In this paper, we propose an efficient single image super-resolution (SR) method for multi-scale image texture recovery, based on Deep Skip Connection and Multi-Deconvolution Network. Our proposed method focuses on enhancing the expression capability of the convolutional neural network, so as to significantly improve the accuracy of the reconstructed higher-resolution texture details in images. The use of deep skip connection (DSC) can make full use of low-level information with the rich deep features. The multi-deconvolution layers (MDL) introduced can decrease the feature dimension, so this can reduce the computation required, caused by deepening the number of layers. All these features can reconstruct high-quality SR images. Experiment results show that our proposed method achieves state-of-the- art performance.
KEYWORDS: Anisotropic diffusion, Gaussian filters, Smoothing, Image filtering, Diffusion, Linear filtering, Signal to noise ratio, Digital filtering, Denoising, Performance modeling
In each step of anisotropic diffusion smoothing, noises must be managed to get better results. The mostly used method is
Gaussian filtering. However, the standard deviation of the Gaussian filter can't be accurately obtained and it should
change during the iterative process. Another problem is how to select a proper standard deviation to reducing noises
while preserving edges. Actually, facet model fitting can be taken as a natural way to overcome the drawbacks
mentioned above. Facet model fitting has the low-pass filtering performance adaptive to the image during evolution of
diffusion; it can also achieve balanced results for noise reduction and edge preserving. Experiments show the method can
preserve more edges as well as obtain higher peak signal-to-noise ratio as compared to other anisotropic diffusion based
selective smoothing approaches.
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