A visible polarization image fusion algorithm based on NSST transform

Jiang, Zhao Zhen; Han, Yu; Ye, Fei; Ren, Shuaijun; Zhai, Huifang; Zhenghao, Hu

doi:10.1117/12.2579549

Cited by 2 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To evaluate the performance of the proposed fusion algorithm, MATLAB software was used to conduct fusion simulation experiments on the calculated polarization and intensity images. Six algorithms, including Mean fusion, Weighted fusion [9], PCNN fusion [10], Wavelet fusion [11] , NSCT-PCNN algorithm 1 fusion [12] , and NSCT-PCNN algorithm 2 fusion [13], were selected for comparison with the proposed fusion algorithm. Both subjective and objective evaluations were performed based on the fusion results of each algorithm.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…In response to the issue of setting the free parameters in the dual-channel PCNN network shown in Figure 1, which may lead to poor robustness if manually determined, this paper addresses this problem by adopting an adaptive computation approach inspired by the reference [17] for parameter setting. The three free parameters of the model are calculated using the following formulas (9)(10)(11):…”

Section: Adaptive Free Parametersmentioning

confidence: 99%

See 1 more Smart Citation

Polarization fusion algorithm based on NSCT decomposition and improved dual-channel PCNN model

Hu,

Fan,

Bian

et al. 2024

Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023)

View full text Add to dashboard Cite

Polarization imaging is capable of effectively showcasing the polarization properties of objects while also mitigating the impact of strong light and enhancing the visibility of weak light. As a result, it compensates for the limitations of intensity imaging in low-light environments. To fully utilize the advantages of intensity and polarization images, this paper proposes a method that combines NSCT decomposition and an improved dual-channel PCNN model. The method enhances the traditional PCNN model by extending the input to dual channels, employing adaptively calculated parameters, and applying adaptive linking weights to the high- and low-frequency images obtained from NSCT decomposition. The highfrequency image utilizes the MSMG operator, while the low-frequency image employs a weight map controlled by multiple parameters. This method is utilized for fusion processing of intensity and polarization images.By conducting comparative experiments with six commonly used algorithms, the results demonstrate the superior performance of this method in terms of preserving texture details, providing high-quality images, achieving information-rich fusion, generating fused images with rich grayscale levels, and maintaining structural similarity. In summary, the proposed method exhibits significant advantages in fusing intensity and polarization images.

show abstract