基于改进总广义变分的单幅红外图像超分辨率算法
Single infrared image super-resolution algorithm based on improved total generalized variation
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摘要: 针对传统总广义变分（TGV）算法在红外图像超分辨率重建过程中难以有效抑制噪声的问题，提出了一种基于改进TGV的单幅红外图像超分辨率算法.该算法首先将二阶TGV模型与一阶梯度锐化算子相结合，在算法实现的梯度上升阶段加上一阶梯度锐化算子，在梯度下降阶段的系数中加上一阶梯度锐化算子的系数，得到一种新的红外图像超分辨率正则化模型；然后采用一阶主-对偶优化算法求得高分辨率红外图像.实验结果表明，该算法的主观视觉效果和客观评价指标均优于其他传统算法，可获得质量较高的高分辨率红外图像，能有效抑制噪声，降低硬件实现的复杂度，有较强的实用性.Abstract: Aiming at the problem that the tranditional total generalized variation (TGV) algorithm could not restrain noise effectively in the process of infrared image super-resolution, a single infrared image super-resolution algorithm based on improved TGV was proposed. Firstly, the algorithm was built by second-order TGV regularization model and first-order graduate sharpening operator. First-order graduate sharpening operator was added during the process of gradient ascent, and the factor of first-order graduate sharpening operator was added during the process of gradient descent, so this algorithm acquired a new kind of infrared image super-resolution regularization model. Then it inferred the high-resolution infrared image with a first-order primal-dual optimization scheme. The experimental results showed that the algorithm was superior to other traditional algorithms in terms of subjective visual effect and objective evaluation index, and could obtain high-quality high-resolution infrared images, effectively suppress noise and reduce the complexity of hardware implementation, and had strong practicality.
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