Abstract
In this paper, we propose a novel local and nonlocal total variation combination method for image restoration in wireless sensor networks (WSN), which plays an important role in improving the quality of the transmitted image. First, the degrade image is preprocessed by an image smoothing scheme to divide the image into two regions. One contains edges and flat regions by the local TV term. The other is rich in image details and regularized by the nonlocal TV term. Then, the alternating direction method of multipliers (ADMM) algorithm is adopted to optimize the complex object function, and two key parameters are discussed for better performance. Finally, we compare our method with several recent state-of-the-art methods and illustrate the efficiency and performance of the proposed model by experimental results in peak signal to noise ratio (PSNR) and computing time.
Highlights
With the rapid development of wireless sensor networks, there are higher requirements for signal transmission and processing [1,2,3,4]
The peak signal to noise ratio (PSNR) is used to evaluate the quality of recovery results
All experiments are conducted by Matlab programming on a desktop PC with 2.3GHz Interl Core computer and 4.0 GB memory
Summary
With the rapid development of wireless sensor networks, there are higher requirements for signal transmission and processing [1,2,3,4]. For such a twodimensional image signal, it is inevitably degraded in the process of image acquisition, transmission and processing, and image restoration techniques are needed to improve the quality of the obtained image. We focus on spatially invariant system and formulate a common degradation model as g 1⁄4 hÃfþn; ð1Þ the additive Gaussian white noise with zero mean. We only focus on the nonblind image restoration
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