Sorry if this seems like a silly or lazy "I-can't-find-it' question but I've been trying for a few days now to find a paper or anything of the like to explain how to generate speckle noise (on 2D images). I have found out that one of the more simple means of removing speckle noise is a mean filter (which I've already implemented) but absolutely nowhere can I find a way of generating the noise. Could someone please direct me to where I can learn to generate speckle noise? Furthermore would it be a stretch to ask if there was a simple way to do it in OpenCV (a C++ image processing library).
Thanks for any help you can provide.
Speckle noise is essentially a multiplicative noise, which may (or may not) have an additive noise as well (definitions vary depending upon circumstances). This paper provides a good overview of speckle noise, including descriptions and approaches to removing it.
Here is a some simple python code that can produce multiplicative speckle noise:
import cv
im = cv.LoadImage('tree.jpg', cv.CV_LOAD_IMAGE_GRAYSCALE)
mult_noise = cv.CreateImage((im.width,im.height), cv.IPL_DEPTH_32F, 1)
cv.RandArr(cv.RNG(6), mult_noise, cv.CV_RAND_NORMAL, 1, 0.1)
cv.Mul(im, mult_noise, im)
cv.ShowImage("tree with speckle noise", im)
cv.WaitKey(0)
no noise:
with speckle noise:
Speckle noise is linked to the physical imaging process, so I'm not sure it's easy (or even really possible) to simulate it in a general manner.
However, depending on your desired type of images, you can use other forms of noise to approach it. I guess that a multiplicative salt-and-pepper noise should more or less do the trick for simularing a SAR image.
Another (probably better) possibility is to explore the websites of NASA / ESA and look for SAR images (look for programs like Pleiades, Cosmo-Skymed and SAR Lupe). Some gated laser imaging labs have mnybe also released publicly some sample data.
It can be just a matter of adding gaussian noise to your image. cvRandArr seems like a good candidate.
You can also have something more sophisticated by pondering your noise with your signal, which is also easy since it's just some pixel-wide multiplication between original image and your noise.
Related
I want to do image processing using OpenCV and C++. When I am capturing an image in a dark environment it seems to be hard to do people detection. Changing brightness and contrast may help the situation. But my project is related with computer vision. So i want my program to identify weather there is a need of adding or reducing brightness and contrast, But how to identify that? I have no idea, Please help
Good solution: Use illumination so your scene is not dark.
If this is not possible you can increase exposure time and/or gain. Both methods degrade your SNR. Especially with moving people motion blur will become a problem if your exposure time is too high.
Do not just increase image brightness or contrast by software. It makes no difference for your computer, only for you.
Read something about auto exposure algorithms. A well exposed image is neither under nor over exposed. It's histogram should be as broad as possible.
I believe, You can try "histogram equalization" .
Here is an example image that i have used for experiment.
Example
Source code in C++ language
Please let me know if you need any more information regarding this topic.
I think you should consider using an infrared camera. See this article here for example: "Selection of a Visible-Light vs. Thermal Infrared Sensor in
Dynamic Environments Based on Confidence Measures", authors: Cuerda and coworkoers.
Working on a small side project related to Computer Vision, mostly to try playing around with OpenCV. It lead me to an interesting question:
Using feature detection to find known objects in an image isn't always easy- objects are hard to find, especially if the features of the target object aren't great.
But if I could choose ahead of time what it is I'm looking for, then in theory I could generate for myself an optimal image for detection. Any quality that makes feature detection hard would be absent, and all the qualities that make it easy would exist.
I suspect this sort of thought went into things like QR codes, but with the limitations that they wanted QR codes to be simple, and small.
So my question for you: How would you generate an optimal image for later recognition by a camera? What if you already know that certain problems like skew, or partial obscuring would occur?
Thanks very much
I think you need something like AR markers.
Take a look at ArToolkit, ArToolkitPlus or Aruco libraries, they have marker generators and detectors.
And papeer about marker generation: http://www.uco.es/investiga/grupos/ava/sites/default/files/GarridoJurado2014.pdf
If you plan to use feature detection, than marker should be specific to used feature detector. Common practice for detector design is good response to "corners" or regions with high x,y gradients. Also you should note the scaling of target.
The simplest detection can be performed with BLOBS. It can be faster and more robust than feature points. For example you can detect circular blobs or rectangular.
