基于L1和L2混合范式的序列图像超分辨率重建

针对超分辨率重建时需要同时滤除高斯噪声和脉冲噪声的问题,提出一种基于L₁和L₂混合范式并结合双边全变分(BTV)正则化的序列图像超分辨率重建方法。首先基于多分辨率策略的光流场模型对序列低分辨率图像进行配准,使图像的配准精度达到亚像素级,进而可以利用图像间的互补信息提高图像分辨率;其次利用L₁和L₂混合范式的优点,用BTV正则化算法解决重建的病态性反问题;最后进行序列图像超分辨率重建。实验数据显示算法可以降低图像均方误差,并将峰值信噪比(PSNR)提高1.2 dB~5.2 dB。实验结果表明,提出的算法能够有效地滤除高斯和脉冲噪声,保持图像边缘,提高图像可辨识度,可为车牌识别、人脸识别和视频监控等方面提供了良好的技术基础。

Sequence images super-resolution reconstruction based on L1 and L2 mixed norm

In order to filter out Gaussian noise and impulse noise at the same time, and get high resolution image in super-resolution reconstruction, a method with L₁ and L₂ mixed norm and Bilateral Total Variation (BTV) regularization was proposed for sequence images super-resolution. Firstly, multi-resolution optical flow model was used to register low-resolution sequence images and the registration precision was up to sub-pixel level, then the complementary information was used to raise image resolution. Secondly, taking advantage of L₁ and L₂ mixed norm, BTV regularization algorithm was used to solve the ill-posed problem. Lastly, the proposed algorithm was used to sequence images super-resolution. Experimental results show that the method can decrease the mean square error and increase Peak Signal-to-Noise Ratio (PSNR) by 1.2 dB to 5.2 dB. The algorithm can smooth Gaussian and impulse noise, protect image edge information and improve image identifiability, which provides good technique basis for license plate recognition, face recognition, video surveillance, etc.