论文标题
盲人运动通过新加索的建筑
Blind Motion Deblurring through SinGAN Architecture
论文作者
论文摘要
盲运动脱毛涉及从模糊的观察结果中重建尖锐的图像。这个问题是一个不适的问题，属于图像恢复问题的类别。基于数据的培训方法用于图像脱毛，主要涉及需要大量时间的训练模型。这些模型是渴望数据的，即它们需要大量培训数据来产生令人满意的结果。最近，开发了各种图像特征学习方法，可减轻我们对训练数据的需求，并执行图像恢复和图像合成，例如DIP，Ingan和Singan。 Singan是一种无条件的生成模型，可以从单个自然图像中学到。该模型主要捕获图像中存在的斑块的内部分布，并能够在保留图像的视觉内容的同时生成各种多样性的样本。由模型产生的图像非常类似于真实的自然图像。在本文中，我们专注于通过新加坡建筑进行盲目动作。
Blind motion deblurring involves reconstructing a sharp image from an observation that is blurry. It is a problem that is ill-posed and lies in the categories of image restoration problems. The training data-based methods for image deblurring mostly involve training models that take a lot of time. These models are data-hungry i.e., they require a lot of training data to generate satisfactory results. Recently, there are various image feature learning methods developed which relieve us of the need for training data and perform image restoration and image synthesis, e.g., DIP, InGAN, and SinGAN. SinGAN is a generative model that is unconditional and could be learned from a single natural image. This model primarily captures the internal distribution of the patches which are present in the image and is capable of generating samples of varied diversity while preserving the visual content of the image. Images generated from the model are very much like real natural images. In this paper, we focus on blind motion deblurring through SinGAN architecture.
