Abstract
This paper presents an improved method to estimate the blur parameters of motion deblurring algorithm for single image restoration based on the point spread function (PSF) in frequency spectrum. We then introduce a modification to the Radon transform in the blur angle estimation scheme with our proposed difference value vs angle curve. Subsequently, the auto-correlation matrix is employed to estimate the blur angle by measuring the distance between the conjugated-correlated troughs. Finally, we evaluate the accuracy, robustness and time efficiency of our proposed method with the existing algorithms on the public benchmarks and the natural real motion blurred images. The experimental results demonstrate that the proposed PSF estimation scheme not only could obtain a higher accuracy for the blur angle and blur length, but also demonstrate stronger robustness and higher time efficiency under different circumstances.
Highlights
Motion blurring is generated inevitably by camera shake during exposure time
The first kinds of methods rely on multi-frame images, and they own a complex network structure, so they are time-consuming [7, 9, 10]; the second types of these approaches only use single image to deblur the degraded image, this kind of method is simple in network structure and fast in training [4, 5, 11], but they still have some shortcomings: Aizenberg developed a multi-layer neural network (MLMVN) [4] to conduct blur
Assuming that the scene objects move uniformly relative to the camera, we can deduce that the gray values of any points in the blurred image are related to the gray values of their corresponding adjacent points in the original image, and the point spread function (PSF) for motion blurring can be expressed as [12]:
Summary
Motion blurring is generated inevitably by camera shake during exposure time. As one of the main causes of image degradation, it seriously affects the performances of computer vision system in various fields. Oliveira and Figueiredo et al proposed a spectrum-based method to estimate the parameters of two types of blurs (linear-uniform and out-of-focus motions) for blind image restoration [29]. Wang et al introduced an improved PSF parameters estimation algorithm which combined bilateral-piecewise estimation strategy and the sub-pixel level image generated with bilinear interpolation in different noisy situations [12] It is too hard for this algorithm to explore effective solution under non-linear and non-uniform motion blur. The existing algorithms still can not achieve a satisfactory balance between precision, robustness and time efficiency To address these limitations in single blind image restoration, we propose a novel Radontransform-based blur angle estimation scheme which is inspired by the dark and bright stripes. In addition to the above, our proposed method can deal with the image of arbitrary sizes of row and column, since the method proposed in [12] can only handle the square image
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