A Multi-focus Image Fusion Method Based on Laplacian Pyramid

Abstract

<p class="Abstract">This paper presented a simple and efficient algorithm for multi-focus image fusion, which used a multi-resolution signal decomposition scheme called Laplacian pyramid method. The principle of Laplacian pyramid transform is introduced, and based on it the fusion strategy is described in detail. The method mainly composed of three steps. Firstly, the Laplacian pyramids of each source image are deconstructed separately, and then each level of new Laplacian pyramid is fused by adopting different fusion rules. To the top level, it adopts the maximum region information rule; and to the rest levels, it adopts the maximum region energy rule. Finally, the fused image is obtained by inverse Laplacian pyramid transform. Two sets of images are applied to verify the fusion approach proposed and compared it with other fusion approaches. By analyzing the experimental results, it showed that this method has good performance, and the quality of the fused image is better than the results of other methods.</p&gt

Image Forgery Detection using Multi Scale Entropy Filter and Local Phase Quantization

“…This section presents visual and quantitative results of the proposed method and the other state-of-the-art methods (PCA [26], sharp fusion [27], linear fusion [28], LP [29], DWT [30], SWT [31], LWT [32], MWT [15], Cvt [34], CT [14] and NSCT [33]) as well as comparison of the proposed method with them. We have performed experiments with several set of images, but for demonstration purpose, here, we present results for four set of images.…”

“…In spatial domain methods, the proposed method is compared with principal component analysis [26], sharp fusion [27] and linear fusion [28] based methods and in transform domain methods, the proposed method is compared with Laplacian pyramid [29], discrete wavelet transform [30], stationary wavelet transform [31], lifting wavelet transform [32] and multiwavelet transform [15] based fusion methods. Apart from wavelet domain methods, we have also compared the proposed method with some new generation wavelet based methods that are highly directional like contourlet transform [14], nonsubsampled contourlet transform [33] and curvelet transform [34] based methods.…”

“…[34], (e) NSCT [33], (f) CT [14], (g) SWT [31], (h) DWT [30], (i) LWT [32] , (j) MWT [15], (k) LP [29], (l) PCA [26], (m) linear fusion [28], (n) sharp fusion [27]. [34], (e) NSCT [33], (f) CT [14], (g) SWT [31], (h) DWT [30], (i) LWT [32], (j) MWT [15], (k) LP [29], (l) PCA [26], (m) linear fusion [28], (n) sharp fusion [27]. [34], (e) NSCT [33], (f) CT [14], (g) SWT [31], (h) DWT [30], (i) LWT [32], (j) MWT [15], (k) LP [29], (l) PCA [26], (m) linear fusion [28], (n) sharp fusion [27].…”

“…[34], (e) NSCT [33], (f) CT [14], (g) SWT [31], (h) DWT [30], (i) LWT [32], (j) MWT [15], (k) LP [29], (l) PCA [26], (m) linear fusion [28], (n) sharp fusion [27]. [34], (e) NSCT [33], (f) CT [14], (g) SWT [31], (h) DWT [30], (i) LWT [32], (j) MWT [15], (k) LP [29], (l) PCA [26], (m) linear fusion [28], (n) sharp fusion [27].…”

A Study and Evaluation of Transform Domain based Image Fusion Techniques for Visual Sensor Networks

“…The basic idea behind the Laplacian pyramid is to perform a pyramid decomposition on every source image, then integrate of these decompositions to make a composite representation and finally reconstruct the fused image by performing an inverse pyramid transform [14]. The various steps used in Laplacian pyramid based fusion method are as follows:…”