用小模型实现高质量模糊图像去模糊，靠深度图和小波变换提升效率
A low-complexity method for efficient depth-guided image deblurring
- 结合小波变换分离细节，降低空间冗余，配合深度图高效调制特征
- 在保持顶尖图像质量的同时，计算复杂度降低达两个数量级
- 适合移动端或边缘设备部署，尤其适用于带深度传感器的场景
图像去模糊因高度病态而极具挑战性。尽管深度学习模型已取得显著进展，但为追求最佳图像质量导致计算复杂度飙升，难以在普通设备上运行。近期研究表明，移动激光雷达可提供深度图作为补充信息，显著提升去模糊效果。本文提出一种新型低复杂度神经网络，用于深度引导的图像去模糊。实验表明，利用小波变换分离结构细节以减少空间冗余，并对深度信息进行高效特征调制，是构建轻量级模型的关键。所提方法在保持与最新先进模型相当的图像质量的同时，计算复杂度降低高达两个数量级。
原文摘要 · Abstract (English)
Image deblurring is a challenging problem in imaging due to its highly ill-posed nature. Deep learning models have shown great success in tackling this problem but the quest for the best image quality has brought their computational complexity up, making them impractical on anything but powerful servers. Meanwhile, recent works have shown that mobile Lidars can provide complementary information in the form of depth maps that enhance deblurring quality. In this paper, we introduce a novel low-complexity neural network for depth-guided image deblurring. We show that the use of the wavelet transform to separate structural details and reduce spatial redundancy as well as efficient feature conditioning on the depth information are essential ingredients in developing a low-complexity model. Experimental results show competitive image quality against recent state-of-the-art models while reducing complexity by up to two orders of magnitude.
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