About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Conference paper
Efficient, chromaticity-preserving sharpening of RGB images
Abstract
The conventional method of performing spatial filtering of RGB images is to subject each plane to the same processing, usually convolution with a filter kernel. Filtering is commonly used in the processing of photographic or photo-realistic images to sharpen or blur images, and to produce esthetically-pleasing effects. For image sharpening, the technique of subjecting each plane to the same processing produces objectionable color errors in some circumstances, and that techniques which convert the image to a color space that separates luminance from chrominance and performing the filtering only on the luminance component can produce better results. The problem with this approach has been the computational cost of making the transformations, first to the luminance-chrominance space, and back to RGB. This paper presents an algorithm which operates on an RGB image and provides results which are free from chromaticity changes. It achieves these results with fewer computations than filtering the luminance component in a luminance-chrominance color space. In fact, the computations required are usually simpler than processing each RGB plane.
Related
Conference paper
Unassisted true analog neural network training chip
Conference paper