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arXiv 2002.110792020-02-25cs.CVcs.MMeess.IV

DDet: Dual-path Dynamic Enhancement Network for Real-World Image Super-Resolution

Yukai Shi, Haoyu Zhong, Zhijing Yang, Xiaojun Yang, Liang Lin

备注：: Code address: https://github.com/ykshi/DDet
DOI：: 10.1109/LSP.2020.2978410

英文摘要：

Different from traditional image super-resolution task, real image super-resolution(Real-SR) focus on the relationship between real-world high-resolution(HR) and low-resolution(LR) image. Most of the traditional image SR obtains the LR sample by applying a fixed down-sampling operator. Real-SR obtains the LR and HR image pair by incorporating different quality optical sensors. Generally, Real-SR has more challenges as well as broader application scenarios. Previous image SR methods fail to exhibit similar performance on Real-SR as the image data is not aligned inherently. In this article, we propose a Dual-path Dynamic Enhancement Network(DDet) for Real-SR, which addresses the cross-camera image mapping by realizing a dual-way dynamic sub-pixel weighted aggregation and refinement. Unlike conventional methods which stack up massive convolutional blocks for feature representation, we introduce a content-aware framework to study non-inherently aligned image pair in image SR issue. First, we use a content-adaptive component to exhibit the Multi-scale Dynamic Attention(MDA). Second, we incorporate a long-term skip connection with a Coupled Detail Manipulation(CDM) to perform collaborative compensation and manipulation. The above dual-path model is joint into a unified model and works collaboratively. Extensive experiments on the challenging benchmarks demonstrate the superiority of our model.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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