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.
LI Li
,
YIN Zengshan
,
SHI Shen
. Super-resolution reconstruction algorithm by combining L1 and L0 prior models[J]. Journal of University of Chinese Academy of Sciences, 2022
, 39(3)
: 369
-376
.
DOI: 10.7523/j.ucas.2020.0013
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