Lightweight network with one-shot aggregation for image super-resolution

In recent years, convolutional neural network-based methods have achieved remarkable performance for the single-image super-resolution task. However, huge computational complexity and memory consumption of these methods limit their applications on the resource-constrained device. In this paper, we propose a lightweight network named one-shot aggregation network (OAN) to address this problem for image super-resolution. Specifically, to take advantage of diversified features with multiple receptive fields and overcome the inefficiency of dense aggregation which aggregates all previous feature maps to the subsequent layer, we propose an one-shot aggregation block as the cascaded block to adopt one-shot aggregation strategy by aggregating the intermediate features with multiple receptive fields only once in the last feature map. Experimental results on benchmark datasets demonstrate that our proposed OAN outperforms the state-of-the-art SR methods in terms of the reconstruction quality, the number of parameters, and multiply-accumulate operations.