低质量相机的模糊内核
我正在做一些图像增强实验，所以我用我的廉价相机拍照。相机有马赛克伪像，所有图像看起来都像网格。我认为药盒（失焦）内核和高斯内核不是最佳候选。有什么建议吗？
编辑：
示例
我怀疑这不能通过恒定内核来完成，因为对像素的影响不一样（所以就有了“网格”）。
I am doing some image enhancement experiments so I take photos from my cheap camera. The camera has mosaic artifacts and all images look like grid. I think pillbox (out-of-focus) kernel and Gaussian kernel would not be the best candidates. Any suggestions?
EDIT:
Sample
I suspect this cannot be done via a constant kernel, because the effects on pixels are not the same (so there are "grids").
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效果是非线性的。 （而且可能是非平稳的），所以你不能简单地反转卷积并增强图像——如果可以的话，相机芯片会在板上完成它。
计算出卷积是什么（或至少是它的近似值）的最佳方法可能是拍摄已知模式的照片、计算并在 2D 频率/拉普拉斯域中工作，将所得频谱除以得到滤波器的线性近似值。
我怀疑您通过这样做发现的卷积将非常依赖于上下文 - 因此增强图像的最佳方法可能是将其划分为图块，将图像的每个区域分类为属于一个不同的集合（对于每个集合，您可以根据测试数据计算出不同的卷积线性近似值），然后分别对每个集合进行反卷积。
The effects are non linear. (And probably non-stationary), so you cannot simply invert the convolution and enhance the image -- if you could, the camera chip would do it on-board.
The best way to work out what the convolution is (or at least an approximation to it) might be to take photos of known patterns, compute, and working in 2D frequency/laplace domain divide the resulting spectra to get a linear approximation to the filter.
I suspect that the convolution you discover by doing this will be very context dependant -- so the best way to enhance an image might be to divide it into tiles, classify each region of the image as belonging to a different set (for each of which you could work out a different linear approximation to the convolution, based on test data), and then deconvolve each separately.