QMambaBSR: Burst Image Super-Resolution with Query State Space Model
Xin Di, Long Peng, Peizhe Xia, Wenbo Li, Renjing Pei, Yang Cao, Yang, Wang, Zheng-Jun Zha

TL;DR
This paper introduces QMambaBSR, a novel burst super-resolution method that uses a query state space model and adaptive up-sampling to better extract sub-pixel details and suppress noise, achieving state-of-the-art results.
Contribution
The paper proposes a new Query State Space Model and adaptive up-sampling module for burst super-resolution, addressing limitations of existing static methods and improving detail reconstruction.
Findings
Achieves state-of-the-art performance on synthetic benchmarks.
Effectively suppresses noise while extracting sub-pixels.
Dynamically adjusts upsampling based on scene content.
Abstract
Burst super-resolution aims to reconstruct high-resolution images with higher quality and richer details by fusing the sub-pixel information from multiple burst low-resolution frames. In BusrtSR, the key challenge lies in extracting the base frame's content complementary sub-pixel details while simultaneously suppressing high-frequency noise disturbance. Existing methods attempt to extract sub-pixels by modeling inter-frame relationships frame by frame while overlooking the mutual correlations among multi-current frames and neglecting the intra-frame interactions, leading to inaccurate and noisy sub-pixels for base frame super-resolution. Further, existing methods mainly employ static upsampling with fixed parameters to improve spatial resolution for all scenes, failing to perceive the sub-pixel distribution difference across multiple frames and cannot balance the fusion weights of…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Image and Signal Denoising Methods · Advanced Vision and Imaging
MethodsMamba: Linear-Time Sequence Modeling with Selective State Spaces · Balanced Selection
