Nonlocal image representation or group sparsity has attracted considerable interest in various low-level vision tasks and has led to several state-of-the-art image denoising techniques, such as BM3D, LSSC. In the past, convex optimization with sparsity-promoting convex regularization was usually regarded as a standard scheme for estimating sparse signals in noise. However, using convex regularization cannot still obtain the correct sparsity solution under some practical problems including image inverse problems. In this paper we propose a non-convex weighted p minimization based group sparse representation (GSR) framework for image denoising. To make the proposed scheme tractable and robust, the generalized soft-thresholding (GST) algorithm is adopted to solve the non-convex p minimization problem. In addition, to improve the accuracy of the nonlocal similar patch selection, an adaptive patch search (APS) scheme is proposed. Experimental results demonstrate that the proposed approach not only outperforms many state-of-the-art denoising methods such as BM3D and WNNM, but also results in a competitive speed.

The InGaN-GaN epitaxial films were grown by low-pressure metal-organic chemical vapor deposition on a sapphire substrate, and then the light-emitting diode ͑LED͒ with double roughened ͑p-GaN and undoped-GaN͒ surfaces was fabricated by surface-roughening, wafer-bonding, and laser lift-off technologies. It was found that the front side luminance intensity of double roughened LED was 2.77 times higher than that of the conventional LED at an injection current of 20 mA. The backside luminance intensity was 2.37 times higher than that of the conventional LED. This is because the double roughened surfaces can provide photons multiple chances to escape from the LED surface, and redirect photons, which were originally emitted out of the escape cone, back into the escape cone.

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