AI in EE

AI IN DIVISIONS

AI in Signal Division

AI in EE

AI IN DIVISIONS

AI in Signal Division ​ ​

AI in Signal Division

Cross-Active Connection for Image-Text Multimodal Feature Fusion

 

Burst image super-resolution is an ill-posed problem that aims to restore a high-resolution (HR) image from a se- quence of low-resolution (LR) burst images. To restore a photo-realistic HR image using their abundant information, it is essential to align each burst of frames containing ran- dom hand-held motion. Some kernel prediction networks (KPNs) that are operated without external motion compen- sation such as optical flow estimation have been applied to burst image processing as implicit image alignment mod- ules. However, the existing methods do not consider the interdependencies among the kernels of different sizes that have a significant effect on each pixel. In this paper, we propose a novel weighted multi-kernel prediction network (WMKPN) that can learn the discriminative features on each pixel for burst image super-resolution. Our experi- mental results demonstrate that WMKPN improves the vi- sual quality of super-resolved images. To the best of our knowledge, it outperforms the state-of-the-art within ker- nel prediction methods and multiple frame super-resolution (MFSR) on both the Zurich RAW to RGB and BurstSR datasets.

 

Copyright ⓒ 2015 KAIST Electrical Engineering. All rights reserved. Made by PRESSCAT

Copyright ⓒ 2015 KAIST Electrical Engineering. All rights reserved. Made by PRESSCAT

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한국과학기술원(KAIST)
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Copyright ⓒ 2015 KAIST Electrical
Engineering. All rights reserved.
Made by PRESSCAT