In Corel PhotoPaint, when you overlay two images using the "Subtract" mode instead of "Normal", you will get more saturated, "neater" colors in the darker areas from the top image. Does anyone know what the algorithm behind this overlaying method is? For instance, I'm looking into emulating it in Objective-C as well as PHP.
For comparison, I created an overlay image of a blurred black center circle which in the top, uses the Normal overlaying mode, and in the bottom, uses the Subtract mode. The normal mode will cause the resulting darker area to look much more gray.
Normal
Subtract
Exporting this CPT file to PSD and opening in Photoshop, the Subtract mode is not available and is lost, so I'm not even sure what it's called in Photoshop.
Thanks for any help! (Original photo CC-licensed by iPyo.)
When combining two images you will have varying options to do so. The general algorithm for such a combination is
for each pixel in resultImage
resultImage[pixel] = sourceA[pixel] OP sourceB[pixel]
Well and then you choose OP. In your questions case thats a '-' (subtraction).
But it can be also +,*,/, MOD, DIV etc.
Usually you will also want to perform some kind of range checking so the pixel intensities of your result image won't over- or underflow. But well then you also might want to do such a thing intentionally.
Related
I am making an image processing project which has a few steps and stuck in one of them. Here is the thing; I have segmented an image and subtract the foreground from background. Now, I need to fill the background.
So far, I have tried the inpainting algorithms. They don't work in my case because my background images haven't at least 40% of them. I mean they fail when they are trying the complete 40% of an image. (By the way, these images have given bad results even in the Photoshop with content-aware tool.)
Anyway, I've given up trying inpainting and decided something else. In my project, I don't need to complete 100% of my background. I want to illustrate my solution;
As you see in the image above, I want to pull the image to the black area (which is transparent) with minimum corruption. Any MATLAB code samples, technique, keyword and approach would be great. If you need further explanation, feel free to ask.
I can think of two crude ways to fill the hole:
use roifill: this fills gaps in 2d image preserving image smoothness.
Alteratively, you can use bwdist to compute the nearest neighbor of each black pixel and assign it to its nearest neighbor's color:
[~, nnIdx] = bwdist( bw );
fillImg(bw) = IMG(bw);
although this code snippet works only for gray images, it is quite trivial to extend it to RGB color images.
Currently I am involved in an image processing project where I am dealing with human faces. But I am facing problems with the images in cases where the light source is on either the left or right side of the face. In those cases, the portion of the image away from the light source is darker. I want to distribute the brightness over the image more evenly, so that the the brightness of darker pixels is increased and the brightness of overly bright pixels is decreased at the same time.
I had used 'gamma correction' techniques to do the same but the results are not desirable , Actually I want to create an output in which the brightness is independent of the light source, in other words , increasing the brightness of the darker part and decreasing the brightness of the brighter part. I am not sure if I reproduced the problem statement correctly but this is a very common problem and I haven't found anything useful abut this on the web.
1. Image with Light source on the right side
2. Image after increasing the brightness of the darker pixels.[img = cv2.pow(img, 0.5)]
3. Image after decreasing the brightness of Bright pixels[img = cv2.pow(img, 2.0)]
I was thinking of taking the mean of both the images 2 and 3 but as we see that the over bright pixels still persist in the image 3 , and I want to get rid of that pixels, Any suggestion ?
In the end I need an image with homogeneous brightness, and independent of the light source.
Take a look at homomorphic filtering applied to image enhancement in which you can selectively filter reflectance and illumination components of an image.
I found this paper: http://www.mirlab.org/conference_papers/International_Conference/ICASSP%202010/pdfs/0001374.pdf i think it exactly addresses the concern you have.
you will need to compute "gradient" of an image i.e. laplacian derivatives for which you can read up on this: http://docs.opencv.org/trunk/doc/py_tutorials/py_imgproc/py_gradients/py_gradients.html
i'd be very interested to know about your implementation. if you run into trouble post a comment here and i can try to help.
I want a formula to detect/calculate the change in visible luminosity in a part of the image,provided i can calculate the RGB, HSV, HSL and CMYK color spaces.
E.g: In the above picture we will notice that the left side of the image is more bright when compared to the right side , which is beneath a shade.
I have had a little think about this, and done some experiments in Photoshop, though you could just as well use ImageMagick which is free. Here is what I came up with.
Step 1 - Convert to Lab mode and discard the a and b channels since the Lightness channel holds most of the brightness information which, ultimately, is what we are looking for.
Step 2 - Stretch the contrast of the remaining L channel (using Levels) to accentuate the variation.
Step 3 - Perform a Gaussian blur on the image to remove local, high frequency variations in the image. I think I used 10-15 pixels radius.
Step 4 - Turn on the Histogram window and take a single row marquee and watch the histogram change as different rows are selected.
Step 5 - Look out for a strongly bimodal histogram (two distimct peaks) to identify the illumination variations.
