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Multi-frame super-resolution with raw images via modified deformable convolution

Gongzhe Li, Linwei Qiu, Haopeng Zhang, Fengying Xie, Zhiguo Jiang

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    Length: 00:05:17
10 May 2022

In this paper, we propose a novel model towards multi-frame super-resolution, which leverages multiple RAW images and yields a super-resolved RGB image. To facilitate the pixel misalignment in burst photography, we apply a refined Pyramid Cascading and Deformable Convolution (PCD) feature alignment module. A new 3D deformable convolution fusion module is proposed subsequently to merge the information from all frames adaptively. In addition, we employ an encoder-decoder network to restore color and details in sRGB space after super-resolving images in linear space. Extensive experiments demonstrate the superiority of our architecture and the strength of multi-frame super-resolution with RAW images.

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    Members: Free
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  • SPS
    Members: Free
    IEEE Members: $11.00
    Non-members: $15.00