论文标题
原始图像脱毛
Raw Image Deblurring
论文作者
论文摘要
基于深度学习的盲目图像deblurring在解决图像模糊中起着至关重要的作用，因为所有现有的内核在对现实世界的模糊建模方面都受到限制。到目前为止，研究人员专注于强大的模型来处理过度的问题并取得不错的结果。对于这项工作，在一个新方面，我们直接从原始图像中发现了图像增强（例如Deblurring）的绝佳机会，并研究有益于原始学习的新型神经网络结构。但是，据我们所知，没有可用的原始图像Deblurring数据集。因此，我们构建了一个包含原始图像和处理过的SRGB图像的新数据集，并设计了一个新模型来利用原始图像的独特特征。所提出的Deblurring模型仅根据原始图像进行训练，实现了最先进的性能，超过了在经过处理的SRGB图像上训练的型号。此外，通过微调，在我们的新数据集中训练的拟议模型可以推广到其他传感器。此外，通过一系列实验，我们证明了现有的DeBlurring模型也可以通过对新数据集中的原始图像进行训练来改进。最终，我们将根据设计的基于原始原始的Deblurring方法和全新的DeBlur-Raw数据集展示了一个新的场所，以获得更多机会。
Deep learning-based blind image deblurring plays an essential role in solving image blur since all existing kernels are limited in modeling the real world blur. Thus far, researchers focus on powerful models to handle the deblurring problem and achieve decent results. For this work, in a new aspect, we discover the great opportunity for image enhancement (e.g., deblurring) directly from RAW images and investigate novel neural network structures benefiting RAW-based learning. However, to the best of our knowledge, there is no available RAW image deblurring dataset. Therefore, we built a new dataset containing both RAW images and processed sRGB images and design a new model to utilize the unique characteristics of RAW images. The proposed deblurring model, trained solely from RAW images, achieves the state-of-art performance and outweighs those trained on processed sRGB images. Furthermore, with fine-tuning, the proposed model, trained on our new dataset, can generalize to other sensors. Additionally, by a series of experiments, we demonstrate that existing deblurring models can also be improved by training on the RAW images in our new dataset. Ultimately, we show a new venue for further opportunities based on the devised novel raw-based deblurring method and the brand-new Deblur-RAW dataset.
