Parameter choice is crucial to regularization-based image deblurring. In this paper, a Monte Carlo method is used to approximate the optimal regularization parameter in the sense of Stein’s unbiased risk estimate (SURE) which has been applied to image deblurring. The proposed algorithm is suitable for the exact deblurring functions as well as those of not being expressed analytically. We justif...

We present a novel progressive framework for blind image restoration. Common blind restoration schemes first estimate the blur kernel, then employ non-blind deblurring. However, despite recent progress, the accuracy of PSF estimation is limited. Furthermore, the outcome of non-blind deblurring is highly sensitive to errors in the assumed PSF. Therefore, high quality blind deblurring has remaine...

This paper describes a cascadic image restoration method which at each level applies a two-way alternating denoising and deblurring procedure. Denoising is carried out with a wavelet transform, which also provides an estimate of the noise-level. The latter is used to determine a suitable regularization parameter for the Krylov subspace iterative deblurring method. The cascadic multilevel method...

To improve the visual quality of blur images, deblurring techniques are desired, which also play an important role in character recognition and image understanding. The problem of recovering the clear scene text by exploiting the text field characteristics. A series of text-specific multiscale dictionaries (TMD) and a natural scene dictionary is learned for separately modeling the priors on the...

State-of-the-art blind image deconvolution approaches have difficulties when dealing with text images, since they rely on natural image statistics which do not respect the special properties of text images. On the other hand, previous document image restoring systems and the recently proposed black-andwhite document image deblurring method [1] are limited, and cannot handle large motion blurs a...

Motion deblurring is a challenging problem in computer vision. Most previous blind deblurring approaches usually assume that the Point Spread Function (PSF) is spatially invariant. However, nonuniform motions exist ubiquitously and cannot be handled successfully. In this paper, we present an automatic method for object motion deblurring based on non-uniform motion information from video. betwee...

Image deblurring has matured over the last decade; today, there are a wide range of deblurring algorithms that operate successfully in the wild. Yet, there are many applications — including telephoto and low-light photography — where camera shake produces a blur kernel that is large enough to cripple state-of-the-art deblurring algorithms. This failure can be attributed to the decreasing SNR at...

In single image deblurring, the “coarse-to-fine” scheme, i.e. gradually restoring the sharp image on different resolutions in a pyramid, is very successful in both traditional optimization-based methods and recent neural-networkbased approaches. In this paper, we investigate this strategy and propose a Scale-recurrent Network (SRN-DeblurNet) for this deblurring task. Compared with the many rece...

Image deblurring problems appear frequently in astronomical image analysis. For image deblurring problems, it is reasonable to add a non-negativity constraint because of the physical meaning of the image. Previous research works are mainly full-space methods, i.e., solving a regularized optimization problem in a full space. To solve the problem more efficiently, we propose a subspace method. We...

This paper describes Parallel Spectral Deconvolution (PSD) Java software for image deblurring. A key component of the software, JTransforms, is the first, open source, multithreaded FFT library written in pure Java. Benchmarks show that JTransforms is competitive with current C implementations, including the well-known FFTW package. Image deblurring examples, including performance comparisons w...