Median filter? Use it to «thin out» bad pixels that appear locally sparse (for some definition of «local»). Then count the remaining bad pixels.
Or convolve with a large-ish 2d kernel (eg flat rectangular window) and decimate to get a number for «how many bad pixels are there inside each eg 16x16 window». Then accumulate the score for each block in a ...
I've not tried this, but I'm wondering if something like a simple low pass filtering, followed by a column sum will do.
No time to explain code right now, but this seems to do something like what you want. Will return in a few hours and explain what it's doing.
R Code Below
N <- 128
noise <- array(runif(N*N*1*1),c(N,N,1,1)) #5x5 ...
Would it be possible to extract the "smudge" (i.e., the equivalent of an image filter applied to the physical photo that results in the scanned image), and then apply the inverse of this filter to all my images (with appropriate rotation and offset)?
It is difficult to reply Yes or No to this question. The results depend very much to what you are working ...
I would take approach based on Blind Deconvolution.
Since we're dealing with ill posed problem some assumptions should be made.
The intuitive approach would be using the information as a prior for the coefficients of the filter. Yet since you have Discrete Prior we're getting into combinatoric problem.
Which means brute force solution where the number of ...
This sounds like a blind channel estimation problem. Blind channel estimation is used such as in emerging massive MIMO systems where pilot contamination can otherwise limit the advantage of adding additional transmitters.
A very simple example of blind channel estimation is decision directed least squares using the least squares technique that I describe at ...
The approach seems reasonable.
Indeed doing edge detection in weighted RGB channel is the classic approach (Though you could also employ more advance methods, See Edge Detection on a Color Image).
I think you could achieve great results if you also look specifically for oval shapes then you reduce the chances for false positives.
Color identification in ...
That really doesn't make sense: you'd be training a neural network to mimic a conversion algorith, which you already have. That's a waste of electricity ;) I doubt the resulting nets would generalize at all: you're not even training them with actual day/night picture pairs. So, really, as usual: one of the most significant problems in machine learning is ...
"Need" or "Want"?
Q1: The second derivative avoids the problem of a gradually changing color being "greyish".
Here is one:
After advice about detecting focus quality of objects in a photo detected using YoloV3
It is based on how planar the color. Very planar means no edge.
There are many, many other variations of these techniques possible.
I think this part in article is starting point of the confusion :
See how the $I(x+u, y+v)$ changed into a totally different form
$I(x,y)+ uI_x + vI_y)$?
The author should have write a bit more detailed notation as:
$I(x,y)+ uI_x(x,y) + vI_y(x,y)$
i.e. pointing out that these are element-wise spatial derivatives (like Sobel) not matrix derivatives ( ...
Probably the value of the DCT coefficient and its frequency (i.e., a histogram plot). However, it's impossible to say that with certainty given the information you have provided. If you found that plot in the linked paper, then the definitions of the x- and y-axes are in all likelihood given therein. The paper is behind a paywall, so it's not helpful to ...
The error source is the kernel function, which should be a multiplication of two 1D sincs (without rotational symmetry) instead of the Sombrero function, which is a sinc function with rotational symmetry.
The approach I took was using MATLAB's functions, either regionprops() or bwconncomp() and bwdist().
The idea is to give a grade for each pixel which is a part of bad pixels object.
The grade is the radius of the circle bounding the object the pixel resides in.
One way to calculate the radius of the bounding circle is the MajorAxisLength property in the ...
Erode the image using a structuring element which is the size/shape of the maximum allowable "bad region". Then dilate using the same structuring element. This will remove the bad-but-good-enough regions. From there you can work on characterizing/measuring what's left. Example given below using Matlab.
% Create a blank image.
Mimg = 1000;
Nimg = 1000;
img = ...