I try to write a matlab function that upsamples me a picture (matrix of grey values). It is actually nothing overwhelmingly complicated, but I yet manage to do it wrong.
My objective is it to resize it by factor 2 and for the start I just want to see my upscaled picture. I want to fill the gaps with zeros, hence every 2nd row/column is a filled with zeros.
When I am done, I wonder why I see nothing but a grey ocean of pixels. I would have expected to be able to recognize at least some stuff in my picture.
Here is my function, does anyone see my mistake?
function [upsampled] = do_my_upsampling(image)
[X Y] = size(image);
upsampled = zeros(X*2, Y*2);
upsampled(1:2:end, 1:2:end) = image(1:1:end, 1:1:end);
end
Your code works fine for me (with image = rand(100);. However, it's not a very Matlab-way to achieve the result.
If you just want to spread out your pixels, why don't you do direct indexing?
[nRows,nCols] = size(image);
upsampled = zeros(2*nRows,2*nCols);
upsampled(1:2:end,1:2:end) = image;
Try imshow(image,[])
or, as your image is a double, convert it into uint8 first and then show i.e
imshow(uint8(image))
Related
I need to combine several images (of different textures) together. I have tried the following code:
% Read 4d data
I1 = importdata('Img1.tif');
I2 = importdata('Img2.tif');
% Extract a slice of the data
extractImg1 = I1(:,:,1);
extractImg2 = I2(:,:,1);
% compute image size
[ny1, nx1] = size(extractA1);
[ny2, nx2] = size(extractA2);
P1 = extractImg1 (round(ny1/2)-120:round(ny1/2)+120, round(nx1/2)-120:round(nx1/2)+120);
figure, imshow(P1); title('Img1');
P2 = extractImg2 (round(ny2/2)-120:round(ny2/2)+120, round(nx2/2)-120:round(nx2/2)+120);
figure, imshow(P2); title('Img2');
Please, what should I do next?
Secondly, the combined image will be needed for laser printing. The images do not have exactly the same pixel dimensions thus; I was told that it would not make sense to combine them, as this might slightly reduce accuracy.
Nonetheless, I still have a feeling that combining the images wouldn’t be wrong considering that they all have the same resolutions.
I need advice as to whether I should go ahead with the combination. Many thanks in advance.
You have extracted two equal-sized regions from the two images. If you want to put those side-by-side in the same image, use cat, or equivalently, use the square brackets []:
next_to_each_other = [P1,P2];
on_top_of_each_other = [P1;P2];
But note that you can put these things together even if they don't have the same sizes. For example, if I1 is NxM pixels, and I2 is NxK (with N the vertical size as customary in MATLAB) then you can still do [I1,I2] because the vertical size matches.
If nether the vertical nor horizontal sizes match, you can pad one with zeros (or whatever value is appropriate) using padarray before putting them together:
ny1 = size(I1,1);
ny2 = size(I2,1);
if ny1<ny2
I1 = padarray(I1,[ny2-ny1,0,0],0,'post'); % The 0 is the value to pad
elseif ny2<ny1
I2 = padarray(I2,[ny1-ny2,0,0],0,'post'); % The 0 is the value to pad
end
out = [I1,I2];
padarray also allows replicating the data in the matrix instead of padding with zeros. Read the documentation to find what is appropriate. padarray requires the Image Processing Toolbox. If you don't have it, you can replicate its functionality by creating an array with zeros of the appropriate size using the zeros function, and adding it to the image using something like [I1;zeros(ny2-ny1,size(I1,2),size(I1,3)].
I have coloured images of size 20 by 20. The objective is that :
based on a query, I need to check which recalled image is the closest match to the query. For example, let 10 images be the recalled ones. Out of the 10 recalled images, I need to find the closest match to the query.
I was thinking of using the correlation between the images. One can use the Correlation coefficient - higher the value, more is the correlation between pixel values. (Please correct me where wrong).
R = corr2(A,B1) will compute the correlation coefficient between A and B where A is the query, B1 is the first recalled image image of the same size. I used the above command for colored images but I got the result R = NaN. How do I solve this problem for colored and gray scale. Thank you.
