Polarization Image Interpolation Algorithm via Tensor Non-Negative Sparse Factorization

Junchao Zhang; Jianlai Chen; Haibo Luo; Degui Yang; Buge Liang

doi:10.3788/AOS202141.1411001

Acta Optica Sinica, Volume. 41, Issue 14, 1411001(2021)

Polarization Image Interpolation Algorithm via Tensor Non-Negative Sparse Factorization

Junchao Zhang¹, Jianlai Chen^1、*, Haibo Luo², Degui Yang¹, and Buge Liang¹

Author Affiliations

¹School of Aeronautics and Astronautics, Central South University, Changsha, Hunan 410083, China

²Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning 110169, China

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Figures & Tables(11)

Fig. 1. Micropolarization system and polarization tensor data structure. (a) Arrangement pattern of micropolarizer; (b) schematic of tensor decomposition of polarization data

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Fig. 2. Interpolation flow of polarizing image in focal plane

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Fig. 3. Results of reconstruction of different algorithms in scenario 1. (a) Ref. [16]; (b) Ref. [17]; (c) Ref. [19]; (d) Ref. [20]; (e) proposed algorithm; (f) ground truth

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Fig. 4. Results of reconstruction of different algorithms in scenario 2. (a) Ref. [16]; (b) Ref. [17]; (c) Ref. [19]; (d) Ref. [20]; (e) proposed algorithm; (f) ground truth

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Fig. 5. Reconstruction results under different image block sizes in scenario 1. (a) 2×2; (b) 4×4; (c) 8×8; (d) ground truth

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Fig. 6. Reconstruction results under different image block sizes in scenario 2. (a) 2×2; (b) 4×4; (c) 8×8; (d) ground truth

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Fig. 7. Reconstruction results of different algorithms in measured data. (a) Ref. [16]; (b) Ref. [17]; (c) Ref. [19]; (d) Ref. [20]; (e) proposed algorithm

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Table 1. RMSE results of different algorithms

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Table 1. RMSE results of different algorithms

Image	Ref.[16]	Ref.[17]	Ref.[19]	Ref.[20]	Proposedalgorithm
I₀	0.9172	0.9130	1.0161	0.8343	0.8289
I₄₅	0.9417	0.9331	1.0818	0.8586	0.8492
I₉₀	1.0278	1.0207	1.0431	0.9386	0.9234
I₁₃₅	1.0154	1.0065	1.1373	0.9214	0.9011
S₀	1.3521	1.3431	1.3120	1.2923	1.3955
DoLP	0.0052	0.0052	0.0068	0.0049	0.0041

Table 2. PSNR results of different algorithms

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Table 2. PSNR results of different algorithms

Image	Ref.[16]	Ref.[17]	Ref.[19]	Ref.[20]	Proposedalgorithm
I₀	49.1474	49.1852	48.4587	49.9669	50.0212
I₄₅	49.0598	49.1380	47.9479	49.8136	49.9674
I₉₀	48.2762	48.3278	48.2223	49.0320	49.1974
I₁₃₅	48.3604	48.4241	47.4757	49.1414	49.3548
S₀	45.9172	45.9708	46.2035	46.2743	45.6678
DoLP	45.8336	45.9552	43.7292	46.6113	48.2496

Table 3. SSIM results of different algorithms

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Table 3. SSIM results of different algorithms

Image	Ref.[16]	Ref.[17]	Ref.[19]	Ref.[20]	Proposedalgorithm
I₀	0.9817	0.9816	0.9793	0.9850	0.9844
I₄₅	0.9822	0.9822	0.9787	0.9856	0.9849
I₉₀	0.9805	0.9805	0.9799	0.9843	0.9837
I₁₃₅	0.9788	0.9787	0.9767	0.9826	0.9821
S₀	0.9856	0.9856	0.9841	0.9868	0.9843
DoLP	0.8815	0.8815	0.8776	0.9093	0.9335

Table 4. Running time of different algorithms
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Table 4. Running time of different algorithms
Algorithm Ref. [16] Ref. [17] Ref. [19] Ref. [20] Proposed algorithm
Running time /s 0.48 0.26 0.22 308.00 278.00

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Junchao Zhang, Jianlai Chen, Haibo Luo, Degui Yang, Buge Liang. Polarization Image Interpolation Algorithm via Tensor Non-Negative Sparse Factorization[J]. Acta Optica Sinica, 2021, 41(14): 1411001

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