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VMD-DMD coupled data-driven approach for visual saliency in noisy images

Publication Type : Journal Article

Publisher : Springer

Source : Multimedia Tools and Application, Springer, 2020 (Scopus)

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Campus : Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking

Department : Center for Computational Engineering and Networking (CEN), Computer Science, Electronics and Communication

Year : 2020

Abstract : Human visual system is endowed with an innate capability of distinguishing the salient regions of an image. It do so even in the presence of noise and other natural disturbances. Conventional8 computational saliency models in the literature assume that the input images are clean, though an explicit treatment of noise is missing. In this paper, we propose a coupled data-driven approach for estimating saliency map for a noisy input using Variational Mode Decomposition (VMD) and Dynamic Mode Decomposition(DMD. Variational Mode Decomposition (VMD) is a well received technique explored for denoising in the literature. VMD modes with high entropy (randomness) are removed and the residual modes are employed to generate a scalar valued saliency map. The proposed method is compared against seven state-of-the-art methods over a wide range of noise strengths. The submitted approach furnished comparable results with respect to state-of-the art methods for clean and noisy images in terms of various benchmark performance measures.

Cite this Research Publication : OK Sikha, KP Soman, Sachin Kumar S, VMD-DMD Coupled Data Driven Approach for Visual Saliency in Noisy Images, Multimedia Tools and Application, Springer, 2020 (Scopus)

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