Simple Base Frame Guided Residual Network for RAW Burst Image Super-Resolution

Anderson Cotrim, Gerson Barbosa, Gerson Barbosa, Cid Santos, Helio Pedrini

2024

Abstract

Burst super-resolution or multi-frame super-resolution (MFSR) has gained significant attention in recent years, particularly in the context of mobile photography. With modern handheld devices consistently increasing their processing power and the ability to capture multiple images even faster, the development of robust MFSR algorithms has become increasingly feasible. Furthermore, in contrast to extensively studied single-image super-resolution (SISR), burst super-resolution mitigates the ill-posed nature of reconstructing high-resolution images from low-resolution ones by merging information from multiple shifted frames. This research introduces a novel and effective deep learning approach, SBFBurst, designed to tackle this challenging problem. Our network takes multiple noisy RAW images as input and generates a denoised, super-resolved RGB image as output. We demonstrate that significant enhancements can be achieved in this problem by incorporating base frame-guided mechanisms through operations such as feature map concatenation and skip connections. Additionally, we highlight the significance of employing mosaicked convolution to enhance alignment, thus enhancing the overall network performance in super-resolution tasks. These relatively simple improvements underscore the competitiveness of our proposed method when compared to other state-of-the-art approaches.

Paper Citation

in Harvard Style

Cotrim A., Barbosa G., Santos C. and Pedrini H. (2024). Simple Base Frame Guided Residual Network for RAW Burst Image Super-Resolution. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 77-87. DOI: 10.5220/0012348300003660

in Bibtex Style

@conference{visapp24,
author={Anderson Cotrim and Gerson Barbosa and Cid Santos and Helio Pedrini},
title={Simple Base Frame Guided Residual Network for RAW Burst Image Super-Resolution},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2024},
pages={77-87},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012348300003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Simple Base Frame Guided Residual Network for RAW Burst Image Super-Resolution
SN - 978-989-758-679-8
AU - Cotrim A.
AU - Barbosa G.
AU - Santos C.
AU - Pedrini H.
PY - 2024
SP - 77
EP - 87
DO - 10.5220/0012348300003660
PB - SciTePress