Multi-focus image fusion based on L1 image transform
- PDF / 6,982,854 Bytes
- 28 Pages / 439.642 x 666.49 pts Page_size
- 48 Downloads / 239 Views
Multi-focus image fusion based on L1 image transform Shuang Yu1,2 · Xiongfei Li1,2 · Mingrui Ma3 · Xiaoli Zhang1,2
· Shiping Chen4
Received: 28 May 2020 / Revised: 12 August 2020 / Accepted: 15 September 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract In this paper, a new multi-focus image fusion algorithm based on L1 image transform is proposed. A distinctive advantage of the proposed algorithm is that an edge-preserving image decomposition (EPID) framework is constructed by introducing a L1-norm based image transform, which can not only effectively preserve and sharpen salient edges and ridges while eliminating insignificant details in the smoothing subband, but also maintain the detail information in the detail subbands. Another advantage is that the fusion rules for the smoothing subband and detail subbands are designed respectively according to their own characteristics so that both the structure and detail information can be fully retained. The fusion process mainly consists of the following three steps. Firstly, each source image is decomposed into a smoothing subband and several detail subbands by utilizing the EPID framework. Then, the subbands are fused by different fusion rules respectively to obtain a fused smoothing subband and a series of fused detail subands. Finally, the final fused image is reconstructed with less distortions by synthesizing the fused smoothing subband and a series of fused detail subands. Experimental results demonstrate the superiority of the proposed algorithm in terms of the visual perception and objective assessments. Keywords Image fusion · Multi-focus · L1 image transform · Image quality
1 Introduction In the applications of imaging devices such as digital cameras, a lens can focus on an object at a particular distance, and the objects at the same distance can be sharply focused. However, objects at different distances will be out of focus and theoretically blurred. Consequently, we can hardly obtain an image with all objects in focus, which may affect human visual perception and further computer processing tasks. A promising solution to obtain Xiaoli Zhang
[email protected] 1
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
2
College of Computer Science and Technology, Jilin University, Changchun, 130012, China
3
College of Software, Jilin University, Changchun, 130012, China
4
CSIRO Data61, Sydney, NSW 2016, Australia
Multimedia Tools and Applications
an all-clear image is the multi-focus image fusion [2, 13]. It combines the supplementary information of several images obtained from the same scene but with different focuses to form a visually perceived and high-quality image [5]. This image can describe the scene more accurately than any single source image. Multi-focus image fusion has been widely applied in image processing tasks such as microscopic imaging [40], biomedical imaging [9], remote sensing [4], computer vision [37], a
Data Loading...