This is not a complete, general purpose solution, but may hold some pointers and cause people who know better to suggest improvememnts for you!!! Note that the method requires the image to have a some areas of high uniformity like the whiteish horizontal bar across your input image. However, nearly any algorithm is going to have a hard time telling the difference between a sheet of white paper with a shadow of uneven light across it and the same sheet of paper with a grey sheet of paper laid on top of it...
In the images below, I have superimposed the histogram top right. In the first one, you can see the histogram is not narrow and bimodal because the dotted horizontal selection marquee is across the bar-code area of the image.
In the subsequent images, you can see a strong bimodal histogram because the dotted selection marquee is across a uniform area of image.
The first problem is in "visible luminosity". It me mean one of several things. This discussion should be a good start. (Yes, it has incomplete and contradictory answers, as well.)
Formula to determine brightness of RGB color
You should make sure you operate on the linear image which does not have any gamma correction applied to it. AFAIK Photoshop does not degamma and regamma images during filtering, which may produce erroneous results. It all depends on how accurate results you want. Photoshop wants things to look good, not be precise.
In principle you should first pick a formula to convert your RGB values to some luminosity value which fits your use. Then you have a single-channel image which you'll need to filter with a Gaussian filter, sliding average, or some other suitable filter. Unfortunately, this may require special tools as photoshop/gimp/etc. type programs tend to cut corners.
But then there is one thing you would probably like to consider. If you have an even brightness gradient across an image, the eye is happy and does not perceive it. Rather large differences go unnoticed if the contrast in the image is constant across the image. Unfortunately, the definition of contrast is not very meaningful if you do not know at least something about the content of the image. (If you have scanned/photographed documents, then the contrast is clearly between ink and paper.) In your sample image the brightness changes quite abruptly, which makes the change visible.
Just to show you how strange the human vision is in determining "brightness", see the classical checker shadow illusion:
http://en.wikipedia.org/wiki/Checker_shadow_illusion
So, my impression is that talking about the conversion formulae is probably the second or third step in the process of finding suitable image processing methods. The first step would be to try to define the problem in more detail. What do you want to accomplish?
i'm interested in some kind of charcoal-filters like the photoshop Photocopy-Filter or the note-paper.
Have someone a paper or some instructions how this filter works?
In best case i want to create the following:
input:
Output:
greetings
I think it's a process akin to pan-sharpening. I could get a quite similar image in gimp by:
Converting to gray
Duplicating into two layers
Lightly blurring one layer
Edge-detecting in the other layer with a DOG filter with large radius
Compositing the two layers, playing a bit with the transparency.
What this is doing is converting the color picture into a 0-1 bitmap picture.
They typically use a threshold function which returns 1 (white) for some values and 0 (black) for some other.
One simple function would be transform the image from color to gray-scale, and then select a shade of gray above which everything is white, and below it everything is black. The actual threshold you use could be made adaptive depending on the brightness of the picture (you want a certain percentage of pixels to be white).
It can also be adaptive based on the context within the picture (i.e. a dark area may still have some white pixels to show local contrast). The trees behind the house are not all black because the filtering is sensitive to the average darkness of the region.
Also note that the area close to the light gap in the tree has a cluster of dark pixels, because of its relative darkness. The edges of the home, the bench are also highlighted. There is an edge detection element at play.
I do not know exactly what effect you gave an example of but there are a variety that are similar to it. As VSOverFlow pointed out, thresholding an image would result in something very similar to that though I do not think it is what is being used. Open cv has a function for this, its documentation can be found here. You may also want to look into Otsu's method for thresholding.
Again as VSOverFlow pointed out, there is an edge detection element at play as well. You may want to investigate the Sobel and Prewitt filters. Those are 3 simple options that will give you something similar to the image you provided. Perhaps you could threshold the result from the Prewitt filter? I have no knowledge of how Photoshop implements its filters. If none of these options are close enough to what you are looking for I would recommend looking for information on the specific implementations of those filters in photoshop.
I have a site that I have built, with a colour scheme based around the companies graphic. This has a gradient from left to right, fading to white. I use the image at the top of the pages.
To provide the same gradient down the page, I took a pixel or two wide cut from the bottom of this image, which I them repeat down my page. This works perfectly well, giving me the right gradient across my screen.
In firefox, there is no break between the bottom of my header image and the top of my repeated background gradient - which is as expected. The colours match, so they should appear continuous. However, in IE7, the top image appears very slightly lighter than the rest of the background. I initially thought that this was because I think IE does not always render style colours correctly, but then I realised that it is not a style, it is an image. I cannot understand why these two images are being rendered differently.
Unfortunately, because of who my client is, I cannot include pictures, and I accept that this will make it harder for anyone to answer, but if there are any suggestions, I would love to know why this is happening.
Thank you.
Try saving them in a different format. I think this has to do wkth color calibration performed in certain computers (can be set up in Windows fo example, but as I am writing this on the go from my mobile I cant/wont do more research). I think JPEG des not care about this setting and PNG do, or the other way around. Someone else can probably describe it way better..