In many practical situations, images are not only down sampled but also compressed for efficient transmission and storage. JPEG and MPEG-2 compressions often introduce blocking artifacts because they process the data as 8 × 8 blocks. Many of the existing super resolution (SR) methods assume low resolution images as a down sampled version of high resolution (HR) image, and neglect the degradation due to compression. This exacerbates artifacts in the SR image and reduces the user experience. To address the joint deblocking and SR (DbSR), a novel deep network with dense skip connections and dilated convolutions is proposed in this paper, and we name it as DenseDbSR. Recently, many researchers have proposed deeper networks and achieved improvement in the SR performance. However, training deeper networks is very challenging because of the problem of vanishing gradients. Simply increasing the depth of the network leads to cumbersome computational costs. To enlarge the field-of-view (FOV) without increasing the computational cost, the dilated convolution is used. The dilated convolution exponentially expands the FOV and helps to exploit the contextual information efficiently. Moreover, the dense skip connections create short paths for gradients to be back-propagated efficiently and alleviates the problem of vanishing gradients. Furthermore, the network is relieved from the training burden by learning residuals of the SR image instead of learning raw images. From the conducted extensive experimentation, the proposed DenseDbSR network produced better performance in terms of PSNR and SSIM than the compared state-of-the-art methods.

在许多实际情况下，不仅要对图像进行下采样，还要对其进行压缩以进行有效的传输和存储。JPEG和MPEG-2压缩通常会引入块效应，因为它们将数据作为8×8块进行处理。许多现有的超分辨率（SR）方法都将低分辨率图像假定为高分辨率（HR）图像的降采样版本，而忽略了由于压缩引起的降级。这加剧了SR图像中的伪像，并降低了用户体验。为了解决联合解块和SR（DbSR），本文提出了一种具有密集跳跃连接和扩张卷积的新型深度网络，我们将其命名为DenseDbSR。最近，许多研究人员提出了更深的网络，并实现了SR性能的提高。然而，由于梯度消失的问题，训练更深层的网络非常具有挑战性。简单地增加网络深度会导致繁琐的计算成本。为了在不增加计算成本的情况下扩大视野（FOV），使用了膨胀卷积。膨胀的卷积以指数形式扩展了FOV，并有助于有效地利用上下文信息。此外，密集的跳过连接为梯度提供了有效的反向传播的短路径，并减轻了梯度消失的问题。此外，通过学习SR图像的残差而不是学习原始图像，减轻了网络的训练负担。经过广泛的实验，