Abstract
An important practical issue in building Convolutional Neural Network (CNN) is a trade-off between the number of parameters and the performance. This paper proposes multiscale fusion convolutional neural network for single image superresolution. The network has the following two advantages: 1) the multi-scale convolutional layer provides the multi-context for image reconstruction; and 2) the fusion of cross-channel features reduces the dimensionality of the output of the intermediate layer. Thus the experimental results on image super-resolution demonstrate that our network achieves better performance over the state-of-art approaches.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
