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Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network
[chapter]
2018
Lecture Notes in Computer Science
In recent years, deep learning methods have been successfully applied to single-image super-resolution tasks. Despite their great performances, deep learning methods cannot be easily applied to realworld applications due to the requirement of heavy computation. In this paper, we address this issue by proposing an accurate and lightweight deep network for image super-resolution. In detail, we design an architecture that implements a cascading mechanism upon a residual network. We also present
doi:10.1007/978-3-030-01249-6_16
fatcat:4ibhd6um5jahji3czpqsss2apy