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Novel optimization-based bidimensional empirical mode decomposition.
- Source :
-
Digital Signal Processing . Mar2023, Vol. 133, pN.PAG-N.PAG. 1p. - Publication Year :
- 2023
-
Abstract
- Despite its rapid advancement in the past two decades, bidimensional empirical mode decomposition (BEMD) still has several limitations in multi-scale feature description of input images. To ameliorate this issue, in this paper we present several optimization-based approaches to BEMD. First, we articulate an improved unconstrained optimization approach to BEMD (IUOA-BEMD). The essential idea is to formulate an optimization model to decompose an input image based on the Delaunay triangulation of its local maxima (minima). Second, we design a scale-guided optimization approach to BEMD (SGO-BEMD) so as to arrive at an improved modal image. SGO-BEMD uses the initial modal image (obtained from the aforementioned proposed IUOA-BEMD) as a necessary guide and can capture much clearer features at various spatial scales of the input image. In addition, an additional edge-preserving property can be obtained with the edge-aware decomposition if an edge-aware scale-guided optimization to BEMD (EASGO-BEMD) is used. The visualization and quantitative results for many artificial amplitude-modulated–frequency-modulated (AM-FM) images and real images have shown that the newly-proposed methods are very competitive with state-of-the-art BEMD methods. Moreover, we further evaluate the performance of BEMD methods according to their applications in image detail enhancement and image contrast & brightness enhancement. It may be noted that image contrast & brightness enhancement represents the first attempt to integrate BEMD with Retinex theory. Collectively, both types of enhancement validate the utility of the novel optimization-based approaches to BEMD proposed herein. [ABSTRACT FROM AUTHOR]
- Subjects :
- *IMAGE intensifiers
*HILBERT-Huang transform
*TRIANGULATION
Subjects
Details
- Language :
- English
- ISSN :
- 10512004
- Volume :
- 133
- Database :
- Academic Search Index
- Journal :
- Digital Signal Processing
- Publication Type :
- Periodical
- Accession number :
- 161281336
- Full Text :
- https://doi.org/10.1016/j.dsp.2022.103891