超分辨率重建可以从低分辨率图像序列中重建出高分辨率图像,提高图像质量。重建出边缘保持且噪声低的高分辨率图像,仍具有挑战。针对此问题,在L1先验模型中添加图像梯度的L0范数作为先验知识,提出联合L1和L0先验模型的超分辨率重建算法,既保留L1先验模型边缘保持的优点,又保留L0先验模型抑制噪声的优点。将该算法与双三次插值、Total Variation (TV)先验模型和L1先验模型作对比,通过仿真实验数据和真实实验数据的分析,验证本文算法的有效性。
Super-resolution (SR) reconstruction can reconstruct a high-resolution image from low-resolution image sequences and improve image quality. Reconstructing a high-resolution image with edge preserving and low noise is still a challenge in SR. Therefore, the L0 norm of the image gradient is added as prior knowledge in the L1 prior model, and a SR reconstruction algorithm by combining the L1 and L0 prior model is proposed in this paper, which not only retains the advantage of L1 prior model preserving edges, but also retains the advantage of L0 prior model suppressing noise. Compared with bicubic interpolation, total variation (TV) prior model, and L1 prior model, the validity of the algorithm is verified through the analysis of simulation experimental data and real experimental data.
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