Abstract
Several recent works discussed application-driven image restoration neural networks, which are capable of not only removing noise in images but also preserving their semantic-aware details, making them suitable for various high-level computer vision tasks as the pre-processing step. However, such approaches require extra annotations for their high-level vision tasks, in order to train the joint pipeline using hybrid losses. The availability of those annotations is yet often limited to a few image sets, potentially restricting the general applicability of these methods to denoising more unseen and unannotated images. Motivated by that, we propose a segmentation-aware image denoising model dubbed U-SAID, based on a novel unsupervised approach with a pixel-wise uncertainty loss. U-SAID does not need any ground-truth segmentation map, and thus can be applied to any image dataset. It generates denoised images with comparable or even better quality, and the denoised results show stronger robustness for subsequent semantic segmentation tasks, when compared to either its supervised counterpart or classical "application-agnostic" denoisers. Moreover, we demonstrate the superior generalizability of U-SAID in three-folds, by plugging its "universal" denoiser without fine-tuning: (1) denoising unseen types of images; (2) denoising as pre-processing for segmenting unseen noisy images; and (3) denoising for unseen high-level tasks. Extensive experiments demonstrate the effectiveness, robustness and generalizability of the proposed U-SAID over various popular image sets.
Abstract (translated)
最近的一些研究工作讨论了应用驱动的图像恢复神经网络，它不仅能够去除图像中的噪声，而且能够保留图像的语义感知细节，使其适合作为预处理步骤的各种高级计算机视觉任务。然而，这种方法需要对其高级视觉任务进行额外的注释，以便使用混合损失训练联合管道。然而，这些注释的可用性通常仅限于几个图像集，这可能限制了这些方法在消除更不可见和未标记图像方面的一般适用性。在此基础上，我们提出了一种基于无监督、像素不确定性损失的图像去噪模型。U-said不需要任何地面真值分割图，因此可以应用于任何图像数据集。它生成的去噪图像具有可比的甚至更好的质量，并且与它的监督对等物或经典的“应用不可知”去噪器相比，去噪结果显示对随后的语义分割任务具有更强的鲁棒性。此外，我们还通过在不进行微调的情况下插入其“通用”去噪器，证明了u-said在三个折叠中具有优越的通用性：（1）去噪看不到的图像类型；（2）去噪作为分割看不到的噪声图像的预处理；（3）去噪看不到的高级任务。大量的实验证明了所提出的U-said在各种常用图像集上的有效性、鲁棒性和可推广性。
URL
https://arxiv.org/abs/1905.08965
