EAdderSR: enhanced AdderSR for single image super resolution

Replacing multiplication with addition can effectively reduce the computational complexity. Based on this idea, adder neural networks (AdderNets) are proposed. Thereafter, AdderNets are applied to super-resolution (SR) task to obtain AdderSR, which significantly reduces the energy consumption caused by SR models. However, the weak fitting ability of AdderNets makes AdderSR only applicable to the low-complexity pixel-wise loss, and the performance of the model drops sharply when the high-complexity perceptual loss is used. Enhanced AdderSR (EAdderSR) is proposed to overcome the limitations of AdderSR in SR tasks. Specifically, current adder networks have serious gradient precision loss problem, which affects the training stability. The normalization layer is adjusted to normalize the output of the adder layer to a reasonably narrow range, which can reduce the amount of precision loss. Then, a coarse-grained knowledge distillation (CGKD) method is developed to give adder networks an efficient guidance to reduce the fitting burden. The experimental results show that the proposed method not only further improves the performance of adder networks, but also ensures the quality of the output results when the complexity of the loss function increases.