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Fast Image Super-Resolution Based on Limit Gradient Embedding Cascaded Forest

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Abstract

At present, the deep learning super-resolution (SR) method has achieved excellent results, but it also faces problems such as large models, high computational cost, a large amounts of training data, and poor interpretability. However, traditional machine learning-based methods still have room for improvement in feature extraction and model structure. This paper constructs a gradient embedding cascade forest structure on the basis of random forest and proposes a limit gradient embedding cascaded forest SR (LGECFSR) model. In feature construction, we not only adopt the first-order gradient, the second-order gradient, and other features of the image but also fuse the information of the original LR image. In addition, image blocks of different sizes are used for training, which increases the model’s generalization ability. Compared with the state-of-the-art machine learning-based methods, our method achieves the best performance and the second-best computational speed. In addition, compared with some deep learning-based methods, our model has a similar reconstruction effect and the best computational speed. In detail, for some reconstruction tasks, the Multi-Adds of LGECFSR is one-tenth to one-4000th of that of some current models. However, the SR performance of LGECFSR is the same or slightly better than that of some current classical algorithms.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (61573182, 62073164), and by the Fundamental Research Funds for the Central Universities (NS2020025).

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Authors and Affiliations

  1. School of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, People’s Republic of China

    Xin Yang, Chenhuan Wu, Dake Zhou & Tao Li

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  1. Xin Yang
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  2. Chenhuan Wu
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  4. Tao Li
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Correspondence to Xin Yang.

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Yang, X., Wu, C., Zhou, D. et al. Fast Image Super-Resolution Based on Limit Gradient Embedding Cascaded Forest. Circuits Syst Signal Process 41, 2007–2026 (2022). https://doi.org/10.1007/s00034-021-01869-5

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