28 September 2016 Super-resolution reconstruction algorithm based on adaptive convolution kernel size selection
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Abstract
Restricted by the detector technology and optical diffraction limit, the spatial resolution of infrared imaging system is difficult to achieve significant improvement. Super-Resolution (SR) reconstruction algorithm is an effective way to solve this problem. Among them, the SR algorithm based on multichannel blind deconvolution (MBD) estimates the convolution kernel only by low resolution observation images, according to the appropriate regularization constraints introduced by a priori assumption, to realize the high resolution image restoration. The algorithm has been shown effective when each channel is prime. In this paper, we use the significant edges to estimate the convolution kernel and introduce an adaptive convolution kernel size selection mechanism, according to the uncertainty of the convolution kernel size in MBD processing. To reduce the interference of noise, we amend the convolution kernel in an iterative process, and finally restore a clear image. Experimental results show that the algorithm can meet the convergence requirement of the convolution kernel estimation.
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Hang Gao, Hang Gao, Qian Chen, Qian Chen, Xiubao Sui, Xiubao Sui, Junjie Zeng, Junjie Zeng, Yao Zhao, Yao Zhao, "Super-resolution reconstruction algorithm based on adaptive convolution kernel size selection", Proc. SPIE 9971, Applications of Digital Image Processing XXXIX, 997120 (28 September 2016); doi: 10.1117/12.2235735; https://doi.org/10.1117/12.2235735
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