Showing papers on "Edge enhancement published in 2000"

Image enhancement via adaptive unsharp masking

Abstract: This paper presents a new method for unsharp masking for contrast enhancement of images. The approach employs an adaptive filter that controls the contribution of the sharpening path in such a way that contrast enhancement occurs in high detail areas and little or no image sharpening occurs in smooth areas.

Image processing with the radial Hilbert transform: theory and experiments

Abstract: The Hilbert transform is useful for image processing because it can select which edges of an input image are enhanced and to what degree the edge enhancement occurs. However, the transform operation is one dimensional and is not applicable for arbitrarily shaped two-dimensional objects. We introduce a radially symmetric Hilbert transform that permits two-dimensional edge enhancement. We implement one-dimensional, two-dimensional, and radial Hilbert transforms with a programmable phase-only liquid-crystal spatial light modulator. Experimental results are presented.

A new fuzzy logic filter for image enhancement

Abstract: This paper presents a new fuzzy-logic-control based filter with the ability to remove impulsive noise and smooth Gaussian noise, while, simultaneously, preserving edges and image details efficiently. To achieve these three image enhancement goals, we first develop filters that have excellent edge-preserving capability but do not perform well in smoothing Gaussian noise. Next, we modify the filters so that they perform all three image enhancement tasks. These filters are based on the idea that individual pixels should not be uniformly fired by each of the fuzzy rules. To demonstrate the capability of our filtering approach, it was tested on several different image enhancement problems. These experimental results demonstrate the speed, filtering quality, and image sharpening ability of the new filter.

A New Method of Analyzing Edge Effect in Phase Contrast Imaging with Incoherent X-rays

Abstract: A method has been devised to calculate the half-width of the phase contrast enhanced edge of an X-ray- image with incoherent X-rays. This method is based on changes in the wave fronts of X-rays after penetrating an object, and it takes into account the blur due to penumbra observed in practical radiographic imaging. The method closely predicted edge enhancement when images of a plastic fiber were produced with practical X-ray tubes (Cu, Mo, and W anodes with focus-diameters of 10,100, and 40 μ, respectively). Thus, the method shows promise in facilitating phase contrast X-ray imaging in both medical radiography and non-destructive inspection using incoherent X-rays from X-ray tubes with substantial focus-sizes.

Image processing device and recording medium

Abstract: An image processing apparatus and a recording medium which can improve the quality of a color document image are provided. An input image is converted into an image having low resolution. A subtractive color image is generated using the converted image having low resolution. From the subtractive color image, adjacent pixels which are allocated to the same representative color are unified so as to extract an FCR (Flat Color Region) candidate region. A region candidate is redetermined using an image having resolution which is higher than the subtractive color image. Next, a boundary of the FCR is detected and an ultimate FCR is determined. Selection of a representative color of the determined FCR is carried out, and a specific color processing which replaces a color which is close to a pure color with the pure color is effected. Finally, image regeneration is carried out by overwriting and drawing (synthesizing) the FCR on the input image.

Real time binarization of gray images

Abstract: A binarization method for gray address images which combines high quality and high speed. The method is designed specifically for efficient software implementation. Two binarization approaches, localized background thresholds and Laplacian edge enhancement, are combined into a process to enhance the strengths of the two methods and eliminate their weaknesses. The image is divided into tiles, making binarization decisions for each tile. Tile decisions are modified based on adjacent tile decisions and then the tiles are binarized. Binarization of pixels is performed by performing background thresholding and edge detection thresholding. Only pixels exceeding both thresholds are selected as “on”.

A modified fuzzy Sobel edge detector

Abstract: A modified fuzzy Sobel method for edge detection and enhancement is proposed. This method is a modification of the fuzzy Sobel method proposed by Kuo, Lee and Liu see (IEEE Conference on Fuzzy Systems, p.1069-74, 1997). The proposed method overcomes the drawbacks of the conventional gradient methods for edge detection such as Prewitt and Sobel methods. It automatically obtains four threshold values, and apply fuzzy reasoning for edge enhancement. The edges extracted by this method are very clear and provides better representation for image edges and object contours.

Edge enhancement preprocessing with image region determining function

Abstract: An image processing apparatus includes an edge processing section, an region data producing section, and a region determining section. The edge processing section enhances an edge portion of a first region of an image in units of picture elements to produce an enhanced image. The image includes the first region and a second region which are mixed, and picture elements of the image are expressed to as RGB data. The region data producing section divides the enhanced image into regions to output region data indicative of each of the regions, variance data of each of the regions and contour edge data indicative of a contour of each of the regions. The region determining section determines the first region in the enhanced image based on the region data, the variance data and the contour edge data.

