Application of convolutional neural networks in image super-resolution
It provides a review that may help researchers and practitioners in computer vision by summarizing existing methods, but it is incremental as it does not introduce new techniques.
This paper tackles the lack of comprehensive summaries on convolutional neural network (CNN) methods for image super-resolution by analyzing and comparing different CNN-based interpolation techniques, such as bicubic and transposed convolution, through experiments to highlight their differences and relations.
Due to strong learning abilities of convolutional neural networks (CNNs), they have become mainstream methods for image super-resolution. However, there are big differences of different deep learning methods with different types. There is little literature to summarize relations and differences of different methods in image super-resolution. Thus, summarizing these literatures are important, according to loading capacity and execution speed of devices. This paper first introduces principles of CNNs in image super-resolution, then introduces CNNs based bicubic interpolation, nearest neighbor interpolation, bilinear interpolation, transposed convolution, sub-pixel layer, meta up-sampling for image super-resolution to analyze differences and relations of different CNNs based interpolations and modules, and compare performance of these methods by experiments. Finally, this paper gives potential research points and drawbacks and summarizes the whole paper, which can facilitate developments of CNNs in image super-resolution.