Effective Alternating Direction Optimization Methods for Sparsity-Constrained Blind Image Deblurring

Abstract

Single-image blind deblurring for imaging sensors in the Internet of Things (IoT) is a challenging ill-conditioned inverse problem, which requires regularization techniques to stabilize the image restoration process. The purpose is to recover the underlying blur kernel and latent sharp image from only one blurred image. Under many degraded imaging conditions, the blur kernel could be considered not only spatially sparse, but also piecewise smooth with the support of a continuous curve. By taking advantage of the hybrid sparse properties of the blur kernel, a hybrid regularization method is proposed in this paper to robustly and accurately estimate the blur kernel. The effectiveness of the proposed blur kernel estimation method is enhanced by incorporating both the L1-norm of kernel intensity and the squared L2-norm of the intensity derivative. Once the accurate estimation of the blur kernel is obtained, the original blind deblurring can be simplified to the direct deconvolution of blurred images. To guarantee robust non-blind deconvolution, a variational image restoration model is presented based on the L1-norm data-fidelity term and the total generalized variation (TGV) regularizer of second-order. All non-smooth optimization problems related to blur kernel estimation and non-blind deconvolution are effectively handled by using the alternating direction method of multipliers (ADMM)-based numerical methods. Comprehensive experiments on both synthetic and realistic datasets have been implemented to compare the proposed method with several state-of-the-art methods. The experimental comparisons have illustrated the satisfactory imaging performance of the proposed method in terms of quantitative and qualitative evaluations.

Efficient blind deblurring under high noise levels

“…where Ω is a rectangular domain covering the support of k, and γ and α are regularization parameters. The regularizers k 1 and ∇k 2 2 were motivated in Xiong [24] for their effectiveness for kernel esti-mation and the spatial constraints were studied in Almeida et al [25]. To highlight to importance of each constraint and prior, we evaluate their contribution by successively removing them and running the full blind kernel estimation method for two noise levels (σ = 2% and σ = 10%).…”

ENCP: a new Energy-efficient Nonlinear Coverage Control Protocol in mobile sensor networks

“…We mainly deal with the first question here while the second one is left for the next section. The calculation of node redundancy degree based on location information employs the geometry knowledge and offers the accurate coverage relationship between nodes [44][45][46]. However, when the location information is not available, it is hard for the nodes to derive the node redundancy degree.…”

An algorithm to optimize deployment of charging base stations for WRSN

“…The massive Internet of things (IoTs) [1] which provide big data for cloud computing [2][3][4][5][6] and traditional networks [7] have been applied to many fields in reality. As an important part of the IoT [8,9], the wireless sensor network (WSN) [10][11][12] has the capabilities of data collection [13][14][15], data storage, and wireless communication [16,17]. WSN plays a significant role in the construction of smart cities [18,19], smart agriculture [20], and public utility monitoring [21], etc.…”