Edge enhancement

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

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.

Non-Linear Direct Multi-Scale Image Enhancement Based on the Luminance and Contrast Masking Characteristics of the Human Visual System

Abstract: Image enhancement is a crucial pre-processing step for various image processing applications and vision systems. Many enhancement algorithms have been proposed based on different sets of criteria. However, a direct multi-scale image enhancement algorithm capable of independently and/or simultaneously providing adequate contrast enhancement, tonal rendition, dynamic range compression, and accurate edge preservation in a controlled manner has yet to be produced. In this paper, a multi-scale image enhancement algorithm based on a new parametric contrast measure is presented. The parametric contrast measure incorporates not only the luminance masking characteristic, but also the contrast masking characteristic of the human visual system. The formulation of the contrast measure can be adapted for any multi-resolution decomposition scheme in order to yield new human visual system-inspired multi-scale transforms. In this article, it is exemplified using the Laplacian pyramid, discrete wavelet transform, stationary wavelet transform, and dual-tree complex wavelet transform. Consequently, the proposed enhancement procedure is developed. The advantages of the proposed method include: 1) the integration of both the luminance and contrast masking phenomena; 2) the extension of non-linear mapping schemes to human visual system inspired multi-scale contrast coefficients; 3) the extension of human visual system-based image enhancement approaches to the stationary and dual-tree complex wavelet transforms, and a direct means of; 4) adjusting overall brightness; and 5) achieving dynamic range compression for image enhancement within a direct multi-scale enhancement framework. Experimental results demonstrate the ability of the proposed algorithm to achieve simultaneous local and global enhancements.

Image compression with color interpolation for a single sensor image system

Abstract: In accordance with the present invention, the R, G, B color image signals from a single sensor having a color filter array are all transformed to Η-space by changing them to R?1/Η, G1/Η, B1/Η?, respectively, where Η is approximately 2.4. In this space, all operations such as color differencing, interpolation of those missing pixels required for color differencing, compression, decompression, edge enhancement and final interpolation of all missing pixels are performed without further transformation of the image signals. For the same final bit rate, noise in the reproduced image is reduced by refraining from interpolating the missing color pixels prior to compression of the image data. In order to avoid over-emphasizing features of the image which are already sufficiently sharp, the combined outputs of horizontal and vertical sharpening processes are subjected to a paring process of the invention which suppresses strong high-spatial frequency components as a function of their amplitude. In the compression-decompression process of the invention, each spatial frequency coefficient of the spatial frequency-transformed image is divided by a normalization factor determined by cascading in the spatial frequency domain the human visual system contrast sensitivity function, the edge enhancement modulation transfer function and the image display modulation transfer function and inversing the resulting matrix elements.

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.

Nonlinear operators in image restoration

Abstract: We present a variational approach such that during image restoration, edges detected in the original image are being preserved. We compare the mathematical foundation of this method with respect to some of the well known methods recently proposed in the literature within the class of PDE based algorithms (anisotropic diffusion, mean curvature motion, min/max flow technique). The performance of our approach is carefully examined and compared to the classical methods. Experimental results on synthetic and real images illustrate the capabilities of all the studied approaches.