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A GPU-accelerated light-field super-resolution framework based on mixed noise model and weighted regularization
Journal of Real-Time Image Processing ( IF 3 ) Pub Date : 2022-07-11 , DOI: 10.1007/s11554-022-01230-2
Trung-Hieu Tran, Kaicong Sun, Sven Simon

Light-field (LF) super-resolution (SR) plays an essential role in alleviating the current technology challenge in the acquisition of a 4D LF, which assembles both high-density angular and spatial information. Due to the algorithm complexity and data-intensive property of LF images, LFSR demands a significant computational effort and results in a long CPU processing time. This paper presents a GPU-accelerated computational framework for reconstructing high-resolution (HR) LF images under a mixed Gaussian-Impulse noise condition. The main focus is on developing a high-performance approach considering processing speed and reconstruction quality. From a statistical perspective, we derive a joint \(\ell ^1\)-\(\ell ^2\) data fidelity term for penalizing the HR reconstruction error taking into account the mixed noise situation. For regularization, we employ the weighted non-local total variation approach, which allows us to effectively realize LF image prior through a proper weighting scheme. We show that the alternating direction method of the multipliers algorithm (ADMM) can be used to simplify the computation complexity and results in a high-performance parallel computation on the GPU Platform. An extensive experiment is conducted on both synthetic 4D LF dataset and natural image dataset to validate the proposed SR model’s robustness and evaluate the accelerated optimizer’s performance. The experimental results show that our approach achieves better reconstruction quality under severe mixed-noise conditions as compared to the state-of-the-art approaches. In addition, the proposed approach overcomes the limitation of the previous work in handling large-scale SR tasks. While fitting within a single off-the-shelf GPU, the proposed accelerator provides an average speedup of 2.46\({\times }\) and 1.57\({\times }\) for \({\times }2\) and \({\times }3\) SR tasks, respectively. In addition, a speedup of \(77{\times }\) is achieved as compared to CPU execution.



中文翻译:

基于混合噪声模型和加权正则化的GPU加速光场超分辨率框架

光场 (LF) 超分辨率 (SR) 在缓解当前采集 4D LF 的技术挑战方面发挥着至关重要的作用,它汇集了高密度的角度和空间信息。由于 LF 图像的算法复杂性和数据密集型特性,LFSR 需要大量的计算工作并导致较长的 CPU 处理时间。本文提出了一种 GPU 加速计算框架,用于在混合高斯脉冲噪声条件下重建高分辨率 (HR) LF 图像。主要重点是开发一种考虑处理速度和重建质量的高性能方法。从统计学的角度,我们推导出一个联合\(\ell ^1\) - \(\ell ^2\)考虑到混合噪声情况,用于惩罚 HR 重建误差的数据保真项。对于正则化,我们采用加权的非局部总变化方法,这使我们能够通过适当的加权方案有效地实现 LF 图像先验。我们表明,乘法器算法(ADMM)的交替方向方法可用于简化计算复杂度,并在 GPU 平台上实现高性能并行计算。在合成 4D LF 数据集和自然图像数据集上进行了广泛的实验,以验证所提出的 SR 模型的鲁棒性并评估加速优化器的性能。实验结果表明,与最先进的方法相比,我们的方法在严重的混合噪声条件下实现了更好的重建质量。此外,所提出的方法克服了先前工作在处理大规模 SR 任务方面的局限性。在适合单个现成 GPU 时,建议的加速器提供 2.46 的平均加速\({\times }\)和 1.57 \({\times }\)分别用于\({\times }2\)\({\times }3\) SR 任务。此外,与 CPU 执行相比,实现了\(77{\times }\)的加速。

更新日期:2022-07-12
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