Abstract
Convolutional Neural Networks (CNNs) have demonstrated great results for the single-image super-resolution (SISR) problem. Currently, most CNN algorithms promote deep and computationally expensive models to solve SISR. However, we propose a novel SISR method that uses relatively less number of computations. On training, we get group convolutions that have unused connections removed. We have refined this system specifically for the task at hand by removing unnecessary modules from original CondenseNet. Further, a reconstruction network consisting of deconvolutional layers has been used in order to upscale to high resolution. All these steps significantly reduce the number of computations required at testing time. Along with this, bicubic upsampled input is added to the network output for easier learning. Our model is named SRCondenseNet. We evaluate the method using various benchmark datasets and show that it performs favourably against the state-of-the-art methods in terms of both accuracy and number of computations required.
V. Jain and P. Bansal—Authors contributed equally.
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Acknowledgements
The authors are grateful to HP Inc. for their support to the Innovations Incubator Program. They are thankful to other stakeholders of this program including Leadership, and Faculty Mentors at IIT-BHU, Drstikona and Nalanda Foundation. Authors are also grateful to Dr. Prasenjit Banerjee, Nalanda Foundation for his mentoring and support
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Jain, V., Bansal, P., Singh, A.K., Srivastava, R. (2018). Efficient Single Image Super Resolution Using Enhanced Learned Group Convolutions. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11306. Springer, Cham. https://doi.org/10.1007/978-3-030-04224-0_40
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