Depending on the distance you want to see your markers from and viewing conditions/backgrounds you typically use and camera resolution/noise you should choose different images/targets. Under moderate perspective from a longer distance a color target is pretty unique, see this:
https://surf-it.soe.ucsc.edu/sites/default/files/velado_report.pdf
at close distances various bar/QR codes may be a good choice. Other than that any flat textured object will be easy to track using homography as opposed to 3D objects.
http://docs.opencv.org/trunk/doc/py_tutorials/py_feature2d/py_feature_homography/py_feature_homography.html
Even different views of 3d objects can be quickly learned and tracked by such systems as Predator:
https://www.youtube.com/watch?v=1GhNXHCQGsM
then comes the whole field of hardware, structured light, synchronized markers, etc, etc. Kinect, for example, uses a predefined pattern projected on the surface to do stereo. This means it recognizes and matches million of micro patterns per second creating a depth map from the matched correspondences. Note that one camera sees the pattern and while another device - a projector generates it working as a virtual camera, see
http://article.wn.com/view/2013/11/17/Apple_to_buy_PrimeSense_technology_from_the_360s_Kinect/
The quickest way to demonstrate good tracking of a standard checkerboard pattern is to use pNp function of open cv:
http://www.juergenwiki.de/work/wiki/lib/exe/fetch.php?media=public:cameracalibration_detecting_fieldcorners_of_a_chessboard.gif
this literally can be done by calling just two functions
found = findChessboardCorners(src, chessboardSize, corners, camFlags);
drawChessCornersDots(dst, chessboardSize, corners, found);
To sum up, your question is very broad and there are multiple answers and solutions. Formulate your viewing condition, camera specs, backgrounds, distances, amount of motion and perspective you expect to have indoors vs outdoors, etc. There is no such a thing as a general average case in computer vision!
I'm trying to align two images taken from a handheld camera.
At first, I was trying to use the OpenCV warpPerspective method based on SIFT/SURF feature points. The problem is the feature-extract & matching process may be extremely slow when the image quality is high (3000x4000). I tried to scale-down the image before find feature-points, the result is not as good as before.(The Mat generated from findHomography shouldn't be affected by scaling down the image, right?) And sometimes, due to lack of good feature point matches, the result is quite strange.
After searching on this topic, it seems that solving the problem in Fourier domain will speed up the registration process. And I've found this question which leads me to the code here.
The only problem is the code is written in python with numpy (not even using OpenCV), which makes it quite hard to re-written to C++ code using OpenCV (In OpenCV, I can only find dft and there's no fftshift nor fft stuff, I'm not quite familiar with NumPy, and I'm not brave enough to simply ignore the missing methods). So I'm wondering why there is not such a Fourier-domain image registration implementation using C++?
Can you guys give me some suggestion on how to implement one, or give me a link to the already implemented C++ version? Or help me to turn the python code into C++ code?
Big thanks!
I'm fairly certain that the FFT method can only recover a similarity transform, that is, only a (2d) rotation, translation and scale. Your results might not be that great using a handheld camera.
This is not quite a direct answer to your question, but, as a suggestion for a speed improvement, have you tried using a faster feature detector and descriptor? In OpenCV SIFT/SURF are some of the slowest methods they have for feature extraction/matching. You could try testing some of their other methods first, they all work quite well and are faster than SIFT/SURF. Especially if you use their FLANN-based matcher.
I've had to do this in the past with similar sized imagery, and using the binary descriptors OpenCV has increases the speed significantly.
If you need only shift you can use OpenCV's phasecorrelate
currently i am having much difficulty thinking of a good method of removing the gradient from a image i received.
The image is a picture taken by a microscope camera that has a light glare in the middle. The image has a pattern that goes throughout the image. However i am supposed to remove the light glare on the image created by the camera light.
Unfortunately due to the nature of the camera it is not possible to take a picture on black background with the light to find the gradient distribution. Nor do i have a comparison image that is without the gradient. (note- the location of the light glare will always be consistant when the picture is taken)
In easier terms its like having a photo with a flash in it but i want to get rid of the flash. The only problem is i have no way to obtaining the image without flash to compare to or even obtaining a black image with just the flash on it.
My current thought is conduct edge detection and obtain samples in specific locations away from the edges (due to color difference) and use that to gauge the distribution of gradient since those areas are supposed to have relatively identical colors. However i was wondering if there was a easier and better way to do this.
If needed i will post a example of the image later.
