Abstract
In order to further improve the reconstruction effect of the image super resolution algorithm, this paper proposes an image super resolution algorithm combining deep learning and wavelet transform (ISRDW). In terms of network design, it is not only simple in structure, but also more effective in capturing image details compared with other neural network structures. At the same time, cross-connection and residual learning methods are used to reduce the difficulty of the training model. In terms of loss function, this paper uses the loss generated in the original image space domain and the wavelet domain to strengthen the constraint of network training. Experimental results show that the algorithm proposed in this paper achieves better results under different data sets and different evaluation indexes.
Highlights
In the field of image processing, in order to obtain higher resolution images, the super-resolution method is usually used to reconstruct the details of low-resolution images
The main contributions are as follows: (1) We propose a super resolution algorithm for ‘‘deep image’’ in wavelet domain to improve the detail of image reconstruction
A new deep image super resolution algorithm based on wavelet domain is proposed, which combines the idea of wavelet transform with the deep residual network, and combines the image space loss and wavelet coefficient loss to strengthen the constraint of network training
Summary
In the field of image processing, in order to obtain higher resolution images, the super-resolution method is usually used to reconstruct the details of low-resolution images. Single Image Super-Resolution focuses on how to reconstruct the local details of a highresolution image from a low-resolution image This problem has developed into an important research direction in the field of image processing [1], [35]. DWSR [20] converts reconstructed HR images into inferring a series of relevant wavelet coefficients of HR images by means of wavelet transform This method achieves simpler computation and faster speed with the same precision as the current optimal very deep super-resolution network (VDSR) algorithm. (2) Different from the previous algorithms, the method in this paper uses a multi-step strategy, that is, the wavelet coefficients corresponding to the high-resolution images are firstly inferred, and the image is reconstructed.
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