This is the QUERY IMAGE
The other image(recalled / retrieved B1)
UPDATE : Code for testing correlation of an image with itself from a databasae called patches.mat (patches is the database. It consists of 59500 20x20 patches, reshaped into 400-dimensional column vectors,
taken from a bunch of motorcycle images; I am using the 50 th example as the query image)
img_query = imagesc(reshape(patches(:,50),20,20));
colormap gray;axis image;
R = corr2(img_query,img_query)
Answer = NaN
That is because one of the images is black or includes a single color, meaning that all the values of the matrix are similar. Check the following examples:
I = imread('pout.tif');
J = I*0; % create a black image
R = corr2(I,J);
R =
NaN
I = imread('pout.tif');
J = 255*ones(size(I)); % create a white image
R = corr2(I,J);
R =
NaN
Update
It should work in your case, as you can see in the following example, it works perfectly:
I1 = abs(255*(rand(10,10));
I2 = abs(255*(rand(10,10));
corr2(I1,I2)
ans =
0.0713
Even with the images you have shared, it is working for me. To find out your problem, you have to either share a part of your code, or post images as they are, not saved images (with a size 420x560x3).
Note: you cannot have images including more than 1 layer.
Your code shows that you are using the handle of the image instead of the image itself.
Test this:
I = reshape(patches(:,50),20,20);
corr2(I,I)
I would try also the Universal Quality Index by Wang which gives you a 1.0 if both images are equal, and less in other cases with spatial indication where they differ, think of one image as reference, the other as a degraded one relative to the first one. See http://www.cns.nyu.edu/~zwang/files/research/quality_index/demo.html
Here is the original image with better vision: we can see a lot of noise around the main skeleton, the circle thing, which I want to remove them, and do not affect the main skeleton. I'm not sure if it called noise
I'm doing it to deblurring a image, and this image is the motion blur kernel which identify the camera motion when the camera capture a image.
ps: this image is the kernel for one case, and what I need is a general method in here. thank you for your help
there is a paper in CVPR2014 named "Separable Kernel for Image Deblurring" which talk about this, I want to extract main skeleton of the image to make the kernel more robust, sorry for the explaination here as my English is not good
and here is the ture grayscale image:
I want it to be like this:
How can I do it using Matlab?
here are some other kernel images:
As #rayryeng well explained, median filtering is the best option to clean noise in the image, which I realized when I had studied about image restoration. However, in your case, what you need to do seems to me not cleaning noise in the image. You want to more likely eliminate the sparks in the image.
Simply I applied single thresholding to your noisy image to eliminate sparks.
Try this:
desIm = imread('http://i.stack.imgur.com/jyYUx.png'); % // Your expected (desired) image
nIm = imread('http://i.stack.imgur.com/pXO0p.png'); % // Your original image
nImgray = rgb2gray(nIm);
T = graythresh(nImgray)*255; % // Thereshold value
S = size(nImgray);
R = zeros(S) + 5; % // Your expected image bluish so I try to close it
G = zeros(S) + 3; % // Your expected image bluish so I try to close it
B = zeros(S) + 20; % // Your expected image bluish so I try to close it
logInd = nImgray > T; % // Logical index of pixel exclude spark component
R(logInd) = nImgray(logInd); % // Get original pixels without sparks
G(logInd) = nImgray(logInd); % // Get original pixels without sparks
B(logInd) = nImgray(logInd); % // Get original pixels without sparks
rgbImage = cat(3, R, G, B); % // Concatenating Red Green Blue channels
figure,
subplot(1, 3, 1)
imshow(nIm); title('Original Image');
subplot(1, 3, 2)
imshow(desIm); title('Desired Image');
subplot(1, 3, 3)
imshow(uint8(rgbImage)); title('Restoration Result');
What I got is:
The only thing I can see that is different between the two images is that there is some quantization noise / error around the perimeter of the object. This resembles salt and pepper noise and the best way to remove that noise is to use median filtering. The median filter basically analyzes local overlapping pixel neighbourhoods in your image, sorts the intensities and chooses the median value as the output for each pixel neighbourhood. Salt and pepper noise corrupts image pixels by randomly selecting pixels and setting their intensities to either black (pepper) or white (salt). By employing the median filter, sorting the intensities puts these noisy pixels at the lower and higher ends and by choosing the median, you would get the best intensity that could have possibly been there.