At the moment i have a preferrence of solving this in c++ using opencv if that makes it easier.
thanks in advance for any possible ideas for this problem. If there is another link, tutorial, or post that may solve my problem i would greatly appreciate the post.
as you can tell there is a light thats being shinned on the img as you can tell from the white spot. and the top is lighter than the bottome due to the light the color inside the oval is actually different when the picture is taken in color. However the color between the box and the oval should be consistant. My original idea was to perhaps sample only those areas some how and build a profile that i can utilize to remove the light but i am unsure how effective that would be or if there is a better way
EDIT :
Well i tried out Roger's suggestion and the results were suprisngly good. Using 110 kernel gaussian blurr to find illumination and conducting CLAHE on top of that. (both done in opencv)
However my colleage told me that the image doesn't look perfectly uniform and pointed out that around the area where the light used to be is slightly brighter. He suggested trying a selective gaussian blur where the areas above certain threshold pixel values are not blurred while the rest of the image is blurred.
Does anyone have opinions regarding this and perhaps a link, tutorial, or an example of something like this being done? Most of the things i find tend to be selective blur for programs like photoshop and gimp
EDIT2 :
it is difficult to tell with just eyes but i believe i have achieved relatively close uniformization by using a simple plane fitting algorithm.((-A * x - B * y) / C) (x,y,z) where z is the pixel value. I think that this can be improved by utilizing perhaps a sine fitting function? i am unsure. But I am relatively happy with the results. Many thanks to Roger for the great ideas.
I believe using a bunch of pictures and getting the avg would've been another good method (suggested by roger) but Unofruntely i was not able to implement this since i was not supplied with various pictures and the machine is under modification so i was unable to use it.
I have done some work in this area previously and found that a large Gaussian blur kernel can produce a reasonable approximation to the background illumination. I will try to get something working on your example image but, in the meantime, here is an example of your image after Gaussian blur with radius 50 pixels, which may help you decide if it's worth progressing.
UPDATE
Just playing with this image, you can actually get a reasonable improvement using adaptive histogram equalisation (I used CLAHE) - see comparison below - any use?
I will update this answer with more details as I progress.
I would like to point you to this paper: http://www.cs.berkeley.edu/~ravir/dirtylens.pdf, but, in my opinion, without any sort of calibration/comparison image taken apriori, it is difficult to mine out the ground truth from the flared image.
However, if you are trying to just present the image minus the lens flare, disregarding the actual scientific data behind the flared part, then you switch into the domain of image inpainting. Criminsi's algorithm, as described in this paper: http://research.microsoft.com/pubs/67276/criminisi_tip2004.pdf and explained/simplified in these two links: http://cs.brown.edu/courses/csci1950-g/results/final/eboswort/ http://www.cc.gatech.edu/~sooraj/inpainting/, will do a very good job in restoring texture information to the flared up regions. (If you'd really like to pursue this approach, do mention that. More comprehensive help can be provided for this).
However, given the fact that we're dealing with microscopic data, I doubt if you'd like to lose the scientific data contained in a particular region of an image. In that case, I really think you need to find a workaround to determine the flare model of the flash/light source w.r.t the lens you're using.
I hope someone else can shed more light on this.
I'm writing some code to scale a 32 bit RGBA image in C/C++. I have written a few attempts that have been somewhat successful, but they're slow and most importantly the quality of the sized image is not acceptable.
I compared the same image scaled by OpenGL (i.e. my video card) and my routine and it's miles apart in quality. I've Google Code Searched, scoured source trees of anything I thought would shed some light (SDL, Allegro, wxWidgets, CxImage, GD, ImageMagick, etc.) but usually their code is either convoluted and scattered all over the place or riddled with assembler and little or no comments. I've also read multiple articles on Wikipedia and elsewhere, and I'm just not finding a clear explanation of what I need. I understand the basic concepts of interpolation and sampling, but I'm struggling to get the algorithm right. I do NOT want to rely on an external library for one routine and have to convert to their image format and back. Besides, I'd like to know how to do it myself anyway. :)
I have seen a similar question asked on stack overflow before, but it wasn't really answered in this way, but I'm hoping there's someone out there who can help nudge me in the right direction. Maybe point me to some articles or pseudo code... anything to help me learn and do.
Here's what I'm looking for:
No assembler (I'm writing very portable code for multiple processor types).
No dependencies on external libraries.
I am primarily concerned with scaling DOWN, but will also need to write a scale up routine later.
Quality of the result and clarity of the algorithm is most important (I can optimize it later).
My routine essentially takes the following form:
DrawScaled(uint32 *src, uint32 *dst,
src_x, src_y, src_w, src_h,
dst_x, dst_y, dst_w, dst_h );
Thanks!
