Research Article
A New Study of Blind Deconvolution with Implicit Incorporation of Nonnegativity Constraints
Figure 4
Experimental results on Images 2–4 with Blur 1. Row 1, l-r: Image 2, received data corrupted by Blur 1, restored image using Algorithm 2. The PSNR/SNR is increased by 4.53/5.01 from 20.76/6.58 to 25.29/11.59. Row 2, l-r: Image 2, received data corrupted by Blur 2, restored image using Algorithm 2. The PSNR/SNR is increased by 15.26/30.52 from 12.61/−13.85 to 27.87/16.67. Our model is capable of restoring details in both cases and of preserving the background in black. Row 3, l-r: Image 3, received data corrupted by Blur 1, restored image using Algorithm 2. The PSNR/SNR is increased by 11.5/11.66 from 24.26/18.5 to 35.76/30.16. Row 4, l-r: Image 4, received data corrupted by Blur 1, restored image using Algorithm 2. The PSNR/SNR is increased by 11.76/11.97 from 19.85/15.72 to 31.61/27.69. Our model is capable of restoring many detailed features and some fine details as well as sharpening edges. There are very few defects in the restored image, notably surrounding the rope in (l).