摘要:图像超分辨率一直是底层视觉领域的研究热点，现有基于卷积神经网络的方法直接利用传统网络模型，未对图像超分辨率属于回归问题这一本质进行优化，其网络学习能力较弱，训练时间较长，重建图像的质量仍有提升空间.针对这些问题，提出了基于深度反卷积神经网络的图像超分辨率算法，该算法利用反卷积层对低分辨率图像进行上采样处理，再经深度映射消除由反卷积层造成的噪声和伪影现象，使用残差学习降低网络复杂度，同时避免了因网络过深导致的网络退化问题.在Set 5、Set 14等测试集中，所提算法的PSNR、SSIM、IFC这3项评价指标都优于FSRCNN，重建图像的视觉效果同样验证了该算法出色的性能.



Abstract:Image super resolution is a research hot spot in the field of low level vision. The existing methods based on convolutional neural network do not optimize the image super resolution as a regression problem. These methods are weak in learning ability and require too much time in training step, also leaving room for improvement in the quality of image reconstruction. To solve above mentioned problems, this article proposes a method based on deep deconvolution neural network, which first upsamples low resolution image by deconvolution layer, and then uses deep mapping to eliminate the noise and artifacts caused by deconvolution layer. The residual learning reduces the network complexity and avoids the network degradation caused by the depth network. In Set 5, Set 14 and other datasets, the presented method performs better than FSRCNN in PSNR, SSIM, IFC and visual.



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