UPDATE: To clarify, I need something more advanced than a box resample for downscaling which blurs the image too much. I suspect what I want is some kind of bicubic (or other) filter that is somewhat the reverse to a bicubic upscaling algorithm (i.e. each destination pixel is computed from all contributing source pixels combined with a weighting algorithm that keeps things sharp.
Example
Here's an example of what I'm getting from the wxWidgets BoxResample algorithm vs. what I want on a 256x256 bitmap scaled to 55x55.
www.free_image_hosting.net/uploads/1a25434e0b.png
And finally:
www.free_image_hosting.net/uploads/eec3065e2f.png
the original 256x256 image
I've found the wxWidgets implementation fairly straightforward to modify as required. It is all C++ so no problems with portability there. The only difference is that their implementation works with unsigned char arrays (which I find to be the easiest way to deal with images anyhow) with a byte order of RGB and the alpha component in a separate array.
If you refer to the "src/common/image.cpp" file in the wxWidgets source tree there is a down-sampler function which uses a box sampling method "wxImage::ResampleBox" and an up-scaler function called "wxImage::ResampleBicubic".
A fairly simple and decent algorithm to resample images is Bicubic interpolation, wikipedia alone has all the info you need to get this implemented.
Is it possible that OpenGL is doing the scaling in the vector domain? If so, there is no way that any pixel-based scaling is going to be near it in quality. This is the big advantage of vector based images.
The bicubic algorithm can be tuned for sharpness vs. artifacts - I'm trying to find a link, I'll edit it in when I do.
Edit: It was the Mitchell-Netravali work that I was thinking of, which is referenced at the bottom of this link:
http://www.cg.tuwien.ac.at/~theussl/DA/node11.html
You might also look into Lanczos resampling as an alternative to bicubic.
Now that I see your original image, I think that OpenGL is using a nearest neighbor algorithm. Not only is it the simplest possible way to resize, but it's also the quickest. The only downside is that it looks very rough if there's any detail in your original image.
The idea is to take evenly spaced samples from your original image; in your case, 55 out of 256, or one out of every 4.6545. Just round the number to get the pixel to choose.
Try using the Adobe Generic Image Library ( http://opensource.adobe.com/wiki/display/gil/Downloads ) if you want something ready and not only an algorithm.
Extract from: http://www.catenary.com/howto/enlarge.html#c
Enlarge or Reduce - the C Source Code
Requires Victor Image Processing Library for 32-bit Windows v 5.3 or higher.
int enlarge_or_reduce(imgdes *image1)
{
imgdes timage;
int dx, dy, rcode, pct = 83; // 83% percent of original size
// Allocate space for the new image
dx = (int)(((long)(image1->endx - image1->stx + 1)) * pct / 100);
dy = (int)(((long)(image1->endy - image1->sty + 1)) * pct / 100);
if((rcode = allocimage(&timage, dx, dy,
image1->bmh->biBitCount)) == NO_ERROR) {
// Resize Image into timage
if((rcode = resizeex(image1, &timage, 1)) == NO_ERROR) {
// Success, free source image
freeimage(image1);
// Assign timage to image1
copyimgdes(&timage, image1);
}
else // Error in resizing image, release timage memory
freeimage(&timage);
}
return(rcode);
}
This example resizes an image area and replaces the original image with the new image.
Intel has IPP libraries which provide high speed interpolation algorithms optimized for Intel family processors. It is very good but it is not free though. Take a look at the following link:
Intel IPP
A generic article from our beloved host: Better Image Resizing, discussing the relative qualities of various algorithms (and it links to another CodeProject article).
It sounds like what you're really having difficulty understanding is the discrete -> continuous -> discrete flow involved in properly resampling an image. A good tech report that might help give you the insight into this that you need is Alvy Ray Smith's A Pixel Is Not A Little Square.
Take a look at ImageMagick, which does all kinds of rescaling filters.
As a follow up, Jeremy Rudd posted this article above. It implements filtered two pass resizing. The sources are C# but it looks clear enough that I can port it to give it a try. I found very similar C code yesterday that was much harder to understand (very bad variable names). I got it to sort-of-work, but it was very slow and did not produce good results which led me to believe there was an error in my adaptation. I may have better luck writing it from scratch with this as a reference, which I'll try.
But considering how the two pass algorithm works I wonder if there isn't a faster way of doing it, perhaps even in one pass?