Lightweight NAS Super-Resolution model

Abstract This paper proposes a lightweight image super-resolution reconstruction algorithm (Lightweight NAS Super-Resolution model, LNSR) based on neural network architecture search. The search space of the algorithm is divided into Cell level and Network level. The design of the Cell-level search space is based on the RFDB structure, focusing on searching for combinations of lightweight operators, aiming to build a more lightweight and efficient structure. The Network-level search space focuses on searching the feature connections between Cells, aiming to find the information flow that is most beneficial to improve performance. In order to reduce the time of network architecture search, this paper extends the search strategy of MiLeNAS. At the same time, this paper uses a new type of loss function, which comprehensively considers image distortion, high-frequency detail reconstruction, and model size to promote the model to search for a lightweight and high-performance network structure. The experimental results show that LNSR only needs to spend about 2 days on a 2080 Ti to search for the optimal network structure, and the hardware resources and time consumed are much less than that of FALSR-B. In terms of network scale and performance, LNSR surpasses the artificial design and NAS-based SOTA lightweight method with a lower amount of parameters and calculation.