Discriminative learning of iteration-wise priors for blind deconvolution

Abstract

The maximum a posterior (MAP)-based blind deconvo lution framework generally involves two stages: blur ker nel estimation and non-blind restoration. For blur kernel estimation, sharp edge prediction and carefully designed image priors are vital to the success of MAP In this pa per, we propose a blind deconvolution framework together with iteration specific priors for better blur kernel estima tion. The family of hyper-Laplacian (Pr( d) ex e-Ildll�/ "\ ) is adopted for modeling iteration-wise priors of image gra dients, where each iteration has its own model parameters {A (t ), p(t )}. To avoid heavy parameter tuning, all iteration wise model parameters can be learned using our principled discriminative learning model from a training set, and can be directly applied to other dataset and real blurry images. Interestingly, with the generalized shrinkage / thresholding operator, negative p value (p < 0) is allowable and we find that it contributes more in estimating the coarse shape of blur kernel. Experimental results on synthetic and real world images demonstrate that our method achieves bet ter deblurring results than the existing gradient prior-based methods. Compared with the state-of-the-art patch prior based method, our method is competitive in restoration re sults but is much more efficient.

Efficient non-uniform deblurring based on generalized additive convolution model

“…Due to its superiority over S-GAC and F-GAC, H-GAC is then compared with the state-of-the-art geometrical methods, i.e., Whyte et al [29], Gupta et al [30], and Hu and Zhang [33], and the state-of-the-art EFF-based methods, i.e., Hirsch et al [32], Xu et al [16], and Cao et al [27]. Finally, in the comparison with the state-of-the-art uniform deblurring methods [9,21,24,43], slice GAC with GS-GAC is adopted. All the experiments are conducted on a PC with Intel Core i5-2400 3.10 GHz CPU and 16 G RAM memory, and the proposed method is implemented in Matlab.…”

“…Compared with uniform deblurring methods, GAC method also can achieve satisfactory deblurring results. , Xu et al [21], Pan et al [43], and Zuo et al [24], respectively. The last column are deblurring results by GAC.…”

“…In the comparison experiments with the state-of-theart uniform deblurring methods, i.e., Shan et al [9], Xu et al [21], Zuo et al [24], and Pan et al [43], we adopt GS-GAC, which performs better in handling spatially invariant blurry images. In Fig.…”

“…On the other hand, by enforcing sparser priors [10,16,17] or exploiting edge prediction step [9,[18][19][20][21] to select salient edges in latent image, several carefully designed MAP algorithms can also exhibit promising performance. Most recently, discriminative learning approaches had been developed to learn proper priors for better estimating blur kernel [22][23][24][25].…”

A two-stage filter for removing salt-and-pepper noise using noise detector based on characteristic difference parameter and adaptive directional mean filter

“…During the past several decades, many techniques have been developed to restore the corrupted image, such as the blind denoising method [ 9 , 10 ] and total-variation denoising method [ 11 , 12 ]. Among the traditional denoising methods, the standard median filter is one of the most popular nonlinear filters for the removal of salt-and-pepper noise in terms of its good denoising capability and computational efficiency [ 13 ].…”