# Fully Symmetric Convolutional Network for Effective Image Denoising
## 1 Introduction  
Image denoising is an active topic in low-level vision since it is an indispensable step in many practical applications. The goal of image denoising is to recover a clean image from a noisy observation resulting in minimal damage to the image. In the past few decades, extensive studies have been carried out to develop various image denoising methods. Traditional image restoration methods rely on predefined image priors for image processing. Various models have been exploited for modeling image priors such as Nonlocal Self-Similarity (NSS) models [1; 2; 3; 4], sparse models [5; 6; 7], gradient models [8; 9; 10], and Markov Random Field (MRF) models [11].  
Despite their high denoising quality, most of these methods suffer from significant drawbacks where a complex optimization in the testing stage is required, thus making the denoising process time-consuming. These methods can hardly achieve high performance without sacrificing computational efficiency.  
The above drawbacks are overcome by discriminative learning-based methods. Unlike in the traditional image restoration methods, the parameters of Deep Neural Networks (DNNs) are directly learned from training data (from the pairs of clean and corrupted images) rather than predefined image priors. Stacked Denoising Autoencoder (SDA) is one of the early deep learning models which has been used for image denoising [12]. However, it fails to learn anything useful if it is given too much large data. Sparsely connected Multilayer Perceptron (MLP) method has the advantage of beingcomputationally easier to train and evaluate. However, the number of parameters in MLPs is often too large [13].  
In deep learning, Convolutional Neural Networks (CNNs) are found to give the most accurate results in solving real-world problems. CNNs are widely used in various image-processing problems [14; 15; 16] and have achieved significant success in the field of image restoration compared to MLPs and autoencoders. CNNs with deep architecture are effective in increasing the capacity and flexibility for exploring image characteristics. For training CNNs, considerable advances have been achieved on regularization and learning including Rectified Linear units (ReLu) [17] and batch normalization [18]. By observing the recent superior performance of CNNs on image-processing tasks, we propose a deep fully symmetric convolutional-deconvolutional neural network for image denoising, which is referred to as FSCN hereafter. The proposed model comprises a novel architecture with a chain of successive symmetric convolutional-deconvolutional layers. The framework learns convolutional-deconvolutional mappings from corrupted images to the clean ones in an end-to-end fashion without using image priors. The convolutional layers act as feature extractor to encode primary components of the image contents while eliminating corruptions, and the deconvolutional layers then decode the image abstraction to recover the image content details. Our model achieves very appealing computational efficiency compared to other methods.  
The content of the paper is organized as follows. Section 2 provides a brief survey of related work, Section 3 presents the architecture of the proposed FSCN model, Section 4 presents an extensive discussion of experimental results to evaluate the model, and the summary and prospects of the study are provided in Section 5.