CVJun 20, 2017

Multi-frame image super-resolution with fast upscaling technique

arXiv:1706.06266v210 citations
Originality Incremental advance
AI Analysis

This work addresses blurring artifacts in multi-frame image super-resolution for applications like video enhancement, but it is incremental as it builds on existing reconstruction-based approaches.

The paper tackles the problem of multi-frame image super-resolution by proposing a fast upscaling technique to replace interpolation, reducing computational complexity and achieving superior performance with fewer blurring artifacts, outperforming state-of-the-art methods when combined with BTV regularization.

Multi-frame image super-resolution (MISR) aims to fuse information in low-resolution (LR) image sequence to compose a high-resolution (HR) one, which is applied extensively in many areas recently. Different with single image super-resolution (SISR), sub-pixel transitions between multiple frames introduce additional information, attaching more significance to fusion operator to alleviate the ill-posedness of MISR. For reconstruction-based approaches, the inevitable projection of reconstruction errors from LR space to HR space is commonly tackled by an interpolation operator, however crude interpolation may not fit the natural image and generate annoying blurring artifacts, especially after fusion operator. In this paper, we propose an end-to-end fast upscaling technique to replace the interpolation operator, design upscaling filters in LR space for periodic sub-locations respectively and shuffle the filter results to derive the final reconstruction errors in HR space. The proposed fast upscaling technique not only reduce the computational complexity of the upscaling operation by utilizing shuffling operation to avoid complex operation in HR space, but also realize superior performance with fewer blurring artifacts. Extensive experimental results demonstrate the effectiveness and efficiency of the proposed technique, whilst, combining the proposed technique with bilateral total variation (BTV) regu-larization, the MISR approach outperforms state-of-the-art methods.

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