If I resize an image in MATLAB, what effect will that have on the original image. Say for instance I have an image of size 437x167 and I want to resize it to 256x256. Will there be any details that will go missing from such operation?
Thanks.
if i understand you correctly you mean: "what are the results on an image after using matlab's built in function imresize?" by looking at the documentation of imresize you can see that you can choose what algorithm you want to use through the parameters of imresize. the best answer can be found by googeling the respective algorithms and reading what is the data loss caused by each of these algorithms
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I am new to image processsing i want to detect crack please any one can help me.
enter image description here
From the description that you're presenting, I believe that the best way to do this is by using a binary mask with the cv2.binary_and() function.
You can do this color segmentation by using 2 thresholds that are going to be the minimum and the maximum color values for the color of the cracks.
Another sollution may be the usage of the Otsu's threshold method. This probably will generate the best values for your mask.
After the masking of the image, you'll have to try to create the contours of those cracks in the image. You can use the cv2.findContours() function. (Check this link that describes the way you can implement this function)
Example Image
For example as Image above, area I need is just like red box, and other section doesn't have any labels for classification/object detection.
What I think is "If I use cropped image to red box will occur better effect" because there's to much useless area without labels in original image. When mosaic augmentation used in yolo v4, It will put images together in one. And, because there's so many area without labels, data after mosaic can be useless than before.
But, This is just my guess, and I need a test to confirm it, but the lack of computing power is limiting the actual test. So the question is, Is it possible to actually improve performance if the original image is cropped in the form of a red box? Is that why I guessed correctly?
Also, my partner said that cropping is not a good choice in Yolo because it can ruin the proportion of the object, but I couldn't understand what the proportion of the object meant in Yolo. I wonder why the proportion of objects in Yolo is not suit with cropping.
Thanks for read, and have a nice day
simply you shouldn't resize the images, however, if the training/testing data set contains considerable difference among width and heights, use the data augmentation methods. Pls, follow the link for more information.
https://github.com/pjreddie/darknet/issues/800
I have a set of synthetically noisy images. Example is shown below:
I have also their corresponding clean text images as my ground truth data. Example below:
The dimension size of the two images is 4918 x 5856. Is it an appropriate size for training my Convolutional Neural Network that will perform image denoising. If no, what shall I do? Resize or crop? Thanks.
This resolution really is overkill. You can start off with 1/64 of the size ~(600,750), which is already pretty big.
I was facing this problem recently as well. I learned that you need to crop the image into patches, each of about 500x500. Then you need to denoise each patch and put it all together. This usually gets the most accurate results. Let me know if you need anything else!
I have a set of images that I am using for a typical classification problem using Tensorflow. The images come in different sizes so I wrote a small piece of code to resize them all. But the question is what is the best strategy of resizing for training purposes? For example, is it better to resize them, no matter how they scale up or down, or it is better to keep the aspect ratio and add some artificial zero padding around the resized images? I believe this is a typical question with some existing studies or solutions. Appreciate your advice.
Regards,
Hamid
I need to remove the blur this image:
Image source: http://www.flickr.com/photos/63036721#N02/5733034767/
Any Ideas?
Although previous answers are right when they say that you can't recover lost information, you could investigate a little and make a few guesses.
I downloaded your image in what seems to be the original size (75x75) and you can see here a zoomed segment (one little square = one pixel)
It seems a pretty linear grayscale! Let's verify it by plotting the intensities of the central row. In Mathematica:
ListLinePlot[First /# ImageData[i][[38]][[1 ;; 15]]]
So, it is effectively linear, starting at zero and ending at one.
So you may guess it was originally a B&W image, linearly blurred.
The easiest way to deblur that (not always giving good results, but enough in your case) is to binarize the image with a 0.5 threshold. Like this:
And this is a possible way. Just remember we are guessing a lot here!
HTH!
You cannot generally retrieve missing information.
If you know what it is an image of, in this case a Gaussian or Airy profile then it's probably an out of focus image of a point source - you can determine the characteristics of the point.
Another technique is to try and determine the character tics of the blurring - especially if you have many images form the same blurred system. Then iteratively create a possible source image, blur it by that convolution and compare it to the blurred image.
This is the general technique used to make radio astronomy source maps (images) and was used for the flawed Hubble Space Telescope images
When working with images one of the most common things is to use a convolution filter. There is a "sharpen" filter that does what it can to remove blur from an image. An example of a sharpen filter can be found here:
http://www.panoramafactory.com/sharpness/sharpness.html
Some programs like matlab make convolution really easy: conv2(A,B)
And most nice photo editing have the filters under some name or another (sharpen usually).
But keep in mind that filters can only do so much. In theory, the actual information has been lost by the blurring process and it is impossible to perfectly reconstruct the initial image (no matter what TV will lead you to believe).
In this case it seems like you have a very simple image with only black and white. Knowing this about your image you could always use a simple threshold. Set everything above a certain threshold to white, and everything below to black. Once again most photo editing software makes this really easy.
You cannot retrieve missing information, but under certain assumptions you can sharpen.
Try unsharp masking.