低画质相机的模糊内核 [英] The blurring kernel of a low-quality camera

问题描述

我正在做一些图像增强实验，所以我用便宜的相机拍照.相机具有马赛克伪像，所有图像看起来都像网格.我认为药盒(散焦)内核和高斯内核将不是最佳选择.有什么建议?

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?

编辑:
样本

我怀疑这不能通过恒定的内核来完成，因为对像素的影响是不一样的(因此有网格").

I suspect this cannot be done via a constant kernel, because the effects on pixels are not the same (so there are "grids").

推荐答案

效果是非线性的. (并且可能是非平稳的)，因此您不能简单地反转卷积并增强图像-如果可以的话，摄像头芯片将在板上完成.

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.

弄清楚卷积是什么(或至少是卷积的近似值)的最佳方法可能是拍摄已知模式的照片，进行计算，并在2D频域/拉普拉斯域中进行工作，将所得频谱除以得到线性近似值过滤器.

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.

我怀疑通过此操作发现的卷积将非常与上下文相关，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对卷积进行不同的线性近似处理)，然后分别对每个卷积进行反卷积.

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.

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