反褶积
盲反褶积
计算机科学
图像复原
人工智能
卷积(计算机科学)
计算机视觉
光学(聚焦)
图像(数学)
傅里叶变换
噪音(视频)
维纳反褶积
不变(物理)
图像处理
算法
模式识别(心理学)
人工神经网络
数学
数学物理
光学
物理
数学分析
作者
Christian J. Schuler,Harold Christopher Burger,Stefan Harmeling,Bernhard Schölkopf
标识
DOI:10.1109/cvpr.2013.142
摘要
Image deconvolution is the ill-posed problem of recovering a sharp image, given a blurry one generated by a convolution. In this work, we deal with space-invariant non-blind deconvolution. Currently, the most successful methods involve a regularized inversion of the blur in Fourier domain as a first step. This step amplifies and colors the noise, and corrupts the image information. In a second (and arguably more difficult) step, one then needs to remove the colored noise, typically using a cleverly engineered algorithm. However, the methods based on this two-step approach do not properly address the fact that the image information has been corrupted. In this work, we also rely on a two-step procedure, but learn the second step on a large dataset of natural images, using a neural network. We will show that this approach outperforms the current state-of-the-art on a large dataset of artificially blurred images. We demonstrate the practical applicability of our method in a real-world example with photographic out-of-focus blur.
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