Edge enhancement

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

Image processing circuit and method for processing image

Abstract: The present invention relates to an image processing circuit and an image processing method, and is applied to, for example, a video camera, an electronic still camera and the like, for compressing the dynamic range at a high compression rate with evading the lowering of an impression concerning the contrast and the unnatural edge emphasis. The present invention smoothes an input image X while preserving the edge to obtain a gain correction coefficient, and corrects the pixel value x(i, j) of the input image X with the gain correction coefficient.

A rank ordered filter for medical image edge enhancement and detection using intuitionistic fuzzy set

Abstract: This paper gives a novel scheme using intuitionistic fuzzy set theory to enhance the edges of medical images. Medical images contain lots of uncertainties, as they are poorly illuminated and fuzzy/vague in nature. So, direct segmentation techniques will not produce better results. There are lots of researches on edge enhancement starting from non-fuzzy to fuzzy set, but proper enhancement (highlighting important structures) is not obtained. Enhancement of edges helps in recovering the important structures that are not visible properly. Even minute pathological blood vessels/cells are not visible properly and in that case edge enhancement will enhance these blood vessels/cells. Intuitionistic fuzzy set theory is found suitable in medical image processing as it considers more (two) uncertainties as compared to fuzzy set theory. In the processing phase, image is initially converted to intuitionistic fuzzy image and intuitionistic fuzzy entropy is used to obtain the optimum value of the parameter in the membership and non-membership functions. Then it computes the total variation of the pixels with respect to the median value of the image window (rank order filtering). This enhances the borders or the edges of the image. The resulting image is then segmented (edge detected) using standard Canny's edge detector, when simply using Canny's edge detector does not give better result. From the result it is observed that on comparing with non-fuzzy and fuzzy methods, the proposed method gives better information about the images, which is helpful to the pathologists in accurate diagnosing of diseases.

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

Transductive object cutout

Abstract: In this paper, we address the issue of transducing the object cutout model from an example image to novel image instances. We observe that although object and background are very likely to contain similar colors in natural images, it is much less probable that they share similar color configurations. Motivated by this observation, we propose a local color pattern model to characterize the color configuration in a robust way. Additionally, we propose an edge profile model to modulate the contrast of the image, which enhances edges along object boundaries and attenuates edges inside object or background. The local color pattern model and edge model are integrated in a graph-cut framework. Higher accuracy and improved robustness of the proposed method are demonstrated through experimental comparison with state-of-the-art algorithms.