PSF Estimation via Gradient Cepstrum Analysis for Image Deblurring in Hybrid Sensor Network

Abstract

In hybrid sensor networks, information fusion from heterogeneous sensors is important, but quite often information such as image is blurred. Single image deblurring is a highly ill-posed problem and usually regularized by alternating estimating point spread function (PSF) and recovering blur image, which leads to high complexity and low efficiency. In this paper, we first propose an efficient PSF estimation algorithm based on gradient cepstrum analysis (GCA). Then, to verify the accuracy of the strategy, estimated PSFs are used for image deconvolution step, which exploits a novel total variation model coupling with a gradient fidelity term. We also adopt an alternating direction method (ADM) numerical algorithm with rapid convergence and high robustness to optimize the energy function. Both synthetic and real blur experiments show that our scheme can estimate PSF rapidly and produce comparable results without involving long time consuming.

An Adaptive Iterative Algorithm for Motion Deblurring Based on Salient Intensity Prior

“…The blur kernel actually describes the path of the random camera shaking or the movement of objects during the camera exposure process. The cepstrum of the blurred image gradient is neither sensitive to the effect of the random path [2] nor to the random noise which produced in the blurring process. Thus, the cepstrum of the blurred image gradient accurately reflects the information of the blur kernel:…”

“…It is the result of the inverse Fourier transform of the logarithm of the estimated spectrum of a signal. Similar to the autocorrelation map [1], in the cepstrum domain, the two-dimensional convolution is converted into an addition operation, which is helpful to separate the information of the latent image and the blur kernel from a given blurred image [2].…”

“…Impressive progress has been made in estimating clear images in recent years, including both direct [1] [2] and iterative [3] [4][5] [6] blind deblurring methods. While the iterative kernel estimation is easily stuck into the local minima, it may lead to an unsatisfactory kernel and some artifacts in the deblurred result.…”

“…The performance of the latent image is evaluated by gradient cepstrum analysis. Similar to an autocorrelation map [1], the cepstrum converts the two-dimensional convolution into an addition operation, which is helpful to separate the information of the latent image and the blur kernel from a given blurred image [2]. The adaptive iteration strategy effectively restrains the negative influence of overabundant iterations, especially in deblurring images with complex motion blurred kernels.…”

Blur parameter locus curve and its applications

“…Deshpande and Patnaik [35] presented a uniform motion blur PSF estimation by estimating the underlying parameters of the PSF using radon transform. Similarly, Shi and Liu [36] proposed PSF estimation algorithm based on gradient cepstrum analysis and Mosleh et al [37] estimated PSF using frequency spectrum. Dong and Ma [38] proposed a novel variational method for single image blind deblurring based on the fractional‐order differential to overcome the staircase effect produced by the total variation regularisation.…”