Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, South Korea.
IEEE Trans Pattern Anal Mach Intell. 2013 Oct;35(10):2498-512. doi: 10.1109/TPAMI.2013.40.
This paper investigates the role that nonlinear camera response functions (CRFs) have on image deblurring. We present a comprehensive study to analyze the effects of CRFs on motion deblurring. In particular, we show how nonlinear CRFs can cause a spatially invariant blur to behave as a spatially varying blur. We prove that such nonlinearity can cause large errors around edges when directly applying deconvolution to a motion blurred image without CRF correction. These errors are inevitable even with a known point spread function (PSF) and with state-of-the-art regularization-based deconvolution algorithms. In addition, we show how CRFs can adversely affect PSF estimation algorithms in the case of blind deconvolution. To help counter these effects, we introduce two methods to estimate the CRF directly from one or more blurred images when the PSF is known or unknown. Our experimental results on synthetic and real images validate our analysis and demonstrate the robustness and accuracy of our approaches.
本文研究了非线性相机响应函数 (CRF) 在图像去模糊中的作用。我们进行了全面的研究来分析 CRF 对运动去模糊的影响。特别地，我们展示了非线性 CRF 如何使空间不变模糊表现为空间变化模糊。我们证明，在没有 CRF 校正的情况下，直接对运动模糊图像进行反卷积会导致非线性引起的边缘附近出现大的误差。即使使用已知的点扩散函数 (PSF) 和最先进的基于正则化的反卷积算法，这些误差也是不可避免的。此外，我们展示了在盲反卷积的情况下，CRF 如何对 PSF 估计算法产生不利影响。为了帮助克服这些影响，我们引入了两种方法，当 PSF 已知或未知时，可以直接从一个或多个模糊图像中估计 CRF。我们在合成和真实图像上的实验结果验证了我们的分析，并展示了我们方法的鲁棒性和准确性。
