论文标题
深度语义统计匹配（D2SM）denoising网络
Deep Semantic Statistics Matching (D2SM) Denoising Network
论文作者
论文摘要
图像恢复的最终目的（例如DeNoising）是找到嘈杂和清除图像域之间的确切相关性。但是，以样本到样本的方式进行了端到端denoising学习（例如像素损失）的优化，这忽略了图像的内在相关性，尤其是语义。在本文中，我们介绍了深度语义统计匹配（D2SM）DENOISISN网络。它利用了预审前的分类网络的语义特征，然后隐含地与语义特征空间上清晰图像的概率分布匹配。通过学习保留DeNocied图像的语义分布，我们从经验上发现我们的方法显着提高了网络的可转换功能，并且可以通过高级视觉任务更好地理解deno的结果。在嘈杂的CityScapes数据集上进行的综合实验证明了我们方法在降解性能和语义分割精度上的优越性。此外，在我们的扩展任务上观察到的绩效改进，包括超分辨率和除尘实验，显示了其作为新的一般插件组件的潜力。
The ultimate aim of image restoration like denoising is to find an exact correlation between the noisy and clear image domains. But the optimization of end-to-end denoising learning like pixel-wise losses is performed in a sample-to-sample manner, which ignores the intrinsic correlation of images, especially semantics. In this paper, we introduce the Deep Semantic Statistics Matching (D2SM) Denoising Network. It exploits semantic features of pretrained classification networks, then it implicitly matches the probabilistic distribution of clear images at the semantic feature space. By learning to preserve the semantic distribution of denoised images, we empirically find our method significantly improves the denoising capabilities of networks, and the denoised results can be better understood by high-level vision tasks. Comprehensive experiments conducted on the noisy Cityscapes dataset demonstrate the superiority of our method on both the denoising performance and semantic segmentation accuracy. Moreover, the performance improvement observed on our extended tasks including super-resolution and dehazing experiments shows its potentiality as a new general plug-and-play component.
