusted and images of a 3 mm lucite phantom were obtained with the other variables remaining constant. A microfocus x-ray source coupled to a CCD camera with an intensifying screen was used to acquire the digital images. Two parameters of image analysis were used to quantify the effects. The edge enhancement index (EEI) was used to measure the absolute degree of edge enhancement, while the edge enhancement to noise ratio (EE/N) was used to measure the conspicuity of the edge enhancement relative to image noise. Little effect on EEI was seen from tube voltage, object thickness, overlying attenuating material, while focal spot size and system geometry demonstrated measurable effects upon the degree of edge enhancement. It was also shown that while the edge enhancement effect over straight edges is highly dependent upon how the edge aligns with the x-ray beam, rounded edges, which better model biological objects, do not suffer from this dependence and the EEI reaches its maximal level at any alignment. Decreasing detector resolution diminished the EEI slightly, but even with pixel sizes of 0.360 x 0.360 mm edge enhancement effects were readily visible. The effect of image noise on EE/N was evaluated using different exposure times showing an expected improvement with longer exposure time with EE/N approaching a plateau at 5 min. Many of the parameters that will go into the design of a future PC-R imaging system have been quantified in terms of their effect on the degree of edge enhancement in the acquired image. These results, taken together, indicate that either a specimen or even clinical breast imaging system could be created with currently available technology. The major limitation to a clinical system would be the low x-ray flux from the microfocal x-ray source.

The joint transform correlator (JTC) is one of the two main optical image processing architecture which provides a highly effective way of comparing images in a wide range of applications. Traditionally, an optical correlator is used to compare an unknown input scene with a pre-captured reference image library, to detect if the reference occurs within the input. Strength of the correlation signal decreases rapidly as the input object rotates or varies in scale relative to the reference object. The aim of this paper is to overcome the intolerance of the JTC to rotation and scale changes in the target image. Many JTC systems are constructed with the use of ferroelectric liquid crystal (FLC) spatial light modulators (SLMs) as they provide fast two-dimensional binary modulation of coherent light. Due to the binary nature of the FLC SLMs used in the JTC systems, any image addressed to the device need to have some form of thresholding. Carefully thresholding the grey scale input plane and the joint power spectrum (JPS) has significant effect on the quality of correlation peaks and zero order (DC) noise. A new thresholding technique to binarise the JPS has been developed and implemented optically. This algorithm selectively enhances the desirable fringes in the JPS which provide correlation peaks of higher intensity. Zero order noise is further reduced when compared to existing thresholding techniques.Keeping in mind the architecture of the JTC and limitations of FLC SLMs, a new technique to design rotation and scale invariant binary phase only filters for the JTC architecture is presented. Filers design with this technique have limited dynamic range, higher discriminability among target and non-target objects, and convenience for implementation on FLC SLMs. Simulation and experiments shows excellent results of various rotation and scale invariant filters designed with this technique. A rotation invariant filter is needed for various machine vision applications of the JTC. By fixing the distance between camera and input object, the scale sensitivity of the correlator can be avoided. In contrast to the industrial machine vision applications, scale factor is very important factor for the applications of the JTC systems in defence and security. A security system using a scale invariant JTC will be able to detect a target object well in advance and will provide more time to take a decision.

In order to illustrate the self-assembly capability, we consider a laser imaging experiment on a wet film that is made of bacteriorhodopsin (BR) molecules suspended in a diffusion-limited viscous medium. BR wet film is similar to a wet photograph film but having a finer resolution and adaptive pixel locations due to laser-induced thermal diffusion. The synergism between thermal diffusion of BR molecules (induced externally by a write-laser) and molecular photochromism (generated internally by a read-laser) is exploited naturally for edge-enhanced image applications.

ABSTRACT It is well known that the Gabor wavelet transform (GWT) provides directional information for the analysis of an image. In this paper, we proposed an approach based on the GWT by combining unsupervised Fuzzy c-means (FCM) clustering which provides plays an important role in recognition as a classifier. After enhancing the edge of the input image using GWT, the binary image showing the edge is obtained using FCM clustering and morphological skeletonization. When compared to the Canny method and other conventional method, the proposed method has showed a better performance in terms of detection accuracy for noisy medical images.

ABSTRACT It is well known that the Gabor wavelet transform (GWT) provides directional information for the analysis of an image. In this paper, we proposed an approach based on the GWT by combining unsupervised Fuzzy c-means (FCM) clustering which provides plays an important role in recognition as a classifier. After enhancing the edge of the input image using GWT, the binary image showing the edge is obtained using FCM clustering and morphological skeletonization. When compared to the Canny method and other conventional method, the proposed method has showed a better performance in terms of detection accuracy for noisy medical images.

