An Improved Single-Frame Super-Resolution Algorithm for Magnetic Resonance Image

Fig. 1. An example of stationary wavelet transform using db2 in internal dataset. (a) Original image; (b)-(d) horizontal, vertical, diagonal high frequency of 1-level SWT; (e)-(g) horizontal, vertical, diagonal high frequency of 2-level SWT

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Fig. 2. Demonstration of overlapping separating

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Fig. 3. Framework of proposed super-resolution method

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Fig. 4. Visual comparison of 3×upscaling reconstruction effect. (a) Original image; (b) bicubic interpolation; (c) method in Ref. [5]; (d) method in Ref. [6]; (e) SRCNN; (f) proposed method (with haar)

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Table 1. Comparison of different patch sizes (3×upscaling, dictionary size 500, clustering number 3, wavelet base db2)
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Table 1. Comparison of different patch sizes (3×upscaling, dictionary size 500, clustering number 3, wavelet base db2)
Window size /(pixel×pixel) PSNR SSIM RLNE EPI
2×2 28.4266 0.9939 0.0232 1.3792
3×3 28.5170 0.9941 0.0228 1.3618
5×5 28.3959 0.9935 0.0234 1.3691
7×7 28.2219 0.9933 0.0239 1.3858

Table 2. Comparison of different dictionary sizes (3×upscaling, patch size 3 pixel×3 pixel, clustering number 3, wavelet base db2)
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Table 2. Comparison of different dictionary sizes (3×upscaling, patch size 3 pixel×3 pixel, clustering number 3, wavelet base db2)
Dictionarysize PSNR SSIM RLNE EPI
2000 28.6238 0.9945 0.0222 1.4045
1000 28.6330 0.9945 0.0222 1.3779
500 28.5170 0.9941 0.0228 1.3618
100 28.2422 0.9928 0.0242 1.3657

Table 3. Comparison of different clustering numbers (3×upscaling, patch size 3 pixel×3 pixel, dictionary size 1000, wavelet base db2)
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Table 3. Comparison of different clustering numbers (3×upscaling, patch size 3 pixel×3 pixel, dictionary size 1000, wavelet base db2)
Clusteringnumber PSNR SSIM RLNE EPI
3 28.6330 0.9945 0.0222 1.3779
4 28.5408 0.9941 0.0226 1.3657
5 28.5996 0.9943 0.0223 1.3807

Table 4. Comparison of different wavelet bases (3×upscaling, patch size 3 pixel×3 pixel, dictionary size 1000, clustering number 3)

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Table 4. Comparison of different wavelet bases (3×upscaling, patch size 3 pixel×3 pixel, dictionary size 1000, clustering number 3)

Index	haar	db2	db3	db4	sym2	sym3	sym4	sym6	sym8	coif1	coif2	dmey
PSNR	28.7574	28.6330	28.5366	28.6411	28.5444	28.6124	28.5836	28.5155	28.4995	28.5814	28.603	28.3491
SSIM	0.9949	0.9945	0.9941	0.9943	0.9941	0.9943	0.9945	0.9942	0.9941	0.9945	0.9941	0.9938
RLNE	0.0215	0.0222	0.0227	0.0221	0.0226	0.0223	0.0224	0.0228	0.0228	0.0224	0.0223	0.0237
EPI	1.3583	1.3657	1.3728	1.3576	1.3690	1.3561	1.3675	1.3671	1.3777	1.3701	1.3575	1.3766

Table 5. Comparison of 3×upscaling reconstruction effect of different methodsdB

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Table 5. Comparison of 3×upscaling reconstruction effect of different methodsdB

Algorithm	MRI				DCE-MRI
Algorithm	PSNR	SSIM	EPI	RLNE	PSNR	SSIM	EPI	RLNE
Bicubic interpolation	30.1714	0.8817	0.4475	0.0348	32.4535	0.8854	0.5003	0.0094
Ref. [5]	31.2093	0.8945	0.5570	0.0274	33.8091	0.8923	0.5718	0.0070
Ref. [6]	31.5505	0.9025	0.5641	0.0254	34.2530	0.9059	0.6290	0.0062
Ref. [7]	31.7256	0.8984	0.5820	0.0330	34.0285	0.8698	0.6237	0.0148
db2	31.7225	0.9040	0.5721	0.0244	34.3888	0.9066	0.6343	0.0060
haar	31.7220	0.9036	0.5740	0.0245	34.5002	0.9072	0.6343	0.0058
sym4	31.7052	0.9041	0.5705	0.0244	34.3643	0.9066	0.6356	0.0061
db2_IBP	31.8756	0.9089	0.6141	0.0238	34.6785	0.9135	0.6831	0.0056
haar_IBP	31.8918	0.9088	0.6147	0.0240	34.5985	0.9104	0.6897	0.0057
sym4_IBP	31.8480	0.9089	0.6155	0.0237	34.4396	0.9094	0.6897	0.0060

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Jinghui Chu, Fengshuo Hu, Jiaqi Zhang, Wei Lü. An Improved Single-Frame Super-Resolution Algorithm for Magnetic Resonance Image[J]. Laser & Optoelectronics Progress, 2018, 55(5): 051009

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Paper Information

Category: Image processing

Received: Oct. 18, 2017

Accepted: --

Published Online: Sep. 11, 2018

The Author Email: Wei Lü ( luwei@tju.edu.cn)

DOI:10.3788/LOP55.051009

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Comparison of different patch sizes (3×upscaling, dictionary size 500, clustering number 3, wavelet base db2)

Table 1. Comparison of different patch sizes (3×upscaling, dictionary size 500, clustering number 3, wavelet base db2)

Table 2. Comparison of different dictionary sizes (3×upscaling, patch size 3 pixel×3 pixel, clustering number 3, wavelet base db2)

Table 2. Comparison of different dictionary sizes (3×upscaling, patch size 3 pixel×3 pixel, clustering number 3, wavelet base db2)

Table 3. Comparison of different clustering numbers (3×upscaling, patch size 3 pixel×3 pixel, dictionary size 1000, wavelet base db2)

Table 3. Comparison of different clustering numbers (3×upscaling, patch size 3 pixel×3 pixel, dictionary size 1000, wavelet base db2)

Table 4. Comparison of different wavelet bases (3×upscaling, patch size 3 pixel×3 pixel, dictionary size 1000, clustering number 3)

Table 4. Comparison of different wavelet bases (3×upscaling, patch size 3 pixel×3 pixel, dictionary size 1000, clustering number 3)

Table 5. Comparison of 3×upscaling reconstruction effect of different methodsdB

Table 5. Comparison of 3×upscaling reconstruction effect of different methodsdB