Memory-efficient single-image super-resolution

Nicholas Chiapputo; Colleen P. Bailey

doi:10.1117/12.2619142

31 May 2022 Memory-efficient single-image super-resolution

Proceedings Volume 12097, Big Data IV: Learning, Analytics, and Applications; 120970G (2022) https://doi.org/10.1117/12.2619142
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Modern displays are steadily increasing in resolution, though sensors can be prohibitively expensive to capture images and video at such high resolutions. Image super-resolution, or upsampling, has recently been applied to alleviate these shortcomings. There exist many deep learning image super-resolution models that reconstruct very high quality high-resolution images from a low-resolution base. However, most of these models use a tremendous amount of parameters, requiring a large amount of free memory and computational power to super resolve a single image. As a result, many modern super-resolution models are not entirely practical due to the computational or memory usage requirements. We propose a highly efficient, small super-resolution model utilizing the sub-pixel convolution block for single image super-resolution.

Conference Presentation

Citation Download Citation

Nicholas Chiapputo and Colleen P. Bailey "Memory-efficient single-image super-resolution", Proc. SPIE 12097, Big Data IV: Learning, Analytics, and Applications, 120970G (31 May 2022); https://doi.org/10.1117/12.2619142

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