Sun Jing, Yuan Qiangqiang, Shen Huanfeng, Li Jie, Zhang Liangpei
School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China.
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China.
Sensors (Basel). 2024 Aug 28;24(17):5566. doi: 10.3390/s24175566.
The objective of image super-resolution is to reconstruct a high-resolution (HR) image with the prior knowledge from one or several low-resolution (LR) images. However, in the real world, due to the limited complementary information, the performance of both single-frame and multi-frame super-resolution reconstruction degrades rapidly as the magnification increases. In this paper, we propose a novel two-step image super resolution method concatenating multi-frame super-resolution (MFSR) with single-frame super-resolution (SFSR), to progressively upsample images to the desired resolution. The proposed method consisting of an L0-norm constrained reconstruction scheme and an enhanced residual back-projection network, integrating the flexibility of the variational model-based method and the feature learning capacity of the deep learning-based method. To verify the effectiveness of the proposed algorithm, extensive experiments with both simulated and real world sequences were implemented. The experimental results show that the proposed method yields superior performance in both objective and perceptual quality measurements. The average PSNRs of the cascade model in set5 and set14 are 33.413 dB and 29.658 dB respectively, which are 0.76 dB and 0.621 dB more than the baseline method. In addition, the experiment indicates that this cascade model can be robustly applied to different SFSR and MFSR methods.
图像超分辨率的目标是利用来自一幅或几幅低分辨率（LR）图像的先验知识重建高分辨率（HR）图像。然而，在现实世界中，由于互补信息有限，随着放大倍数的增加，单帧和多帧超分辨率重建的性能都会迅速下降。在本文中，我们提出了一种新颖的两步图像超分辨率方法，将多帧超分辨率（MFSR）与单帧超分辨率（SFSR）相结合，以逐步将图像上采样到所需分辨率。所提出的方法由一个L0范数约束重建方案和一个增强的残差反投影网络组成，融合了基于变分模型方法的灵活性和基于深度学习方法的特征学习能力。为了验证所提算法的有效性，我们对模拟序列和真实世界序列进行了广泛的实验。实验结果表明，所提方法在客观和感知质量测量方面均具有卓越的性能。级联模型在set5和set14中的平均峰值信噪比分别为33.413 dB和29.658 dB，比基线方法分别高出0.76 dB和0.621 dB。此外，实验表明该级联模型可以稳健地应用于不同的SFSR和MFSR方法。
