Adaptive Feature Consolidation Network for Burst Super-Resolution

Nancy Mehta, Akshay Dudhane, Subrahmanyam Murala, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022, pp. 1279-1286


Modern digital cameras generally count on image signal processing (ISP) pipelines for producing naturalistic RGB images. Nevertheless, in comparison to DSLR cameras, low-quality images are generally output from portable mobile devices due to their physical limitations. The synthesized low-quality images usually have multiple degradations - low-resolution owing to small camera sensors, mosaic patterns on account of camera filter array and sub-pixel shifts due to camera motion. Such degradation usually restrain the performance of single image super-resolution methodologies for retrieving high-resolution (HR) image from a single low-resolution (LR) image. Burst image super-resolution aims at restoring a photo-realistic HR image by capturing the abundant information from multiple LR images. Lately, the soaring popularity of burst photography has made multi-frame processing an attractive solution for overcoming the limitations of single image processing. In our work, we thus aim to propose a generic architecture, adaptive feature consolidation network (AFCNet) for multi-frame processing. To alleviate the challenge of effectively modelling the long-range dependency problem, that multi-frame approaches struggle to solve, we utilize encoder-decoder based transformer backbone which learns multi-scale local-global representations. We propose feature alignment module to align LR burst frame features. Further, the aligned features are fused and reconstructed by abridged pseudo-burst fusion module and adaptive group upsampling modules, respectively. Our proposed approach clearly outperforms the other existing state-of-the-art techniques on benchmark datasets. The experimental results illustrate the effectiveness and generality of our proposed framework in upgrading the visual quality of HR images.

Related Material

@InProceedings{Mehta_2022_CVPR, author = {Mehta, Nancy and Dudhane, Akshay and Murala, Subrahmanyam and Zamir, Syed Waqas and Khan, Salman and Khan, Fahad Shahbaz}, title = {Adaptive Feature Consolidation Network for Burst Super-Resolution}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2022}, pages = {1279-1286} }