Digital camera with video input

Abstract: A digital camera with video input is disclosed. An image sensor is provided to capture an image and convert the captured image into R/G/B image signals the are processed by a video signal processing unit for being converted into pixel data having a first format. A TV decoder converts input analog video signals into pixel data having a second format. A scaling unit performs aspect ratio processing and zoom/pan function to pixel data. A TV interface unit converts pixel data having the second format into pixel data having the first format for being selectively output to the scaling unit directly and output to the scaling unit after transferring to the video signal processing unit for performing edge enhancement. A compressing and storage unit performs compress operation to the pixel data output from the video signal processing unit and the scaling unit. An USB unit converts pixel data after being compressed into serial data for output.

Edge enhancement of gray level images

Abstract: A method and apparatus for processing gray level image data. Gray level image data is subjected to plural separate halftone screen processing to form plural separate halftone processed screen gray level image data. The current pixel is also analyzed for contrast index. In response to analysis of the contrast index blending coefficients for processing of the halftone screen modified image data is made. The respective halftone outputs of the screen processings are multiplied by the respective blending coefficient. The resulting blended halftone gray value of the current pixel as well as neighboring pixels are then subjected to a threshold criterion test to determine if this represents a substantially binary image file such as might occur in a saturated text image file. The blended halftone gray value data for the current pixel is subjected to gray level edge enhancement processing to replace certain binary pixels adjacent an edge to reduce anti-aliasing effects. A signal resulting from the threshold criterion test is used to determine whether there is output to the printer or display of an edge enhanced version of the current blended halftone pixel or a pixel value representing the blended halftone pixel.

Edge enhancement and edge-enhanced correlation with photorefractive polymers.

Abstract: We demonstrate a simple all-optical realization of programmable edge enhancement and edge-enhanced correlation using novel photorefractive polymers. We show that the higher non-Bragg order in a two-beam coupling scheme contains the edge enhancement of the object when placed in the path of one of the incident beams. Also, this arrangement provides a scheme for writing joint transform correlation dynamic holograms, which can be read by a third beam. The correlation is edge enhanced, and the correlation peak increases with the applied bias voltage. Numerical results without and with beam fanning are presented. Theoretical predictions are reconciled with experimental results.

Edge enhancement processor and method with adjustable threshold setting

Abstract: An edge enhancement processing system and method for modifying image data at certain pixel locations to include gray scale image data so as to reduce jaggedness in the image. An adjustable threshold device establishes a binary pixel value for an incoming current gray level pixel in accordance with a thresholding criterion. An operator accessible input to the thresholding device is used by the operator to adjust the threshold value in the thresholding criterion. A current binary pixel formed in accordance with the thresholding criterion and neighboring pixels also so formed in accordance with the thresholding criterion are examined in accordance with predetermined criteria for determining adjustment of the current pixel to a gray scale value to reduce edge jaggedness of the image. Generally, adjustment of the thresholding value is made in instances where under color removal and/or gray component replacement is employed.

Special Purpose Image Convolution with Evolvable Hardware

Abstract: In this paper, we investigate a unique method of inventing linear edge enhancement operators using evolution and reconfigurable hardware. We show that the technique is motivated by the desire for a totally automated object recognition system. We show that an important step in automating object recognition is to provide flexible means to smooth images, making features more obvious and reducing interference. Next we demonstrate a technique for building an edge enhancement operator using evolutionary methods, implementing and testing each generation using the Xilinx 6200 family FPGA. Finally, we present the results and conclude by mentioning some areas of further investigation.

Image enhancement filter with adaptive threshold

Abstract: A method of adaptive spatial filtering for enhancing digital images is presented. A window is applied to a source pixel to generate an array of windowed pixels. An upper threshold value and lower threshold value corresponding to the maximum and minimum pixel values, respectively, in the array of windowed pixels are determined. A spatial frequency filter is applied to the array of windowed pixels to generate a filtered array of pixels including a reference pixel. The values of the source pixel and the reference pixel are scaled by respective weighting factors and then combined to create an enhanced pixel value. If the enhanced pixel value exceeds the upper threshold value, the enhanced pixel value is replaced by the upper threshold value. Similarly, if the enhanced pixel value is less than the lower threshold value, the enhanced pixel value is replaced by the lower threshold value. The resulting array of enhanced values provides greater edge transitions yet does not result in overshoot and undershoot regions which are generated by many traditional edge enhancement methods. In addition, color shifts associated with independent processing of individual color planes are avoided.

