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Deep-network based method for joint image deblocking and super-resolution

机译：基于深度网络的联合图像解块和超分辨率方法

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摘要

Many pieces of research have been conducted on image-restoration techniques to recover high-quality images from their low-quality versions, but they usually aim to handle a single degraded factor. However, captured images usually suffer from various degradation factors, such as low resolution and compression distortion, in the procedures of image acquisition, compression, and transmission simultaneously. Ignoring the correlation of different degraded factors may result in the limited efficiency of the existing image-restoration methods for captured images. A joint deep-network-based image-restoration algorithm is proposed to establish a restoration framework for image deblocking and super-resolution. The proposed convolutional neural network is made up of two stages. A deblocking network is constructed with two cascade deblocking subnets first, then, super-resolution is performed by a very deep network with skipping links. Cascading these two stages forms a novel deep network. An end-to-end training scheme is developed, which makes the two stages be trained jointly so as to achieve better performance. Intensive evaluations have been conducted to measure the performance of the authors' method both in general images and face images. Experimental results on several datasets demonstrate that the proposed method outperforms other state-of-the-art methods, in terms of both subjective and objective performances.
机译：关于图像恢复技术已经进行了许多研究，以从低质量版本中恢复高质量图像，但是它们通常旨在处理单个降级因素。然而，在同时进行图像获取，压缩和传输的过程中，捕获的图像通常遭受各种劣化因素，例如低分辨率和压缩失真。忽略不同退化因素的相关性可能会导致现有图像恢复方法对捕获图像的效率有限。提出了一种基于深度网络的联合图像复原算法，以建立图像去块和超分辨率的复原框架。所提出的卷积神经网络由两个阶段组成。首先使用两个级联解块子网构建一个解块网络，然后由具有跳过链接的非常深的网络执行超分辨率。将这两个阶段串联起来，便形成了一个新颖的深度网络。制定了端到端的培训计划，使两个阶段可以一起接受培训，以实现更好的性能。已经进行了广泛的评估，以评估作者的方法在一般图像和面部图像中的性能。在几个数据集上的实验结果表明，在主观和客观表现方面，该方法均优于其他最新方法。

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