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 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.
翻译:本文展示了一个GPU加速计算框架, 用于在高山- 低地噪音混杂的情况下重建高分辨率( HR) LF 图像。 主要重点是开发高性能方法, 同时考虑到处理速度和重建质量。 从统计角度, 我们得出了一个联合的 $=1- $\ ell=2$ 数据忠诚术语, 以惩罚人力资源重建错误, 同时考虑到混合噪音情况。 对于正规化, 我们采用了加权的非本地价格总变异方法, 使我们能够通过适当的加权方案在之前有效地实现LF 图像。 我们显示, 乘数算法( ADMM) 的交替方向方法可以用来简化计算复杂性, 并在 GPU平台上进行高性性平行计算。 我们从合成的 4D LF 数据集和自然图像数据集上进行了广泛的实验, 以验证拟议的SR 模型的稳健性, 并评估加速优化的性能。 实验结果显示, 我们的方法在严重的混合化条件下, 与州一级 $ 1. 美元 的平价 方法( ADMMM ) 交价 的交替方向方法( ADMMMMM ) 方法可以用来简化 。