A new image fusion technology based on object extraction and NSCT

Suxia Xing; Peng Liu

doi:10.1117/12.2019643

4 March 2013 A new image fusion technology based on object extraction and NSCT

Suxia Xing, Peng Liu

Proceedings Volume 8761, PIAGENG 2013: Image Processing and Photonics for Agricultural Engineering; 87610B (2013) https://doi.org/10.1117/12.2019643
Event: Third International Conference on Photonics and Image in Agriculture Engineering (PIAGENG 2013), 2013, Sanya, China

Abstract

In this effort, we proposed an new image fusion technique, utilizing Renyi entropy’s object extraction and Non-Subsampled Contourlet Transform (NSCT), for improved visible effect of the image. NSCT is a multiscale transform method, it is a shift-invariant, linear phase, ‘‘true” two-dimensional transform that can decomposes an image into any directional sub-images to capture the intrinsic geometrical structure. In this paper we decompose visible image into 21, 22, and 23 directional sub-images at three different level respectively. Image enhancement is performed at the decomposition level and fused. Renyi entropy is a generalized information entropy. Infrared image can be divided into two parts of the object and the background through the maximum value of Renyi entropy. Image fusion is performed after NSCT and Renyi entropy. The fused image has significantly improved brightness and higher contrast than other images. In order to evaluate the proposed method, information entropy (IE), standard deviation (STD), spatial frequency (SF) and mutual information (MI) are adopted to compare with Laplace, wavelet, and NSCT et al. Results are shown that all evaluation value of the proposed method is higher than that of other methods, and it is a better image fusion method.

Citation Download Citation

Suxia Xing and Peng Liu "A new image fusion technology based on object extraction and NSCT", Proc. SPIE 8761, PIAGENG 2013: Image Processing and Photonics for Agricultural Engineering, 87610B (4 March 2013); https://doi.org/10.1117/12.2019643

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