Very new to MatLab, just figuring some things out and had a question. I am basically trying to filter/blur an image using conv2() but I am getting an all white image when I am using imshow()
I am reading the image in with
testImage = imread('test.bmp');
This is a uint8 image of a grayscale.
I am trying to convolve the image with a 4 x 4 matrix.
fourByFour = ones(4);
When I actually execute, I am getting all white with imshow()
convolvedImage = conv2(testImage, fourByFour);
I should expect a filter placed on the image, not an entirely white one.
Any help would be appreciated.
I don't have your test image so I explain on an image. As the definition of conv2 it returns the two-dimensional convolution.
So please look at this little code:
clc;% clear the screen
clear all;% clear everything
close all;% close all figures
test = imread('test.bmp');
% read test image that is .bmp format and size :294x294x3 and uint8.
fourByFour = ones(4); % define 4 * 4 matrix all ones
convolvedImage = conv2(test(:,:,1), fourByFour);
%apply the ones matrix on image but to use conv2 on this image we apply on one of channels
figure, imshow(convolvedImage,[])
This is my command window, out put:
I'm using MAtlab 2017a, and if I use conv2(test, fourByFour); instead of conv2(test(:,:,1), fourByFour); ,the error is :
Error using conv2
N-D arrays are not supported.
So we should attention to class type and dimensions. And one more thing, in your command window please type edit conv2 you can read the details of this function and how to use it, but never edit it:). Thanks
test = imread('test.bmp');
% read test image that is .bmp format and size :294x294x3 and uint8.
fourByFour = ones(4);
% define 4 * 4 matrix all ones
test=rgb2gray(test);
% convert colorimage into gray
convolvedImage = conv2(double(test), double(fourByFour));
%perform the convolution
figure, imshow(convolvedImage,[])
% display the image
Related
I have written some lines of code using the following function:
adaptivethreshold(IM,ws,c)
and it gives me a Mask bw. I multiply this mask with my original image bb and show the result.
clear
clc
bb=dicomread('30421573');
figure(1)
imagesc(bb)
bw=adaptivethreshold(bb,50,128);
imaa=double(bw).*double(bb);
figure(2)
image(imaa)
the original Image and the result are shown:
It does not seem to be giving me any mask for my image. Is there any way I can extract those yellow parts from my results?
Try to create the mask before applying to the image, i.e.
bw=adaptivethreshold(bb,50,128);
BW = imbinarize(bb,bw);
imaa=double(bw).*double(BW);
figure(2)
image(imaa)
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.
so I'm using the imread function in matlab and when I save the TIFF file and open it in photoshop, it has a white border and I can't understand why. I want to maintain its resolution as a 512 by 512 image. Any ideas why? And how I can fix that?
Here's a sample code:
B = imread('W_noise1.tif');
for n = 1:5,
B = medfilt2(B);
end
B = filter2(fspecial('average',3),B)/255;
imshow(B)
Are you sure it's an issue with imread? I'd be surprised if it is.
See this link about medfilt2 where it explains that "medfilt2 pads the image with 0s on the edges, so the median values for the points within [m n]/2 of the edges might appear distorted."
EDIT: I tried to replicate your problem. This is an issue with print where it puts a white frame around the image after you save it. This functionality, print is made for printing plots. If you want to save the image, you should use imwrite.
How can I use the qtdecomp(Image,threshold) function in MATLAB to find a quadtree decomposition of an RGB image?
I tried this:
Ig = rgb2gray(I); % I is the rgb image
S = qtdecomp(I,.27);
but I get this error:
??? Error using ==> qtdecomp>ParseInputs
A must be two-dimensional
Error in ==> qtdecomp at 88
[A, func, params, minDim, maxDim] = ParseInputs(varargin{:});
Also I get this error:
??? Error using ==> qtdecomp>ParseInputs
Size of A is not a multiple of the maximum block dimension
Can someone tell me how can I do it?
One obvious error... In the code above you are still passing the original RGB image I to the QTDECOMP function. You have to pass Ig instead:
S = qtdecomp(Ig,.27);
There are two problem with the code in your original post:
1) The first error is because you need to supply Ig, the greyscale version of your image to the qtdecomp function, rather than the colour version I:
S = qtdecomp(Ig, .27);
2) The second error is because for the function qtdecomp, the image's size needs to be square and a power of 2. I suggest you resize the image in an image editor. For example, if your image is 1500x1300, you probably want to resize or crop it to 1024x1024, or perhaps 2048x2048.
You can find the size of the greyscale version of your image with this MATLAB command:
size(Ig)
To crop it to the 1024x1024 in the top-left corner, you can run this MATLAB command:
Ig = Ig(1:1024, 1:1024);