To do median filtering in MATLAB, use the medfilt2 function. This is assuming you have the Image Processing Toolbox installed. If you don't, then what I am proposing won't work. Assuming that you do have it, you would call it in the following way:
out = medfilt2(im, [M N]);
im would be the image loaded in imread and M and N are the rows and columns of the size of the pixel neighbourhood you want to analyze. By choosing a 7 x 7 pixel neighbourhood (i.e. M = N = 7), and reading your image directly from StackOverflow, this is the result I get:
Compare this image with your original one:
If you also look at your desired output, this more or less mimics what you want.
Also, the code I used was the following... only three lines!
im = rgb2gray(imread('http://i.stack.imgur.com/pXO0p.png'));
out = medfilt2(im, [7 7]);
imshow(out);
The first line I had to convert your image into grayscale because the original image was in fact RGB. I had to use rgb2gray to do that. The second line performs median filtering on your image with a 7 x 7 neighbourhood and the final line shows the image in a separate window with imshow.
Want to implement median filtering yourself?
If you want to get an idea of how to actually write a median filtering algorithm yourself, check out my recent post here. A question poser asked to implement the filtering mechanism without using medfilt2, and I provided an answer.
Matlab Median Filter Code
Hope this helps.
Good luck!
i'm trying to perform mean filtering on an image in Matlab, i have to make it modular so i have another function for my averaging and then my script calls the function. I run the script and there are no errors but it doesn't seem to do the filtering as the output of the image is no different to the original. Can anyone see where i am going wrong?
%input image
image1 = imread('moon.jpg');
%convert to grayscale
%mean filtering
mean = averagefilter2(image1);
image_grey = rgb2gray(mean);
figure;
imshow(image_grey);
%my average filter function%
function img=averagefilter2(image1)
meanFilter = fspecial('average',[3 3]);
img = imfilter (image1,meanFilter);
end
Thanks!
Can you show your figures of image1 and image_grey? You can also try to imagesc(abs(image1-image_grey)) to see the difference between the original image and the averaged one. I ran your code and didn't see problems. I can observed the smooth effect in my sample image.
Remember to apply rgb2gray to your image1 as well.
Is it possible to compare the color of two images using Matlab if the two images are of different sizes?The problem that am facing is that, i want to detect the presence of a colored patch in an image?
You could just compare normalized histograms (i.e., like a color probability distribution). If the large and small images are semantically identical, then their normalized histograms are similar.
If they are semantically different, then their histograms will likely differ.
Do you have the image processing toolbox? If so, you might approach the problem by taking the images, splitting them into their component color channels, resizing the individual channels, and re-assembling them into resized color images. I wrote a program to do that a while ago, and I recall the code looking something like this:
function imout = ResizeRGB(imin,height,width)
imout = zeros(height,width,3);
iminR = imin(:,:,1);
iminG = imin(:,:,2);
iminB = imin(:,:,3);
imoutR = imresize(iminR, [height width]);
imoutG = imresize(iminG, [height width]);
imoutB = imresize(iminB, [height width]);
imout(:,:,1) = imoutR;
imout(:,:,2) = imoutG;
imout(:,:,3) = imoutB;
(Since I don't have the IPT handy at the moment, that program should be considered pseudocode even though it's more or less in correct matlab syntax. I can't run it without the IPT, so I can't tell if it's buggy or not.)
Once you resize the images so that they have common dimensions, the problem becomes identical to the problem of comparing colors for two images of equal dimensions.
On the other hand, if you have a picture of the patch and a picture that may contain the patch, you might consider using a binary mask to threshold the results of a cross-correlation (xcorr2 in the IPT). For more information on that approach, there's a tutorial on the MathWorks website: http://www.mathworks.com/products/demos/image/cross_correlation/imreg.html
This would be a bit crude, but you could crop your images to the minimum common size if this will be sufficient for your application:
A = imread("image1.jpg");
B = imread("image2.jpg");
rows = min(size(A,1), size(B,1));
cols = min(size(A,2), size(B,2));
croppedA = A(1:rows, 1:cols, :);
croppedB = B(1:rows, 1:cols, :);