An improved compression algorithm based on IDN model of image super-resolution reconstruction

Zemin Xu; Jian Xu; Bing Song; Zhengguang Xie

doi:10.1117/12.2639111

15 July 2022 An improved compression algorithm based on IDN model of image super-resolution reconstruction

Zemin Xu, Jian Xu, Bing Song, Zhengguang Xie

Proceedings Volume 12258, International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022); 122581U (2022) https://doi.org/10.1117/12.2639111
Event: International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022), 2022, Qingdao, China

Abstract

At present, the convolutional neural network is deepening in level and has a huge amount of computation, so it is difficult to realize application in scenarios with low computing capacity. Therefore, this paper proposes a method based on channel pruning and weight quantization to reduce the amount of computation and compress the image super-resolution to reconstruct the network model IDN. Experimental results show that the proposed method effectively compresses the model structure, greatly shortens the calculation time of the model and makes the model more lightweight under the premise that the performance indexes are basically unchanged.

Citation Download Citation

Zemin Xu, Jian Xu, Bing Song, and Zhengguang Xie "An improved compression algorithm based on IDN model of image super-resolution reconstruction", Proc. SPIE 12258, International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022), 122581U (15 July 2022); https://doi.org/10.1117/12.2639111

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