Directional-rational approach for color image enhancement

Abstract: In this paper, we present an unsharp masking-based approach for noise smoothing and edge enhancing in multichannel images. The proposed structure is similar to the conventional unsharp masking structure, however, the enhancement is allowed only in the direction of maximal change and the enhancement parameter is computed as a nonlinear function of the rate of change. The proposed scheme enhances the true details, limits the overshoot near sharp edges and attenuates noise in flat areas. Moreover the use of the control function eliminates the need for the subjective coefficient /spl lambda/ used in the conventional unsharp masking technique. Simulations results show that the processed image presents sharp edges which makes it more pleasant to the human eye. Moreover, the amount of noise in the image is clearly reduced.

Image processing method

Abstract: PROBLEM TO BE SOLVED: To enable smoothing selectively changing edge enhancement or strength while considering all band components contained in edges by performing band division processing and performing band synthesis processing after reloading band components while referring to the band components. SOLUTION: In a band dividing step 101, an image to be the object of processing is inputted and such images are divided into plural bands. In a band component change step 102, while referring to one or plural band components, one or plural band components are reloaded. In an image synthesizing step 103, the divided and changed band components are synthesized, made into image and outputted. In this case, the band component of a visually important edge is widely distributed from low frequency component to high frequency component but the band component of visually non-important edge or noise is eccentric in the high frequency component of its strength. Then, in the band dividing step 101, band division is performed while using wavelet transformation.

Improving image segmentation using edge information

Abstract: We report methods for image segmentation that combine region growing and edge detection. Existing schemes that use region-based processing provide unambiguous segmentation, but they often divide regions that are not clearly separated, while merging regions across a break in an otherwise strong edge. Edge-based schemes are subject to noise and global variation in the picture (e.g. illumination), but do reliably identify strong boundaries. Our combined algorithm begins by using region growing to produce an over-segmented image. This phase is fast (order N, where N is the number of pels in the image). We then modify the over-segmented output of the region growing using edge criteria such as edge strength, edge smoothness, edge straightness and edge continuity. Two techniques-line-segment subtraction and line-segment addition-have been investigated. In the subtraction technique, the weakest edge (based on a weighted combination of the criteria) is removed at each step. In addition technique, the strongest edge is used to seed a multi-segment line that grows out from it at both ends. At every junction, the adjoining edge that has the highest edge strength is appended. We have also investigated a form of look-ahead, where the growing of lines depends on the strength of the adjoining edge and those to which it is linked. The overall procedure for both techniques, current results and the areas for improvement and expansion have been discussed.

Circuit and method for image processing

Abstract: PROBLEM TO BE SOLVED: To compress a dynamic range with high compressibility by applying a circuit and a method for image processing to, for example, a video camera, an electronic still camera, etc., and effectively avoiding a decrease in contrast feeling and unnatural edge enhancement. SOLUTION: An input image X is smoothed while holding its edge to find a gain correction coefficient G, with which pixel values x(i,j) of the input image X are corrected.

Video caption image enhancement for an efficient character recognition

Abstract: For improved recognition of videotexts, we have focused on image enhancement techniques. Since the videotexts are low-resolution and mixed with complex backgrounds, image enhancement is a key to successful recognition of the videotexts. Especially in Hangul characters, several consonants cannot be distinguished without sophisticated image enhancement techniques. In the paper, after multiple videotext frames containing the same captions are detected and the caption area in each frame is extracted, five different image enhancement techniques are serially applied to the image: multi-frame integration, resolution enhancement, contrast enhancement, advanced binarization, and morphological smoothing operations. We have tested the proposed techniques with the video caption images containing both Hangul and English characters from various video sources such as cinema, news, sports, etc. The character recognition results are greatly improved by using enhanced images in the experiment.

Method and apparatus to provide edge enhancements as part of a demosaicing process

Abstract: A method and apparatus provide edge enhancements as part of a demosaicing process in which an image is captured. By providing edge enhancements as part of the demosaicing process, the edge enhancement process has access to unaltered spatial and chromatic information contained in the raw captured image data.

Edge enhancement method by 4-directional 1-dimensional high pass filtering

Abstract: An edge enhancement method by 4-directional 1-dimensional high pass filtering characterized in that 4 adjacent pixels positioned at a pixel point at which the primary pixel has euclidean distance of 2 in direction of right, left, up, and down, another 4 adjacent pixels positioned at a pixel point at which the primary pixel has euclidean distance of 22 in 45° and 135° direction, and 9 pixels including the primary pixel are masked by a window mask, and if the maximum difference of the surround pixels which are masked is greater than a predetermined reference difference, each edge enhancement value is calculated by performing 1-dimensional high pass filtering in column, row, 45°, and 135° direction centered from the primary pixel using the pixel values which are masked to the window mask. If the primary pixel is greater than the average value of the pixels which are masked to the window mask, the primary pixel is substituted by the maximum edge enhancement value among the above-calculated each edge enhancement value. If the primary pixel is less than the average value of the pixels, the primary pixel is substituted by the minimum edge enhancement value.

