Learnable Global Spatio-Temporal Adaptive Aggregation for Bracketing Image Restoration and Enhancement

Xinwei Dai, Yuanbo Zhou, Xintao Qiu, Hui Tang, Wei Deng, Qinquan Gao, Tong Tong; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 6235-6245

Employing specific networks to address different types of degradation often proved to be complex and time-consuming in practical applications. The Bracket Image Restoration and Enhancement (BIRE) aimed to address various image restoration tasks in a unified manner by restoring clear single-frame images from multiple-frame shots including denoising deblurring enhancing high dynamic range (HDR) and achieving super-resolution under various degradation conditions. In this paper we propose LGSTANet an efficient aggregation restoration network for BIRE. Specifically inspired by video restoration methods we adopt an efficient architecture comprising alignment aggregation and reconstruction. Additionally we introduce a Learnable Global Spatio-Temporal Adaptive (LGSTA) aggregation module to effectively aggregate inter-frame complementary information. Furthermore we propose an adaptive restoration modulator to address specific degradation disturbances of various types thereby achieving high-quality restoration outcomes. Extensive experiments demonstrate the effectiveness of our method. LGSTANet outperforms other state-of-the-art methods in Bracket Image Restoration and Enhancement and achieves competitive results in the NTIRE2024 BIRE challenge.