# Reduction of Compression Artifacts Using a Densely Cascading Image Restoration Network
## 2 Related Works  
Due to the advancements in deep learning technologies, research of low-level computer vision, such as super-resolution (SR) and image denoising, has been combined with a variety of CNN architectures to provide higher image restoration than that of conventional image processing. [PERSON] et al. proposed an artifact reduction convolutional neural network (ARCNN) [30], which consists of four convolutional layers and trains an end-to-end mapping from a compressed image to a reconstructed image. After the advent of ARCNN, [PERSON] et al. [31] proposed a residual encoder-decoder network, which conducts encoding and decoding processes with symmetric skip connections in stacking convolutional and deconvolutional layers. [PERSON] et al. [32] proposed a trainable nonlinear reaction diffusion, which is simultaneously learned from training data through a loss-based approach with all parameters, including filters and influence functions. [PERSON] et al. [33] proposed a denoising convolutional neural network (DrCNN), which is composed of a combination of 17 convolutional layers with a rectified linear unit (ReLU) [34] activation function and batch normalization for removing white Gaussian noise. [PERSON] et al. [35] proposed a deep CNN for image compression artifact suppression, which consists of 12 convolutional layers with hierarchical skip connections and a multi-scale loss function. [PERSON] et al. [36] proposed a one-to-many network, which is composed of many stacked residual units, with each branch containing five residual units and the aggregation sub-network comprising 10 residual units. Each residual unit uses batch normalization, ReLU activation function, and convolutional layer twice. The architecture of residual units is found to improve the recovery quality. [PERSON] et al. [37] proposed a very deep persistent memory network with a densely recursive residual architecture-based memory block that adaptively learns the different weights of various memories. [PERSON] et al. [38] proposed a variable-filter-size residual-learning CNN, which contains six convolutional layers and concatenates variable-filter-size convolutional layers. [PERSON] et al. [39] proposed a dual-domain multi-scale CNN with an auto-encoder, dilated convolution, and discrete cosine transform (DCT) unit. [PERSON] et al. [40] designed a multi-level wavelet CNN that builds a u-net architecture with a four-layer fully convolutional network (FCN) without pooling and takes all sub-images as inputs. Each layer of a CNN block is composed of 3 \(\times\) 3 kernel filters, batch normalization, and ReLU. A dual-pixel-wavelet domain deep CNN-based soft decoding network for JPEG-compressed images [41] is composed of two parallel branches, each serving as the pixel domain soft decoding branch and wavelet domain soft decoding branch. [PERSON] et al. [42] proposed a deep convolutional sparse coding (DCSC) network that has dilated convolutions to extract multi-scale features with the same filter for three different scales. The implicit dual-domain convolutional network (IDCN) for robust color image compression AR [43] consists of a feature encoder, correction baseline and feature decoder. [PERSON] et al. [44] proposed a residual dense network (RDN), which consists of 16 residual dense blocks, and each dense block contains eight dense layers with local residual learning. Although most of the aforementioned methods demonstrate better AR performance, they tend to possess more complicated network structures on account of the large number of network parameters needed and heavy memory consumption. Table 1 lists the properties of the various AR networks and compares their advantages and disadvantages. For the network component, a residual network [45] was designed for shortcut connections to simplify identity mapping, and outputs were added to the outputs of the stacked layers. A densely connected convolutional network [46] directly connects all layers with one another based on equivalent feature map sizes.