Multi-focus noisy image fusion based on gradient regularized convolutional sparse representatione
Article No.: 34, Pages 1 - 7
Abstract
The method proposes a multi-focus noisy image fusion algorithm combining gradient regularized convolutional sparse representatione and spatial frequency. Firstly, the source image is decomposed into a base layer and a detail layer through two-scale image decomposition. The detail layer uses the Alternating Direction Method of Multipliers (ADMM) to solve the convolutional sparse coefficients with gradient penalties to complete the fusion of detail layer coefficients. Then, The base layer uses the spatial frequency to judge the focus area, the spatial frequency and the "choose-max" strategy are applied to achieved the multi-focus fusion result of base layer. Finally, the fused image is calculated as a superposition of the base layer and the detail layer. Experimental results show that compared with other algorithms, this algorithm provides excellent subjective visual perception and objective evaluation metrics.
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Index Terms
- Multi-focus noisy image fusion based on gradient regularized convolutional sparse representatione
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Information & Contributors
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Published In
March 2021
512 pages
ISBN:9781450383080
DOI:10.1145/3444685
- General Chairs:
- Tat-Seng Chua,
- Jingdong Wang,
- Qi Tian,
- Program Chairs:
- Cathal Gurrin,
- Jia Jia,
- Hanwang Zhang,
- Qianru Sun
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Published: 03 May 2021
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- Regional Joint Fund of the National Natural Science Foundation of China
- The National Natural Science Foundation of China
- The Jilin Province Science and Technology Development Plan Project
- The National Key Research and Development Program of China
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