AdderSR: Towards Energy Efficient Image Super-Resolution

Understand

This paper studies the single image super-resolution problem using adder neural networks (AdderNet).

• Compared with convolutional neural networks, AdderNet utilizing additions to calculate the output features thus avoid massive energy consumptions of conventional multiplications.
• However, it is very hard to directly inherit the existing success of AdderNet on large-scale image classification to the image super-resolution task due to the different calculation paradigm.
• Specifically, the adder operation cannot easily learn the identity mapping, which is essential for image processing tasks.