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Learning Enriched Features for Image Super Resolution
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DOI：10.1109/ACCESS.2022.3216672.png)
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摘要
En 中文
In recent years, significant progress has been made in image super-resolution (SR) methods based on convolutional neural networks. However, most of them do not fully utilize multi-scale feature correspondence in the image SR process, resulting in blurred and artifact detail restoration, especially for SR tasks with larger scaling factors (i.e. x4 and x 8). A multi-scale feature-enhanced SR network (MI-ENet) is proposed to solve the problems mentioned above. Specifically, the super-resolution method is enhanced by utilizing multi-scale feature correspondence in two ways. On the one hand, we propose an effective multi-scale non-local attention module and it can fully exploit a large number of low-level features at different scales of a single low-resolution (LR) feature map by exhaustively evaluating the correlations in local feature, in-scale non-local feature, and cross-scale non-local feature. On the other hand, the mapping from high-resolution (HR) to LR images is explored by a feedback branch we introduced, that provides an additional constraint for images reconstruction. It can penalize incorrect SR predictions to achieve better SR performance. Experimental results on 5 benchmark datasets show that our method outperforms state-of-theart methods in terms of both subjective visual quality and objective quantitative metrics.
Keyword:
Image super-resolution
multi-scale features
attention mechanism
feedback learning
deep learning
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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