This paper presents a deblurring method that effectively restores fine textures and details, such as a tree's leaves or regular patterns, and suppresses noises in flat regions using consecutively captured blurry and noisy images.
More recently, Zhuo et al. [5] proposed a flash deblurring method that recovers a sharp image by combining a blurry image and a corresponding flash image.
By means of this extra degree of efficiency, for a given accuracy, we have been able to include these SV-filtering techniques as part of an iterative non-linear deblurring method which is much more powerful than classical linear approaches, in a similar computation time.
With the properly chosen Lipschitz matrix, each variable has it own Lipschitz constant, rather than the largest one adopted in APG algorithm, so that the GAPG-based deblurring method has a faster convergence rate.
Compared to non-uniform deblurring method, GAC has a much lower peak memory usage than [16, 30, 33] and is much more efficient than the state-of-the-art camera shake removal methods [16, 29, 30, 32, 33].
In their paper, "An adaptively accelerated Bayesian deblurring method with entropy prior," M. K. Singh et al. propose a method for image deblurring that uses a multiplicative correction term and has been calculated using an exponent on the correction factor.
Proper deblurring method is challenging because the blur kernel is difficult to estimate.
For the deblurring method in the last step of the proposed SR algorithm, we used the classical but effective Wiener filter approach with a constant noise-to-signal ratio (NSR) to reduce the computation complexity.
Otherwise, roll rotations will cause the non-uniform blur, where slice GAC can play like non-uniform deblurring method.
Compared with non-uniform deblurring methods, GAC method has a much lower peak memory usage and is much more efficient.
