Multi-Attention Based Ultra Lightweight Image Super-Resolution

• URL: http://arxiv.org/abs/2008.12912v2
• Date: Mon, 21 Sep 2020 06:07:14 GMT
• Title: Multi-Attention Based Ultra Lightweight Image Super-Resolution
• Authors: Abdul Muqeet, Jiwon Hwang, Subin Yang, Jung Heum Kang, Yongwoo Kim, Sung-Ho Bae
• Abstract summary: We propose a Multi-Attentive Feature Fusion Super-Resolution Network (MAFFSRN) MAFFSRN consists of proposed feature fusion groups (FFGs) that serve as a feature extraction block. We participated in AIM 2020 efficient SR challenge with our MAFFSRN model and won 1st, 3rd, and 4th places in memory usage, floating-point operations (FLOPs) and number of parameters, respectively.
• Score: 9.819866781885446
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Lightweight image super-resolution (SR) networks have the utmost significance for real-world applications. There are several deep learning based SR methods with remarkable performance, but their memory and computational cost are hindrances in practical usage. To tackle this problem, we propose a Multi-Attentive Feature Fusion Super-Resolution Network (MAFFSRN). MAFFSRN consists of proposed feature fusion groups (FFGs) that serve as a feature extraction block. Each FFG contains a stack of proposed multi-attention blocks (MAB) that are combined in a novel feature fusion structure. Further, the MAB with a cost-efficient attention mechanism (CEA) helps us to refine and extract the features using multiple attention mechanisms. The comprehensive experiments show the superiority of our model over the existing state-of-the-art. We participated in AIM 2020 efficient SR challenge with our MAFFSRN model and won 1st, 3rd, and 4th places in memory usage, floating-point operations (FLOPs) and number of parameters, respectively.

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