A Deep Convolutional Neural Network with Selection Units for Super-Resolution

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Hierarchical Neural Architecture Search for Single Image Super-Resolution (2020, methodology): “In this experiment, we compare our method with several hand-crafted SR models, including the Bicubic, SRCNN [7], VDSR [27], DRCN [28], DRRN [29], SelNet [30], CARN [31].”
Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search (2019, neutral): “9188 SelNet [15] 225.”
Lightweight Image Super-Resolution with Adaptive Weighted Learning Network (2019, methodology): “We compared the proposed AWSRN with many lightweight and efficient SR methods on ×2, ×3, ×4, ×8 scales, including SRCNN [1], FSRCNN [3], VDSR [4], DRCN [5], LapSRN [34], DRRN [11], SelNet [35], MemNet [36], CARN [9], MoreMNAS-A [13], FALSR [14], SRMDNF [37], MSRN [38].”
Fast, Accurate, and, Lightweight Super-Resolution with Cascading Residual Network (2018, result): “Moreover, our models are most efficient in terms of the computation cost: CARN shows second best results with 90.9G Mult-Adds, which is on par with SelNet [4].”
NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results (2017, unknown): “8 and [6]).”