  Deep convolutional neural networks can enhance images taken with small mobile
camera sensors and excel at tasks like demoisaicing, denoising and
super-resolution. However, for practical use on mobile devices these networks
often require too many FLOPs and reducing the FLOPs of a convolution layer,
also reduces its parameter count. This is problematic in view of the recent
finding that heavily over-parameterized neural networks are often the ones that
generalize best. In this paper we propose to use HyperNetworks to break the
fixed ratio of FLOPs to parameters of standard convolutions. This allows us to
exceed previous state-of-the-art architectures in SSIM and MS-SSIM on the
Zurich RAW- to-DSLR (ZRR) data-set at > 10x reduced FLOP-count. On ZRR we
further observe generalization curves consistent with 'double-descent' behavior
at fixed FLOP-count, in the large image limit. Finally we demonstrate the same
technique can be applied to an existing network (VDN) to reduce its
computational cost while maintaining fidelity on the Smartphone Image Denoising
Dataset (SIDD). Code for key functions is given in the appendix.