The nonlinearity of phase-conjugate beam reflection with four-wave mixing in BSO crystal by the moving grating at large fringe modulation formed by the incident-beams ratio is investigated. The experimental results have shown that the optimum fringe velocity is the incident-beam ratio and the total-light-intensity dependent, the reflectivity at the optimum fringe velocity is the incident-beam ratio and the fringe-spacing dependent. Based on these nonlinear properties, the edge-enhancement of an object is achieved.

This paper describes an extension of the S-CIELAB image comparison model and its use in the Virtual Image Chain for inkjet and medical display (project VICTOR: granted by European commission). The extension starts from the representation of the perceived image in Fourier space with CSF-filters in AC1C2. For the achromatic channel bandpass-characteristics (edge enhancement) and anisotropy (Daly's work) are introduced, presenting an alternative normalization of the bandpass filters (DC component = 1). To obtain the best representation of the perceived image, the edge enhancement should be measured and modeled. After filtering with the extended model (VICTOR S- CIELAB) similarities between images are quantified by higher order image quality metrics. Please include a brief abstract of this paper. Avoid using figures or equations in the abstract.

A new approach for efficient and robust super-resolution is presented in this paper. The essence of the proposed method is in selectively applying a Bayesian MAP estimator to visually important image regions. Regions with high spatial activity are considered as visually important. The degree of image spatial activity is measured by calculating the gradient of the obtained estimate for the high-resolution image at each iteration. The results obtained through simulation and with real video exhibit a computational complexity reduction of up to 50%, along with improved visual quality and higher SNR gains for magnification factors equal to or higher than 4.

The purpose of this study was to demonstrate that a commercially available cabinet x-ray system is capable of phase-contrast radiography (PC-R) and to evaluate the effect of different system parameters on the degree of edge enhancement. An acrylic plastic edge phantom was imaged at different tube potentials (25-60 kV) and in different geometries (variable object-to-detector distances, R(2), at a constant source-to-detector distance, R(1) + R(2)). In addition, the effect of noise on the perceived edge enhancement was studied as a function of exposure time. Our results show that a modest degree of phase contrast can be achieved in an unmodified cabinet x-ray system. In addition, the particular system evaluated allowed low-noise PC-R images to be obtained with short (6 s or less) exposures. These results suggest that with appropriate geometric choices PC-R is already available to a wide range of research scientists for use in both small-animal and human-specimen experiments.

Color printers usually have only four inks (Cyan, Magenta, Yellow, and Black). They can not represent all colors of a color image. Traditional error-diffusion algorithm is proposed for solving this problem. In general, error-diffusion produces better halftoned images. Unfortunately, it also produces some artifacts, i.e. unstable phenomenon and smeared edges. These situations are much worse when dot overlap is included.

Summary Embedded real-time image processing is widely used in many applications, including industrial inspection, robot vision, photo-copying, traffic control, automotive control, surveillance, security systems and medical imaging. In these applications, the size of the image can be very large, the processing time should often be small and real-time constraints should be met. Starting in the 1980's, many parallel hardware architectures for low-level image processing have been developed. They range from frame-grabbers with attached Digital Signal Processors (DSPs), to systolic pipelines, square and linear single-instruction multiple-data stream (SIMD) systems, SIMD pyramids, PC-clusters, and since a number of years, smart cameras. As processors are becoming faster, smaller, cheaper, and more efficient, new opportunities arise to integrate them into a wide range of CMOS processor devices. Since there are so many different applications, there is no single processor that meets all t...

Edge-detection algorithms are applied to SAR images in order to generate different edge strength maps. The results obtained can be combined to improve the final output in order to get a more defined edge strength map. In this paper different combinations of methods are implemented and evaluated using a criterion based on the quality of the detected edges over the

BACKGROUND: Two-dimensional echocardiography (2D-echo) allows the evaluation of cardiac structures and their movements. A wide range of clinical diagnoses are based on the performance of the left ventricle. The evaluation of myocardial function is typically performed by manual segmentation of the ventricular cavity in a series of dynamic images. This process is laborious and operator dependent. The automatic segmentation of

A new approach for efficient and robust super-resolution is presented in this paper. The essence of the proposed method is in selectively applying a Bayesian MAP estimator to visually important image regions. Regions with high spatial activity are considered as visually important. The degree of image spatial activity is measured by calculating the gradient of the obtained estimate for the

A numerical model of a nearest-neighbor interconnected cellular neural network is described. The cells consist of optically driven autonulling circuits that generate analog output signals. For the numerical analysis we demonstrate that the 2D array provides lateral inhibition properties. Control of the image enhancement is obtained through the the weights associated with a 2D convolution operator