An adaptive two phase blind image deconvolution algorithm for an iterative regularization model |
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Affiliation: | 1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China;2. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;3. College of Optical Science and Engineering, Zhejiang University, Hangzhou 310007, China;1. Department of Electrical Engineering, Indian Institute of Technology Patna, Bihta 801 106, Patna, India;2. Department of Physics, Indian Institute of Technology Patna, Bihta 801 106, Patna, India |
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Abstract: | This paper proposes a blind image deconvolution method which consists of two sequential phases, i.e., blur kernel estimation and image restoration. In the first phase, we adopt the L0-norm of image gradients and total variation (TV) to regularize the latent image and blur kernel, respectively. Then we design an alternating optimization algorithm which jointly incorporates the estimation of intermediately restored image, blur kernel and regularization parameters into account. In the second phase, we propose to take the mixture of L0-norm of image gradients and TV to regularize the latent image, and design an efficient non-blind deconvolution algorithm to achieve the restored image. Experimental results on both a benchmark image dataset and real-world blurred images show that the proposed method can effectively restore image details while suppress noise and ringing artifacts, the result is of high quality which is competitive with some state of the art methods. |
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Keywords: | Blind image deconvolution TV regularization |
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