Spatial domain sharpening of color image employing bidimensional empirical mode decomposition

Abstract

Image sharpening is an image processing technique that highlights transitions in intensity and/or enhances the darker regions. This paper formulates a bidimensional empirical mode decomposition (BEMD) based spatial domain color image sharpening. In this approach, color image is first decomposed into several hierarchical components using BEMD, which is a multi-scale/multi-resolution technique. The hierarchical color image components are known as color bidimensional empirical mode functions (CBEMFs), where the first CBEMF contains the highest/finest local spatial variations, and the final CBEMF contains the color trend of an image. The final CBEMF is also known as color bidimensional residue (CBR), whereas the other CBEMFs are known as color bidimensional intrinsic mode functions (CBIMFs). However, instead of using classical BEMD, a modified BEMD, known as fast and adaptive BEMD (FABEMD) is utilized, which uses order-statics filters for envelope estimation in the process instead of surface interpolation. The BEMD developed for color images employing FABEMD is known as color BEMD (CBEMD). Since the first CBEMF contains the finest spatial variations in the image and the CBR contains the color trend information, manipulation of these two elements can provide useful sharpening of a color image. In one simple approach, suitable weighting of the first CBEMF and CBR is accomplished, where weighting is done to all three color components of these two elements. Finally, the image is reconstructed from the addition of all the CBEMFs to obtain the primary sharpening. An additional level of sharpening is achieved when the primarily sharpened image, as mentioned above, is added to the original image. By varying the weights, desired color image sharpening can be achieved, which is inherently data driven.