Multi-Scale Image Blind Deblurring Based on Salient Intensity and a priori Gradient

Chen Chen; Jinxin Xu; Caihua Wei; Qingwu Li

doi:10.3788/LOP57.041505

Journals >Laser & Optoelectronics Progress >Volume 57 >Issue 4 >Page 041505 > Article

Laser & Optoelectronics Progress
Vol. 57, Issue 4, 041505 (2020)

Multi-Scale Image Blind Deblurring Based on Salient Intensity and a priori Gradient

Chen Chen¹, Jinxin Xu¹, Caihua Wei², and Qingwu Li^1、*

Author Affiliations

¹College of Internet of Things Engineering, Hohai University, Changzhou, Jiangsu 213022, China

²Institute of Fluid Physics, China Academy Of Engineering Physics, Mianyang, Sichuan 621900, China

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DOI: 10.3788/LOP57.041505 Cite this Article Set citation alerts

Chen Chen, Jinxin Xu, Caihua Wei, Qingwu Li. Multi-Scale Image Blind Deblurring Based on Salient Intensity and a priori Gradient[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041505 Copy Citation Text

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Qualitative comparison of restoration results for simulated blurred images (σ=3%). (a) Blurred image; (b) algorithm in Ref. [7]; (c) algorithm in Ref. [8]; (d) algorithm in Ref. [9]; (e) algorithm in Ref. [10]; (f) algorithm in Ref. [11]; (g) our algorithm

Fig. 1. Qualitative comparison of restoration results for simulated blurred images (σ=3%). (a) Blurred image; (b) algorithm in Ref. [7]; (c) algorithm in Ref. [8]; (d) algorithm in Ref. [9]; (e) algorithm in Ref. [10]; (f) algorithm in Ref. [11]; (g) our algorithm

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Fig. 2. Comparison of image blur kernel similarity under different algorithms

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Fig. 3. Qualitative comparison of restoration results for true images. (a) Blurred image; (b) algorithm in Ref. [7]; (c) algorithm in Ref. [8]; (d) algorithm in Ref. [9]; (e) algorithm in Ref. [10]; (f) algorithm in Ref. [11]; (g) our algorithm

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Image	Algorithmin Ref. [7]		Algorithmin Ref. [8]		Algorithmin Ref. [9]		Algorithmin Ref. [10]		Algorithmin Ref. [11]		Our algorithm
Image	PSNR /dB	MSE /%	PSNR /dB	MSE /%	PSNR /dB	MSE /%	PSNR /dB	MSE /%	PSNR /dB	MSE /%	PSNR /dB	MSE /%
Carton	68.47	0.94	69.87	0.78	64.75	2.53	68.72	0.92	70.16	0.73	71.62	0.62
House	75.05	0.21	70.79	0.62	72.15	0.42	78.85	0.14	83.12	0.09	82.83	0.10
Roman	68.76	0.89	68.54	1.05	74.50	0.23	68.60	0.92	77.41	0.16	78.36	0.14
Buddha	74.42	0.25	71.59	0.67	73.38	0.33	74.34	0.26	75.13	0.23	76.85	0.21
Building	64.18	1.48	67.42	1.02	68.35	0.93	69.74	0.81	70.22	0.71	72.47	0.58

Table 1. Statistic results of PSNR and MSE of different algorithms

Romanimage	Layer 7image	Layer 6image	Layer 5image	Layer 4image	Layer 3image	Layer 2image	Layer 1image	Totaltime
Image resolution	10×7	19×14	38×27	75×53	150×105	300×210	600×420
Kernel resolution	7×7	11×11	15×15	21×21	29×29	39×39	51×51
Normal iteration /s	18	36	90	221	429	864	1944	3602
Adaptive iteration /s	18	36	82	160	306	558	1026	2196

Table 2. Statistical results before and after using adaptive iteration

Romanimage	Algorithmin Ref. [7]	Algorithmin Ref. [8]	Algorithmin Ref. [9]	Algorithmin Ref. [10]	Algorithmin Ref. [11]	Our algorithm(normalization)	Our algorithm(adaptiveiteration)
Time /h	0.10	0.12	0.43	0.92	0.95	1.00	0.61

Table 3. Run time of 6 algorithms (Roman image)

Chen Chen, Jinxin Xu, Caihua Wei, Qingwu Li. Multi-Scale Image Blind Deblurring Based on Salient Intensity and a priori Gradient[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041505

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