Efficient and light-weight super resolution (SR) is highly demanded in practical applications. However, most of the existing studies focusing on reducing the number of model parameters and FLOPs may not necessarily lead to faster running speed on mobile devices. In this work, we propose a re-parameterizable building block, namely Edge-oriented Convolution Block (ECB), for efficient SR design. In the training stage, the ECB extracts features in multiple paths, including a normal 3 x 3 convolution, a channel expanding-and-squeezing convolution, and 1st-order and 2nd-order spatial derivatives from intermediate features. In the inference stage, the multiple operations can be merged into one single 3 3 convolution. ECB can be regarded as a drop-in replacement to improve the performance of normal 3 3 convolution without introducing any additional cost in the inference stage. We then propose an extremely efficient SR network for mobile devices based on ECB, namely ECBSR. Extensive experiments across five benchmark datasets demonstrate the effectiveness and efficiency of ECB and ECBSR. Our ECBSR achieves comparable PSNR/SSIM performance to state-of-the-art light-weight SR models, while it can super resolve images from 270p/540p to 1080p in real-time on commodity mobile devices, e.g., Snapdragon 865 SOC and Dimensity 1000+ SOC. The source code can be found at https://github.com/xindongzhang/ECBSR.
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