A multi-focus image fusion method via region mosaicking on Laplacian pyramids

<div><p>In this paper, a method named Region Mosaicking on Laplacian Pyramids (RMLP) is proposed to fuse multi-focus images that is captured by microscope. First, the Sum-Modified-Laplacian is applied to measure the focus of multi-focus images. Then the density-based region growing algorithm is utilized to segment the focused region mask of each image. Finally, the mask is decomposed into a mask pyramid to supervise region mosaicking on a Laplacian pyramid. The region level pyramid keeps more original information than the pixel level. The experiment results show that RMLP has best performance in quantitative comparison with other methods. In addition, RMLP is insensitive to noise and can reduces the color distortion of the fused images on two datasets.</p></div

An illustration of density-connectivity with same mask label.

<p>An illustration of density-connectivity with same mask label.</p

Flowchart of multi-focus images fusion based on RMLP approach.

<p>Flowchart of multi-focus images fusion based on RMLP approach.</p

An illustration of 3D object imaging with an optical camera.

<p><i>I</i>_<i>A</i> and <i>I</i>_<i>B</i> are image pixels of point <i>A</i> and <i>B</i> respectively. With the current focal length, the surface that point <i>A</i> lies on is focused while that of point <i>B</i> is not. It can be seen1 that in-focus pixels in the image plane form a continuous region. By adjusting the object distance to the lens, a series of defocused (part-in-focus) images could be obtained.</p

Illustration of the S2 Dataset and its fusion results.

<p>(a)-(f) are six random sampled examples from sixty multi-focus images, (g) is the mask image with only EOF measurement, (h) is the mask image with the proposed DBRG segmentation algorithm. (i) is the fusion result of the Laplacian pyramid (LP) method and (j)is the fusion result of the proposed RMLP.</p

A multi-focus image fusion method via region mosaicking on Laplacian pyramids - Fig 7

<p>Illustration of quantitative evaluation (a) RMSE and (b) SSIM under different DBRG radius for the focus region mask segmentation. (c) RMSE and (d) SSIM with different pyramid layers. (e) and (f) are comparisons of nine methods.</p

Illustration of RMLP with two multi-focus images and three pyramid layers.

<p>Illustration of RMLP with two multi-focus images and three pyramid layers.</p

Illustration of the S1 Dataset and its fusion results.

<p>(a)-(f) are six random sampled examples from fifty multi-focus images, (g) is the mask image with only EOF measurement, (h) is the mask image with the proposed DBRG segmentation algorithm. (i) is the fusion result of the Laplacian pyramid (LP) method [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0191085#pone.0191085.ref011" target="_blank">11</a>] and (j)is the fusion result of the proposed RMLP.</p