Infrared and visible image fusion using guided filter and convolutional sparse representation

Xianhong, 刘先红 Liu; Zhibin, 陈志斌 Chen; Mengze, 秦梦泽 Qin

doi:10.3788/ope.20182605.1242

Cited by 4 publications

(1 citation statement)

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“…Finally, the denoised image blocks are obtained by inverse DCT. However, the image after noise reduction will be weakened and blurred due to the processing technology, so it is necessary to enhance the denoised image [23,24].…”

Section: Image Denoising Based On Discrete Cosine Transformmentioning

confidence: 99%

Digital Image Progressive Fusion Method Based on Discrete Cosine Transform

Chen

2023

Journal of Mathematics

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The current progressive fusion methods for digital images have poor denoising performance, which leads to a decrease in image quality after progressive fusion. Therefore, a new method for digital image progressive fusion was proposed based on discrete cosine transform, and its effectiveness was verified through experiments. The experimental results show that the proposed method has a PSNR value higher than 42.13 db in image fusion, both of which are higher than the comparison method, and the fusion effect comparison also has higher image quality. In terms of fusion time, the time of the research method is lower than that of the comparison method when the data volume is between 10 and 100, while in the comparison of structural similarity, the structural similarity of the image fused by the research method is always higher than 0.83. Overall, the fusion method proposed in the study results in higher image quality and is effective in progressive digital image fusion, which is of great significance for practical digital image fusion.

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Section: Image Denoising Based On Discrete Cosine Transformmentioning

confidence: 99%