Trend adjustment in color images using bidimensional empirical mode decomposition

Abstract

Bidimensional empirical mode decomposition (BEMD) decomposes an image into several bidimensional intrinsic mode components, which is useful for various image enhancement and/or feature extraction applications. However, because of the requirement of scattered data interpolation and associated difficulties, the classical BEMD methods appear unsuitable for many applications. Recently, a fast and adaptive BEMD (FABEMD) method is proposed, which alleviates some of the difficulties, otherwise encountered in classical BEMD approaches. On the other hand, existing BEMD methods are proposed for gray scale images only. This paper first presents a novel BEMD approach for color images known as color BEMD (CBEMD), which employs FABEMD principle and decomposes a color image into color bidimensional intrinsic mode components based on hierarchical local spatial variation of image intensity and color. In fact, FABEMD facilitates the extension of the BEMD process for color images in a convenient and useful way, whereas the other interpolation based BEMD techniques appear unsuitable for this purpose. In FABEMD, order statistics filters are employed to estimate the envelope surfaces from the data instead of surface interpolation, which enables fast decomposition and well characterized bidimensional intrinsic mode components. Second, the CBEMD is utilized in this paper for adjusting and/or modifying the trend of color images. In this process, the image is reconstructed by adding the color bidimensional intrinsic mode components after applying suitably selected weights. Test results with real images demonstrate the potential of the proposed CBEMD method for color image processing, which include color trend adjustment.