Mr. Ning Zhao Profile

Ning Zhao

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Proceedings Article | 28 May 2024 Poster + Paper

How down-sampling affects supervised-learning-based image super-resolutions

Ning Zhao, Cong Zhang, Haopeng Zhang, Zhiguo Jiang

Proceedings Volume 13083, 130831O (2024) https://doi.org/10.1117/12.3022989

KEYWORDS: Spatial resolution, Image restoration, Image resolution, Super resolution, Image quality

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Recently, supervised-learning-based Single Image Super-Resolution (SISR) methods have been more and more popular, owing to their breakthroughs in SR performance for High Resolution (HR) and Low Resolution (LR) image pairs. However, LR images used for model training and performance evaluation are usually down-sampled from HR images by the same method. Thus, the evaluation results may not be consistent to a large extent when the down-sampling kernel is different. The motivation of this paper is to evaluate the robustness of supervised-learning-based SISR methods against different down-sampling kernels and analyze the impact of down-sampling to the supervised training and SR performance evaluation. We use six kinds of down-sampling methods to construct LR images from the same HR images, and comprehensively evaluate eleven popular supervised-learning-based SISR methods including dictionary learning and deep learning. Experimental results show that the SR performance of supervised-learning-based methods is the best when the down-sampling methods of the training data and the test data are consistent. We also collect a publicly available Standard Resolution Target (SRT) image dataset to provide a quantitative basis for subjective evaluation on real data. Our insights may facilitate further development and better evaluation of learning-based SISR methods.

Proceedings Article | 12 September 2021 Presentation + Paper

Deep-learning-based remote sensing video super-resolution for Jilin-1 satellite

Ning Zhao, Jiawei Shi, Pengrui Wang, Zhiguo Jiang, Haopeng Zhang

Proceedings Volume 11862, 118620E (2021) https://doi.org/10.1117/12.2599926

KEYWORDS: Optical flow, Super resolution, Video, Optical networks, Remote sensing, Image fusion, Convolution, Satellites, Motion estimation, Visualization

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Due to far imaging distance and relatively harsh imaging conditions, the spatial resolution of remote sensing data are relatively low. Images/videos super-resolution is of great significance to effectively improve the spatial resolution and visual effect of remote sensing data. In this paper, we propose a deep-learning-based video super-resolution method for Jilin-1 remote sensing satellite. We use explicit motion compensation method by calculating the optical flow through the optical flow estimation network and compensating the motion of the image through warp operation. After obtaining the multi-frame images after motion compensation, it is necessary to use multi-frame image fusion for super-resolution reconstruction. We performed super-resolution experiments with scale factor 4 on Jilin-1 video dataset. In order to explore suitable fusion method, we compared two kinds of image fusion methods in the super-resolution network, i.e. concatenation by channel and 3D convolution, without motion compensation. Experimental results show that 3D convolution achieves better super-resolution performance, and video super-resolution result is better than the compared single image super-resolution method. We also performed experiments with motion compensation by optical flow estimation network. Experimental results show that the difference between the image after motion compensation and the reference frame becomes smaller. This indicates that the explicit motion compensation method can compensate the difference between the frames due to the target motion to a certain extent.

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