Showing papers by "Til Aach published in 1996"

Anisotropic spectral magnitude estimation filters for noise reduction and image enhancement

Abstract: Describes an algorithm for noise reduction and enhancement of images which is able to take into account anisotropies of signal as well as of noise. Processing is based on subjecting each image to a block DFT, followed by comparing each observed magnitude coefficient to the expected noise standard deviation for it. Depending on this comparison, each coefficient is attenuated the more, the more likely it is that it contains only noise. In addition, the attenuation is made dependent on whether or not the observed coefficient contributes to an oriented prominent structure within the processed image block. Orientation as well as the distinctness with which it occurs are detected in the spectral domain by an inertia-like matrix. Orientation information is additionally exploited to selectively enhance oriented structures, thus only marginally increasing noise as compared to isotropic enhancement.

Noise reduction in an image

Abstract: In a method of processing an image, the image is divided in one or more blocks. Separate blocks are spatially frequency transformed in that pixel-values of said blocks are transformed into spectral coefficients. A noise level of the image is estimated and reduced spectral coefficients are derived from spectral coefficients and the estimated noise level. Pixel-values for a processed block are synthesized from the reduced spectral coefficients and the processed blocks are assembled into a processed image. The noise level is estimated from the image information within the image. Preferably, a few parameters relating to the circumstances under which the image was acquired are also taken into account for estimating the noise level.

Image processing for noise reduction

Abstract: An image processing method includes multi-resolution decomposition to decompose an input image into frequency-band images, which are subsequently filtered according to an order statistics filtering. Preferably, a finite impulse response median hybrid is employed. The filtered frequency-band images are synthesized to form the filtered output image.

Directional adaptive noise reduction

Abstract: An image is processed taking into account the direction of a predominant structure of the image. Said predominant direction is derived from image information in the image. In particular, the covariance matrix having matrix elements depending on products of differences between pixel-values in separate directions is calculated. The eigenvectors of the covariance matrix correspond with the predominant direction of the image structure and the eigenvalues of the covariance matrix represent the strength of the structure in the image. The covariance matrix is computed locally, i.e. for separate regions in the images so as to take variations of the direction of predominant structures into account.

Noise Reduction and Image Enhancement Algorithms for Low-Dose X-Ray Fluoroscopy.

Abstract: After describing our quantum noise model, we rst develop a new motion-adaptive temporal lter based on a recursive structure. Compared to other motion-adaptive lters, our lter is much less prone to generate artefacts known as noise tails. We then describe spatial ltering techniques derived from the concept of spectral amplitude estimation. These lters work in the spectral domain, and can conveniently be tailored to the statistical properties of quantum noise. The noise reduction performance of these lters is evaluated