Edge enhancement device, edge enhancement method and recording medium

Abstract: PROBLEM TO BE SOLVED: To provide an edge enhancement device that can adaptively correct various types of edges from steep edges to rather smooth edges. SOLUTION: In the extraction of an edge part and correction of the pixel value of the edge part in its increasing direction to enhance the edge, an image correction processing section 57 generates a variable amplification factor (ΔG) changed corresponding to a pixel value (EBASIC) of the edge part and applies the amplification factor to the pixel value of the edge part to generate an edge enhancement correction value (E). The amplification factor (ΔG) has a characteristic of increasing the edge enhancement correction value (E) when the pixel value of the edge part resides in a large value area (an area where the EBASIC is large) more than when the pixel value of the edge part resides in a small value area (an area where the EBASIC is small).

Digital still camera

Abstract: PROBLEM TO BE SOLVED: To provide a digital still camera with which multiple exposure photographing can be performed by changing the image quality of each picture by a simple method without requiring addition and extension of a special circuit for multiple exposure. SOLUTION: Emphasis optionally applied to the trace of a moving object in such a manner that a controlling means 103 uses an operation that optionally varies the occurrence timing of a charge pulse 113 from a driving means 102 and an operation that optionally varies the occurrence timing of a subpulse 112 at the same time, and a signal processing means 108 can perform edge enhancement corresponding to multiple exposure in regard to an imaged picture, and thus image quality is improved and the trace is represented in a more easily understandable way.

Color image interpolation with edge-enhancement

Abstract: We propose the use of an edge-retaining asymptotic projections onto convex sets (ERAPOCS) algorithm for the interpolation of color images. Our algorithm differs from POCS in that the projections sequence in one (or more) of the convex sets can enter a desired region rapidly. Like POCS, our algorithm is guaranteed to converge. In addition, our algorithm is more computationally efficient than the POCS algorithm since it converges more quickly and has computational complexity for each iteration. The algorithm alleviates edge blurring by properly amplifying the wavelet transform (WT) of the image. The amplification in the WT domain biases the projection sequence to the subset of interpolated images that has less edge-blurring. In this way, we can obtain an interpolated image that retains a significant amount of edge information. We use our algorithm to interpolate a color image in the red, green and blue planes.

Automated analysis of electrophoretic gels by image digitalization and processing

Abstract: In this paper a system and method of automated analysis of electrophoresis gels by image digitalization and processing are reported. The image digitalization system is formed by a non-intensified colour charge-coupled device camera interfaced to a microcomputer through a frame grabber that allows image acquisition to be performed in real time. The illumination and image acquisition conditions will be discussed. A few different macro routines for image processing and data analysis were built. They were designed to cope specifically with the different requirements of the analysis of the different types of electrophoresis gel employed. After selection of the appropriate routine the automated evaluation process begins with digitalization and processing of the images in order to enhance the contrast of the images and to detect relevant features. The image-processing techniques employed include histogram equalization and contrast enhancement, noise reduction by low-pass Fourier filtering, edge enhancement and location by the application of differential operators, posterization or binarization, and blob analysis, matching and feature extraction. The obtained images are also compressed and stored on the patient's files for future reference.

X-ray transmission inspecting method and device therefor

Abstract: PROBLEM TO BE SOLVED: To realize an precise discovery and identification of defects, etc., by differential calculation of each image data of an edge enhanced image and transmitting image and providing a distinguishing image data for an inspection and a separation. SOLUTION: A substance 5 to be examined is set, and an X-ray 4 is irradiated and exposed thereto. A transmitted X-ray data obtained by the exposure is subjected to a subject imaging and the image is photographed by a CCD camera. The image photographed is A/D converted to be captured as an image data A into a personal computer body 9 RAM. Then, after the image data captured is subjected to a filtering processing by a median filter as a previous processing provided, if necessary to the image data, the image data A is subjected to an image processing of an edge enhancement such as Robert and Sobel, to obtain an image and an image data B. The image data B is taken in RAM and a processed image data A, and the image data B obtained by the edge enhanced processing are subjected to a differential operation processing to obtain an image data C. The image data C is taken in RAM, and an intermediate image due to the image data C is outputted and indicated on an indicator 10 as a distinguishing image for an inspection and an identification.

Edge-localized image sharpening via reassignment with application to computed tomography

Abstract: Traditional filtering methods operate on the entire signal or image. In some applications, however, errors are concentrated in specific regions or features. A prime example is images generated using computed tomography. Practical implementations limit the amount of high frequency content in the reconstructed image, and consequently, edges are blurred. We introduce a new post-reconstruction edge enhancement algorithm, based on the reassignment principle and wavelets, that localizes its sharpening exclusively to edge features. Our method enhances edges without disturbing the low frequency textural details.