Mamba-based Light Field Super-Resolution with Efficient Subspace Scanning

• URL: http://arxiv.org/abs/2406.16083v1
• Date: Sun, 23 Jun 2024 11:28:08 GMT
• Title: Mamba-based Light Field Super-Resolution with Efficient Subspace Scanning
• Authors: Ruisheng Gao, Zeyu Xiao, Zhiwei Xiong,
• Abstract summary: Transformer-based methods have demonstrated impressive performance in 4D light field (LF) super-resolution. However, their quadratic complexity hinders the efficient processing of high resolution 4D inputs. We propose a Mamba-based Light Field Super-Resolution method, named MLFSR, by designing an efficient subspace scanning strategy.
• Score: 48.99361249764921
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Transformer-based methods have demonstrated impressive performance in 4D light field (LF) super-resolution by effectively modeling long-range spatial-angular correlations, but their quadratic complexity hinders the efficient processing of high resolution 4D inputs, resulting in slow inference speed and high memory cost. As a compromise, most prior work adopts a patch-based strategy, which fails to leverage the full information from the entire input LFs. The recently proposed selective state-space model, Mamba, has gained popularity for its efficient long-range sequence modeling. In this paper, we propose a Mamba-based Light Field Super-Resolution method, named MLFSR, by designing an efficient subspace scanning strategy. Specifically, we tokenize 4D LFs into subspace sequences and conduct bi-directional scanning on each subspace. Based on our scanning strategy, we then design the Mamba-based Global Interaction (MGI) module to capture global information and the local Spatial- Angular Modulator (SAM) to complement local details. Additionally, we introduce a Transformer-to-Mamba (T2M) loss to further enhance overall performance. Extensive experiments on public benchmarks demonstrate that MLFSR surpasses CNN-based models and rivals Transformer-based methods in performance while maintaining higher efficiency. With quicker inference speed and reduced memory demand, MLFSR facilitates full-image processing of high-resolution 4D LFs with enhanced performance.

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