  Lighter and faster image restoration (IR) models are crucial for the
deployment on resource-limited devices. Binary neural network (BNN), one of the
most promising model compression methods, can dramatically reduce the
computations and parameters of full-precision convolutional neural networks
(CNN). However, there are different properties between BNN and full-precision
CNN, and we can hardly use the experience of designing CNN to develop BNN. In
this study, we reconsider components in binary convolution, such as residual
connection, BatchNorm, activation function, and structure, for IR tasks. We
conduct systematic analyses to explain each component's role in binary
convolution and discuss the pitfalls. Specifically, we find that residual
connection can reduce the information loss caused by binarization; BatchNorm
can solve the value range gap between residual connection and binary
convolution; The position of the activation function dramatically affects the
performance of BNN. Based on our findings and analyses, we design a simple yet
efficient basic binary convolution unit (BBCU). Furthermore, we divide IR
networks into four parts and specially design variants of BBCU for each part to
explore the benefit of binarizing these parts. We conduct experiments on
different IR tasks, and our BBCU significantly outperforms other BNNs and
lightweight models, which shows that BBCU can serve as a basic unit for
binarized IR networks. All codes and models will be released.
