Research Article

A New Study of Blind Deconvolution with Implicit Incorporation of Nonnegativity Constraints

Figure 3

Experimental results on Image 1 with Blur 1. Row 1, l-r: Image 1, received data corrupted by Blur 1, restored image using Algorithm 2. The PSNR/SNR is lowered by Algorithm 1 by 2.25/3.7 from 20.15/13.54 to 17.90/9.84 but increased using Algorithm 2 by 8.21/8.4 to 28.36/21.94. Row 2, l-r: Image 1, received data corrupted by Blur 2, restored image using Algorithm 2. The PSNR/SNR is increased by 19.44/34.53 from 7.88/−13.4 to 27.32/21.13. Our model is capable of restoring edges and preserving the background in black.
(a) True image
(b) Received SNR = 13.5
(c) Algorithm 1 [3] SNR = 9.8
(d) Algorithm 2 SNR = 21.9
(e) True image
(f) Received PSNR = 7.9
(g) Algorithm 2 PSNR = 27.3