Table 1.
Restoration models (M1 and M4) evaluated using PSNR and RelErr.
Recall that the PSNR and RelErr of M1-model are reported in [38].
Table 2.
The number of iterations and time (in seconds) for distinct blurs and gamma noise levels showed in [38] using M1-model for ADM scheme and the number of ADMM iterations and time (in seconds) for different blurs and gamma noise levels reported using our model-M4.
Table 3.
Performance of M4-model using SSIM on Lena, Cameraman and Rice images.
Table 4.
Restoration models (M2 and M4) evaluated using PSNR, number of iterations and time.
Recall that the PSNR, number of iterations and time of M2-model are reported in [1].
Table 5.
Restoration models (M2 and M4) for deblurring with denoising evaluated using PSNR, number of iterations and CPU-time in seconds.
Recall that the PSNR, number of iterations and CPU-time in seconds of M2-model are reported in [1].
Table 6.
Restoration results of M3-model and M4-model by evaluating PSNR, a no: of iterations and CPU-time in seconds.
Recall that the PSNR, a no: of iterations and CPU-time in seconds of M3-model are displayed in [2].
Table 7.
Restoration results of M3 and M4 for multiplicative deblurring, evaluated using PSNR, number of iterations and CPU-time in seconds.
Recall that the PSNR, no. of iterations and CPU-time in seconds of M3-model are shown in [2].
Fig 1.
Performance of M4-model on Lena image.
(a) original image; (b) blur image with (fspecial(‘disk’,5)); (c) noisy image with gamma noise variance 0.03; (d) the clean image contaminated by motion blur and the Gamma noise (Gn) with variance 0.03; (e) the restored image by our proposed model (M4).
Fig 2.
Performance of M4-model on Cameraman image.
(a) original image; (b) blurry image with (fspecial(‘gaussian’,7,5)); (c) gamma noise variance is 0.03; (d) the given image degraded by motion blur and the Gamma noise (Gn) with variance 0.03; (e) the image restored by our proposed model (M4).
Fig 3.
Performance of M4-model on Rice image.
(a) original image; (b) blur image with (fspecial(‘motion’,7)); (c) gamma noise variance is 0.03; (d) the given image degraded by the Gamma noise (Gn) with variance 0.03 and motion blur; (e) the output image by M4-model.
Fig 4.
Performance of M4-model on Phantom image.
(a) clean image; (b) degraded image with L = 10; (c) the restored image by our proposed model; (d) the observed image corrupted with L = 6 by multiplicative noise; (e) the recovered image by our proposed model (M4).
Fig 5.
Performance of M4-model on Cameraman image.
(a) original image; (b) degraded image with L = 10; (c) the restored image by our proposed model; (d) the observed image corrupted with L = 6; (e) the restored image by our proposed model (M4).
Fig 6.
Performance of M4-model on Parrot image.
(a) observed image; (b) noisy image with L = 10; (c) the restored image by our proposed model; (d) the observed image corrupted with L = 6; (e) the restored image by our proposed model (M4).
Fig 7.
Performance of M4-model on Parrot image.
(a) original image; (b) blur image with (fspecial(‘motion’,5,30)); (c) corrupted image with L = 10; (d) the original image contaminated by multiplicative noise with L = 10 and motion blur; (e) the recovered image by M4-model.
Fig 8.
Performance of M4-model on Cameraman image.
(a) original image; (b) blur image with (fspecial(‘motion’,5,30)); (c) degraded image with L = 10; (d) the clean image degraded by motion blur and the multiplicative noise with L = 10; (e) the restored image by the M4-model.
Fig 9.
Performance of M4-model on Parrot image.
(a) original image; (b) blur image with (fspecial(‘gaussian’,[7,7,2)); (c) contaminated image with L = 10; (d) the given image contaminated by motion blur and the multiplicative noise with L = 10; (e) the restored image by our proposed model (M4).
Fig 10.
Performance of M4-model on Cameraman image.
(a) original image; (b) blur image with (fspecial(‘gaussian’,[7,7],2)); (c) degraded image with L = 10; (d) the original image degraded by multiplicative noise with L = 10 and motion blur; (e) the restored image by our proposed model (M4).
Fig 11.
Performance of M4-model on Cameraman image.
(a) clean image; (b) the observed image contaminated by the Gaussian noise; (c)the observed image contaminated by the Gamma noise; (d) the observed image contaminated by the Rayleigh noise; (e) original image; (f) the recovered image by our proposed model (M4); (g) restoration by our proposed model (M4); (h) the restored image by M4-model.
Fig 12.
Performance of M4-model on Parrot image.
(a) original image; (b) the observed image contaminated by the Gaussian noise; (c)the observed image contaminated by the Gamma noise; (d) the observed image contaminated by the Rayleigh noise; (e) original image; (f) the recovered image by M4-model; (g) restoration by M4-model; (h) the recovered image by M4-model.
Fig 13.
Performance of M4-model on Cameraman image.
(a),(f),(k) original image; (b),(g),(l) blur image with (fspecial(‘motion’,5,30)), (fspecial(‘gaussian’,[7,7],2)), (psfMoffat(‘motion’,[7,7],1,5)); (c),(h),(m) degraded image with L = 10; (d),(i),(n) the clean image contaminated by motion blur, gaussian blur, Moffat and the Gamma noise with (L = 10); (e),(j),(o) the restored result by the M4-model.
Fig 14.
Performance of M4-model on Parrot image.
(a),(f),(k) original image; (b),(g),(l) blur image with (fspecial(‘motion’,5,30)), (fspecial(‘gaussian’,[7,7],2)), (psfMoffat(‘motion’,[7,7],1,5)); (c),(h),(m) noisy image with L = 10; (d),(i),(n) the observed image degraded by motion blur, gaussian blur, Moffat and the Gamma noise with (L = 10); (e),(j),(o) the restored image by our proposed model (M4).
Fig 15.
RGB Lena image 2562 result from the proposed model (M4).
(a) original Lena image; (b) blur image blurred by the within-channel kernel; (c) the observed image degraded by blurring operator and the multiplicative noise with σ2 = 0.1; (d) the restored image by our proposed model (M4).
Table 8.
Restoration results of the proposed model (M4) for multiplicative deblurring, evaluated using SNR, number of iterations and CPU-time in seconds.
Fig 16.
Performance of M4-model on color parrot image 2162.
(a) original image; (b) blur image contaminated by the cross-channel kernel; (c) the observed image degraded by blur and the multiplicative noise with σ2 = 0.2; (d) the restored image by our proposed model (M4).
Fig 17.
Performance of M4-model on color eye image 2788 × 1864.
(a) original image; (b) blur image corrupted by the cross-channel kernel; (c) the observed image degraded by blurring kernel and the multiplicative noise with σ2 = 0.1; (d) the restored image by our proposed model (M4).
Fig 18.
Performance of M4-model on color butterfly image 4502.
(a) original image; (b)blur image corrupted by the cross-channel kernel; (c) the observed image degraded by blur and the multiplicative noise with σ2 = 0.2; (d) the restored image by our proposed model (M4).