I am new to Image processing and have learnt only few bits using Octave + net resources.
I am trying to subtract two images A & B. A contains noise and B doesn't. Both of these images are gray (blank shaded of gray white image) and the noise looks like a a few dark shaded pixels runnnig horizontally in image A which can be distinguished visually.
Using imsubtract method deosn't give me the result I want, so I am looking for advanced techniques / steps that can help this issue.
The noise on the image is due to flicker on LCD screen which is captured by camera. I also plan to apply this subtraction technique(s) on the video of under same conditions.
In tutorials I found people subtracting videos but those are rich in color, my input image is lacks color. But definitely running through pixel values will result in some differences.
Aany ideas? Am I thinking correctly about this problem by utlizing FFT and pixel subtraction based techniques?
p.s. Apologies if this is not the right forum.
Related
I am a new user on image processing via Matlab. My first aim is applying the article and comparing my results and authors' results.
The article can be found here: http://arxiv.org/ftp/arxiv/papers/1306/1306.0139.pdf
First problem, Image Quality: In Figure 7, masks are defined but I couldn't reach the mask data set, and I use the screenshot so image quality is low. In my view, it can effect the results. Is there any suggestions?
Second problem, Merging images: I want to apply mask 1 on the Lena. But I don't want to use paint =) On the other hand, is it possible merging the images and keeping the lena?
You need to create the mask array. The first step is probably to turn your captured image from Figure 7 into a black and white image:
Mask = im2bw(Figure7, 0.5);
Now the background (white) is all 1 and the black line (or text) is 0.
Let's make sure your image of Lena that you got from imread is actually grayscale:
LenaGray = rgb2gray(Lena);
Finally, apply your mask on Lena:
LenaAndMask = LenaGray.*Mask;
Of course, this last line won't work if Lena and Figure7 don't have the same size, but this should be an easy fix.
First of all, You have to know that this paper is published in archive. when papers published in archive it is always a good idea to know more about the author and/or the university that published the paper.
TRUST me on that: you do not need to waste your time on this paper.
I understand your demand: but it is not a good idea to do get the mask by doing print screen. The pixel values that can be achieved by using print screen may not be the same as the original values. The zoom may change the size. so you need to be sure that the sizes are the same.
you can do print screen. past the image.
crop the mask.
convert rgb to gray scale.
threshold the gray scale to get the binary.
if you saved the image as jpeg. distortions because of high frequency edges will change edge shape.
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.
My use case is to detect the angle a black&white scanned image is rotated. Most image processing algorithms I am finding online as a first step do a bitwise not on the image so that the background is black and the objects white.
My questions is what is the reason behind this? I cannot find any other answers that this is how it is done in image processing.
Thank you.
I have come across two reasons for doing this inversion:
1) Inverted image sometimes gives better interpretation (eg. in medical image analysis)
2) When background region of the image capture (black in colour) is needed for segmentation as mentioned in this problem
Problems with line detection in Emgu CV
I'm trying to get an image of a blackboard readable by OCR. Naturally, most OCR software doesn't like dirty images. What image processing should I try to put the image through to clean the image up?
Have you tried the OCR software yet? It's likely that the OCR software is well suited to reading what's essentially already a black and white image.
However, if you were required to do so you could try to:
Threshold the image.
Essentially take a greyscale version of the image and turn it into black / white pixels
Perform Binary Dilation to grow the remaining objects
Perform Binary Erosion
The idea is by dilating then eroding you would remove any rough / noisy edges and then you can pass the skeletonized image to the OCR.
There are probably plenty of methods to achieve a similar result. Given that there are entire books devoted to computer vision this answer will hardly do them justice.
The only texts I have are from 1997, but surely there's been more written on the subject since.
Algorithms for Image Processing and Computer Vision - J.R. Parker
Digital Image Processing - Gonzalez / Woods
Offhand, I'd say invert the image (reverse the colors, so that the writing is black on white) and increase the contrast a bit. You can try modifying the brightness to get the erased chalk fogginess to disappear into the background.
In Photoshop, the Levels dialog may be your most useful image adjustment. Mimicking this in code is another subject, entirely.
The basis of Levels is that you adjust the max, min and midpoints of the brightness levels. Usually shown on a histogram, you adjust the points such that you obtain the desired amount of contrast, but also move the midpoint such that text in the image is the most well-defined; critical for OCR applications. By moving the midpoint you can "eliminate" the grayscale fuzz that ordinarily surrounds handwriting by causing it to disappear into the light (or dark) areas of the image.
Also you might try converting the image to 1-bit after such an adjustment, forcing everything to black or white. Sometimes this speeds up the OCR process. But be careful, it also will discard detail.
Have you tried edge detection techniques such as Roberts Cross and Sobel operator to filter noise out of the image? Without seeing the quality of the image, can't say how effective that'd be.
Not sure how constrained you are in the choice of OCR solution, but the ABBYY OCR engine (and a web API based on it, http://www.wisetrend.com/wisetrend_ocr_cloud.shtml ) includes automatic image cleanup / texture removal options.
There are commercial solutions but cleaning up board images appears to be an open problem. Add OCR to an unsolved problem, and you get... an unsolved problem.