Using the simplified pulse coupled neural network (S-PCNN) model and hue, saturation and value (HSV) color space, an effective color image fusion algorithm was proposed in this paper. In the HSV color space, using S-PCNN, the feature region clustering of each component (H, S, V) was done; the fusion of the various components of the different source images based on the oscillation frequency graph (OFG) was achieved; then through the inverse HSV transform to get RGB color image, the fusion of the color image were realized. Experimental results show that the algorithm both in the subjective visual effect and objective evaluation criteria is superior to other common color image fusion algorithms.
In CIELab color space, we propose a remote sensing image fusion technique based on nonsubsampled shearlet transform (NSST) and pulse coupled neural network (PCNN), which aim to improve the efficiency and performance of the remote sensing image fusion by combining the excellent properties of the two methods. First, panchromatic (PAN) and multispectral (MS) are transformed into CIELab color space to get different color components. Second, PAN and L component of MS are decomposed by the NSST to obtain corresponding the low-frequency coefficients and high-frequency coefficients. Third, the low-frequency coefficients are fused by intersecting cortical model (ICM); the high-frequency coefficients are divided into several sub-blocks to calculate the average gradient (AG), and the linking strength β of PCNN model is determined by the AG, so that the parameters β can be adaptively set according to the quality of the sub-block images, then the sub-blocks image are input into PCNN to get the oscillation frequency graph (OFG), the method can get the fused high-frequency coefficients according to the OFG. Finally, the fused L component is obtained by inverse NSST, and the fused RGB color image is obtained through inverse CIELab transform. The experimental results demonstrate that the proposed method provide better effect compared with other common methods.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.