Jingming Xia1, Yi Lu1, Ling Tan2,*, Ping Jiang3
CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 613-624, 2021, DOI:10.32604/cmc.2021.013457
- 12 January 2021
Abstract Multi-source information can be obtained through the fusion of infrared images and visible light images, which have the characteristics of complementary information. However, the existing acquisition methods of fusion images have disadvantages such as blurred edges, low contrast, and loss of details. Based on convolution sparse representation and improved pulse-coupled neural network this paper proposes an image fusion algorithm that decompose the source images into high-frequency and low-frequency subbands by non-subsampled Shearlet Transform (NSST). Furthermore, the low-frequency subbands were fused by convolutional sparse representation (CSR), and the high-frequency subbands were fused by an improved pulse More >
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