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.

翻译：更轻量、更快速的图像复原（IR）模型对于在资源受限设备上的部署至关重要。二值神经网络（BNN）作为最具潜力的模型压缩方法之一，能够大幅减少全精度卷积神经网络（CNN）的计算量与参数规模。然而，BNN与全精度CNN存在本质差异，我们难以直接借鉴CNN的设计经验来开发BNN。在本研究中，我们针对图像复原任务重新审视了二值卷积中的关键组件，包括残差连接、批归一化、激活函数及网络结构。通过系统性分析，我们阐释了各组件在二值卷积中的作用机理，并讨论了潜在陷阱。具体而言，我们发现：残差连接能有效降低二值化带来的信息损失；批归一化可解决残差连接与二值卷积之间的值域范围差异；激活函数的位置对BNN性能具有显著影响。基于上述发现与分析，我们设计了一种简单高效的基础二值卷积单元（BBCU）。进一步，我们将图像复原网络划分为四个模块，并针对每个模块专门设计了BBCU变体，以探索各部分二值化的潜在优势。我们在不同图像复原任务上进行了实验，结果表明我们的BBCU显著优于其他BNN及轻量级模型，证明BBCU可作为二值化图像复原网络的基础单元。所有代码与模型将公开发布。