BASIC BINARY CONVOLUTION UNIT FOR BINARIZED IMAGE RESTORATION NETWORK

Abstract

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, Batch-Norm, 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.

1. INTRODUCTION

Image restoration (IR) aims to restore a high-quality (HQ) image from its low-quality (LQ) counterpart corrupted by various degradation factors. Typical IR tasks include image denoising, superresolution (SR), and compression artifacts reduction. Due to its ill-posed nature and high practical values, image restoration is an active yet challenging research topic in computer vision. Recently, the deep convolutional neural network (CNN) has achieved excellent performance by learning a mapping from LQ to HQ image patches for image restoration (Chen & Pock, 2016; Zhang et al., 2018a; Tai et al., 2017; Xia et al., 2023) . However, most IR tasks require dense pixel prediction and the powerful performance of CNN-based models usually relies on increasing model size and computational complexity. That requires extensive computing and memory resources. While, most hand-held devices and small drones are not equipped with GPUs and enough memory to store and run the computationally expensive CNN models. Thus, it is quite essential to largely reduce its computation and memory cost while preserving model performance to promote IR models. Binary neural network (Courbariaux et al., 2016) (BNN, also known as 1-bit CNN) has been recognized as one of the most promising neural network compression methods (He et al., 2017; Jacob et al., 2018; Zoph & Le, 2016) for deploying models onto resource-limited devices. BNN could achieve 32× memory compression ratio and up to 64× computational reductions on specially designed processors (Rastegari et al., 2016) . Nowadays, the researches of BNN mainly concentrate on high-level tasks, especially classification (Liu et al., 2018; 2020) , but do not fully explored in lowlevel vision, like image denoising. Considering the great significance of BNN for the deployment of IR deep networks and the difference between high-level and low-level vision tasks, there is an

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