Edge enhancement

About: Edge enhancement is a research topic. Over the lifetime, 2324 publications have been published within this topic receiving 30962 citations.

Edge enhancement system for ultrasound images

Abstract: A method for enhancing an ultrasound image The image is assumed to be in the form of an ordered array of pixels Each pixel corresponding to the intensity of the echo generated by a corresponding voxel in an ultrasound energy field Axial and lateral directions relative to the ultrasound beam used to generate the sound field are defined within the ordered array The method of the present invention includes the steps of dividing the image into a plurality of processing blocks, picking blocks that satisfy certain statistical constraints for edge enhancement, enhancing the pixels in the selected blocks, and then displaying the pixels in each block, whether enhanced or not, that are above a display threshold for the block in question The statistical constraints are determined by second order statistics with reference to the axial and later directions In one embodiment of the present invention, sum and difference histograms are used to approximate the second order statistics, thereby reducing the computational load

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.

Edge-Directed Error Diffusion Halftoning

Abstract: In this letter, we propose two simple extensions of the existing error diffusion (ED) halftoning technique: one stops the diffusion at edge pixels, and the other tunes the support of the diffusion kernel to match the local edge orientation. Experimental results are used to show that the proposed edge-directed ED schemes achieve noticeably better performance over existing ED with edge enhancement for a certain class of gray-scale images

Preprocessing of dot-matrix/ink-jet printed text for Optical Character Recognition

Abstract: Method and apparatus are disclosed for processing image data of dot-matrix/ink-jet printed text to perform Optical Character Recognition (OCR) of such image data. In the method and apparatus, the image data is viewed for detecting if dot-matrix/ink-jet printed text is present. Any detected dot-matrix/ink-jet produced text is then pre-processed by determining the image characteristic thereof by forming a histogram of pixel density values in the image data. A 2-D spatial averaging operation as a second pre-processing step smooths the dots of the characters into strokes and reduces the dynamic range of the image data. The resultant spatially averaged image data is then contrast stretched in a third pre-processing step to darken dark regions of the image data and lighten light regions of the image data. Edge enhancement is then applied to the contrast stretched image data in a fourth pre-processing step to bring out higher frequency line details. The edge enhanced image data is then binarized and applied to a dot-matrix/ink jet neural network classifier for recognizing characters in the binarized image data from a predetermined set of symbols prior to OCR.

Edge enhancement and smoothing in digital images

Abstract: Edge definition in FLIR images is improved by assigning to each pixel (X) the average intensity value of that 3×3 pixel sub-matrix (30a-30i) containing the pixel (X) which exhibits the smallest edge value. Apparatus for obtaining that intensity value in real time is disclosed. This process can be iteratively repeated on the same image, with each iteration creating more enhanced edges and greater area smoothing between edges.