Super-resolution reconstruction algorithm by combining L1 and L0 prior models

doi:10.7523/j.ucas.2020.0013

Abstract

Abstract: 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.

Key words: super-resolution reconstruction, L₁ prior model, L₀ prior model, noise suppression, bicubic interpolation

CLC Number:

TN911.73

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.

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Super-resolution reconstruction algorithm by combining L₁ and L₀ prior models

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