Abstract
Super resolution(SR) with high efficiency and low power consumption is highly demanded in the actual application scenes. In this paper, We designed a super light-weight SR network with strong feature expression. The network we proposed is named RCBSR. Based on the novel technique of re-parameterization, we adopt a block with multiple paths structure in the training stage and merge multiple paths structure into one single 3\(\times \)3 convolution in the inference stage. And then the neural architecture search(NAS) method is adopted to determine amounts of block M and amounts of channel C. Finally, the proposed SR network achieves a fairly good result of PSNR(27.52 dB) with power consumption(0.1 W@30 fps) on the MediaTek Dimensity 9000 platform in the challenge testing stage.
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Acknowledgements
I would like to express my gratitude to all those who helped me during the experiment. My deepest gratitude goes first and foremost to our team for their instructive advice and usefull suggestions. I am deeply gratefull of their help and participation in this competition.
High tribute shall be paid to our leader, who encourages us to participate in the competition and realize our self-worth. Special thanks should go to colleagues from other departments for providing the cloud server for training. Finally, I am indebted to competition organizers for hosting the challenges and providing the opportunity to us.
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Gao, S. et al. (2023). RCBSR: Re-parameterization Convolution Block for Super-Resolution. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13802. Springer, Cham. https://doi.org/10.1007/978-3-031-25063-7_33
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