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

Figure 7

Test results on Images 5-6 with Blur 2. Row 1, l-r for Image 5, received data , restored image using Algorithm 2 and by Algorithm 3. The PSNR/SNR is increased from 7.65/−12.97 by 15.7/30.42 to 23.35/17.45 using Algorithm 2 and by 13.12/27.75 to 20.77/14.78 using Algorithm 3. Row 2, l-r: Image 6, received data corrupted by Blur 2, restored image using Algorithm 2. The PSNR/SNR is increased by 3.37/3.77 from 18.18/9.91 to 21.55/13.68. Our model is capable of restoring detailed features in these challenging cases of Gaussian blur. Most of the details are restored in both cases. The slower Algorithm 2 performs better than the faster Algorithm 3.

(a) True image

(b) Received data

(c) Algorithm 2 PSNR = 23.4

(d) Algorithm 3 PSNR = 20.8

(e) True image

(f) Received data

(g) Algorithm 2 PSNR = 21.6