In this paper, a joint restoration convolutional neural network (JRCNN) is proposed to produce a visually pleasing super resolution (SR) image from a single low-quality (LQ) image. The LQ image is a low resolution (LR) image with ringing, blocking and blurring artifacts arising due to compression. JRCNN consists of three deep dense residual blocks (DRB). Each DRB comprises of parallel convolutional layers with cross residual connections. The representational power of JRCNN is improved by depth-wise concatenation of feature representations from each of the DRBs. Moreover, these connections mitigate the problem of vanishing of gradients. Different from the previous networks, JRCNN exploits the contextual information directly in the LR image space without using any interpolation. This strategy improves the training efficiency of the network. The exhaustive experimentation on different datasets show that the proposed JRCNN produces state-of-the-art performance. Furthermore, ablation experiments are performed to assess the effectiveness of JRCNN. In addition, individual experiments are conducted for SR and compression artifact removal on benchmark datasets.

中文翻译：

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低质量图像超分辨率的联合恢复卷积神经网络

在本文中，提出了一种联合恢复卷积神经网络 (JRCNN)，以从单个低质量 (LQ) 图像生成视觉上令人愉悦的超分辨率 (SR) 图像。LQ 图像是低分辨率 (LR) 图像，具有因压缩而产生的振铃、块状和模糊伪影。JRCNN 由三个深度密集残差块 (DRB) 组成。每个 DRB 由具有交叉残差连接的并行卷积层组成。通过将每个 DRB 的特征表示进行深度级联，提高了 JRCNN 的表示能力。此外，这些连接减轻了梯度消失的问题。与之前的网络不同，JRCNN 直接在 LR 图像空间中利用上下文信息，而不使用任何插值。这种策略提高了网络的训练效率。对不同数据集的详尽实验表明，所提出的 JRCNN 产生了最先进的性能。此外，还进行了消融实验以评估 JRCNN 的有效性。此外，还针对基准数据集上的 SR 和压缩伪影去除进行了单独的实验。