# Fully Symmetric Convolutional Network for Effective Image Denoising
## 2 Related Works  
Extensive studies have been conducted to develop various image restoration methods. Traditional BM3D [2] algorithm and dictionary-learning-based methods have shown promising performance on image denoising despite relying on predefined image priors [5]. However, image prior knowledge formulated as regularization techniques [8] is effective in removing noise artifacts but tends to oversmooth images. The popular and dominant wavelet-based method introduces ringing artifacts in the denoised image. Application of spatiotemporal filters reduces noise but strong denoising causes blurring [19]. Bilateral filter smooths noisy images but leads to oversmoothing of images while preserving the edges [20]. However, direct implementation of a bilateral filter takes longer for one-megapixel images and does not achieve real-time performance on high-definition content. It also tends to oversmoothing and edge-sharpening. Joint distribution wavelet method was proposed [21] to remove noise from digital images based on a statistical model of the coefficients of an overcomplete multiscale-oriented basis. Nonetheless, this method introduced ringing artifacts and additional edges or structures in the denoised image. [PERSON] et al. [22] prefiltered the noisy image by using the bilateral filter and eigenvectors were approximated by using Nystrom and Sinkhorn approximation to decompose the prefiltered noisy image. However, the process could be very slow and complicated if the eigenvalues are estimated for a full image. Currently, dynamic and promising neural-network-based methods have been explored in image denoising. Neural-network-based methods typically learn parameters directly from training data rather than relying on predefined image priors. Recently, there have been several attempts to handle the denoising problem by DNN. The early DNN-based SDA pretraining minimizes the reconstruction error with respect to input layers one at a time. The network goes through a fine-tuning stage when all layers are pretrained. Nevertheless, the focus turned out to capture the interesting structure for subsequent learning tasks which is different from that of developing a competitive denoising algorithm [12]. [PERSON] et al. [23] used Adaptive Multicolumn Stacked Sparse Denoising Autoencoder (AMC-SSDA) for image denoising. Each denoising autoencoder layer was trained by generating new noise using optimized weights which are determined by features of each given input image. It is effective for images corrupted by multiple types of noises but requires a separate training network to predict the optimal weights. MLPs are capable of dealing with various types of noise. [PERSON] et al. [13] presented a patch-based algorithm learned on a large dataset with a plain MLP which could match with state-of-the-art traditional image denoising methods such as BM3D. However, the drawback of MLP is that every hidden unit is connected to every input pixel and does not assume any spatial relationships between pixels. On the other hand, CNN has achieved more significant success in the field of image restoration compared to autoencoders and MLPs. CNN is used to denoise natural images wherein the network was trained by minimizing the loss between a clean image and its corrupted version produced by adding noise. CNN worked well on blind and nonblind image denoising showing superior performance compared to wavelet and Markov Random Field (MRF). Their framework is the same as that of a recently developed Fully Convolutional Neural Network (FCN) for semantic segmentation [24] and super-resolution [25]. Their network could accept an image as the input and produce an entire image as an output through four hidden layers of convolutional filters. [PERSON] et al. [26] proposed a deep convolutional architecture for natural image denoising to overcome the limitation of fixed image size in deep learning methods.