Partial Attention for Data-Driven Image Super Resolution Under Low Computing Cost

Low computation cost is crucial for a seamless experience on mobile consumer devices with limited resources. This study presents an efficient attention module for deep learning-based image super-resolution under low computing cost. We propose partial enhanced spatial attention (PESA) to achieve effi...

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Bibliographic Details
Main Authors: Nugroho, Kuntoro Adi, Windarto, Yudi Eko
Format: Conference Proceeding
Language:English
Subjects:
attention
Attention mechanisms
Computational efficiency
Computational modeling
Consumer electronics
Convolution
Costs
efficient
Feature extraction
Interpolation
partial
super resolution
Superresolution
Online Access:Request full text
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Summary:Low computation cost is crucial for a seamless experience on mobile consumer devices with limited resources. This study presents an efficient attention module for deep learning-based image super-resolution under low computing cost. We propose partial enhanced spatial attention (PESA) to achieve efficient and high-performing attention modules, which draws inspiration from partial convolution for feature extraction. Utilizing an efficient super-resolution network, our approach is assessed on two super-resolution datasets and contrasted with other attention strategies. PESA obtains the lowest computing cost and model parameters, as well as the top quantitative results on both datasets.
ISSN:2693-0854
DOI:10.1109/GCCE62371.2024.10760957