Multi-Frame Super-Resolution With Raw Images Via Modified Deformable Convolution

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.