i'm work on graduation project for image forgery detection using CNN , Most of the paper i read before feed the data set to the network they Down scale the image size, i want to know how Does this process effect image information ?
Images are resized/rescaled to a specific size for a few reasons:
(1) It allows the user to set the input size to their network. When designing a CNN you need to know the shape (dimensions) of your data at each step; so, having a static input size is an easy way to make sure your network gets data of the shape it was designed to take.
(2) Using a full resolution image as the input to the network is very inefficient (super slow to compute).
(3) For most cases the features desired to be extracted/learned from an image are also present when downsampling the image. So in a way resizing an image to a smaller size will denoise the image, filtering out much of the unimportant features within the image for you.
Well you change the images size. Of course it changes it's information.
You cannot reduce image size without omitting information. Simple case: Throw away every second pixel to scale image to 50%.
Scaling up adds new pixels. In its simplest form you duplicate pixels, creating redundant information.
More complex solutions create new pixels (less or more) by averaging neighbouring pixels or interpolating between them.
Scaling up is reversible. It doesn't create nor destroy information.
Scaling down divides the amount of information by the square of the downscaling factor*. Upscaling after downscaling results in a blurred image.
(*This is true in a first approximation. If the image doesn't have high frequencies, they are not lost, hence no loss of information.)
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I need a dataset(image). So I downloaded images, for training purpose I resized images twice. From random sizes to (200,300), using that resized images I resized them again to (64,64). Is there any possibility that I can face problems while training. Does a picture loss it's data when resized again and again.
can u please explain me in detail. Thanks in advance
Images fundamentally lose their data when down sampling. If a pixel is the fundamental piece of data in an image and you remove pixels, then you have removed data. Different down sample methods lose different amounts of data. For instance a bilinear or bicubic down sample method will use multiple pixels in the larger image to generate a single pixel in the smaller image, whereas nearest neighbor downsampling uses a single pixel in the larger image to generate a single pixel in the smaller image, thereby losing more data.
Whether the down sampling will affect your training depends on more information than you have provided.
I have an image and a version that is scaled down to exactly half the width and height. The Lanczos filter (with a = 3) has been used to scale the image. Color spaces can be ignored, all colors are in a linear space.
Since the small image contains one pixel for each 2x2 pixel block of the original I'm thinking it should be possible to restore the original image from the small one with just 3 additional color values per 2x2 pixel block. However, I do not know how to calculate those 3 color values.
The original image has four times as much information as the scaled version. Using the original image I want to calculate the 3/4 of information that is missing in the scaled version such that I can use the scaled version and the calculated missing information to reconstruct the original image.
Consider the following use-case: Over a network you send the scaled image to a user as a thumbnail. Now the user wants to see the image at full size. How can we avoid repeating information that is already in the thumbnail? As far as I can tell progressive image compression algorithms do not manage to do this with more complex filtering.
For the box filter the problem is trivial. But since the kernels of the Lanczos filter overlap each other I do not know how to solve it. Given that this is just a linear system of equations I believe it is solvable. Additionally I would rather avoid deconvolution in frequency space.
How can I calculate the information that is missing in the down-scaled version and use it to restore the original image?
I would like to know if it is possible to take low resolution image from street camera, increase it
and see image details (for example a face, or car plate number). Is there any software that is able to do it?
Thank you.
example of image: http://imgur.com/9Jv7Wid
Possible? Yes. In existence? not to my knowledge.
What you are referring to is called super-resolution. The way it works, in theory, is that you combine multiple low resolution images, and then combine them to create a high-resolution image.
The way this works is that you essentially map each image onto all the others to form a stack, where the target portion of the image is all the same. This gets extremely complicated extremely fast as any distortion (e.g. movement of the target) will cause the images to differ dramatically, on the pixel level.
But, let's you have the images stacked and have removed the non-relevant pixels from the stack of images. You are left hopefully with a movie/stack of images that all show the exact same image, but with sub-pixel distortions. A sub-pixel distortion simply means that the target has moved somewhere inside the pixel, or has moved partially into the neighboring pixel.
You can't measure if the target has moved within the pixel, but you can detect if the target has moved partially into a neighboring pixel. You can do this by knowing that the target is going to give off X amount of photons, so if you see 1/4 of the photons in one pixel and 3/4 of the photons in the neighboring pixel you know it's approximate location, which is 3/4 in one pixel and 1/4 in the other. You then construct an image that has a resolution of these sub-pixels and place these sub-pixels in their proper place.
All of this gets very computationally intensive, and sometimes the images are just too low-resolution and have too much distortion from image to image to even create a meaningful stack of images. I did read a paper about a lab in a university being able to create high-resolution images form low-resolution images, but it was a very very tightly controlled experiment, where they moved the target precisely X amount from image to image and had a very precise camera (probably scientific grade, which is far more sensitive than any commercial grade security camera).
In essence to do this in the real world reliably you need to set up cameras in a very precise way and they need to be very accurate in a particular way, which is going to be expensive, so you are better off just putting in a better camera than relying on this very imprecise technique.
Actually it is possible to do super-resolution (SR) out of even a single low-resolution (LR) image! So you don't have to hassle taking many LR images with sub-pixel shifts to achieve that. The intuition behind such techniques is that natural scenes are full of many repettitive patterns that can be use to enahance the frequency content of similar patches (e.g. you can implement dictionary learning in your SR reconstruction technique to generate the high-resolution version). Sure the enhancment may not be as good as using many LR images but such technique is simpler and more practicle.
Photoshop would be your best bet. But know that you cannot reliably inclrease the size of an image without making the quality even worse.
Are there libraries, scripts or any techniques to increase image size in height and width....
or you must need to have a super good resolution image for it?.....
Bicubic interpolation is pretty much the best you're going to get when it comes to increasing image size while maintaining as much of the original detail as possible. It's not yet possible to work the actual magic that your question would require.
The Wikipedia link above is a pretty solid reference, but there was a question asked about how it works here on Stack Overflow: How does bicubic interpolation work?
This is the highest quality resampling algorithm that Photoshop (and other graphic software) offers. Generally, it's recommended that you use bicubic smoothing when you're increasing image size, and bicubic sharpening when you're reducing image size. Sharpening can produce an over-sharpened image when you are enlarging an image, so you need to be careful.
As far as libraries or scripts, it's difficult to recommend anything without knowing what language you're intending to do this in. But I can guarantee that there's an image processing library including this algorithm already around for any of the popular languages—I wouldn't advise reimplementing it yourself.
Increasing height & width of an image means one of two things:
i) You are increasing the physical size of the image (i.e. cm or inches), without touching its content.
ii) You are trying to increase the image pixel content (ie its resolution)
So:
(i) has to do with rendering. As the image physical size goes up, you are drawing larger pixels (the DPI goes down). Good if you want to look at the image from far away (sau on a really large screen). If look at it from up close, you are going to see mostly large dots.
(ii) Is just plainly impossible. Say your image is 100X100 pixels and you want to make 200x200. This means you start from 10,000 pixels, end up with 40,000... what are you going to put in the 30,000 new pixels? Whatever your answer, you are going to end up with 30,000 invented pixels and the image you get is going to be either fuzzier, or faker, and usually both. All the techniques that increase an image size use some sort of average among neighboring pixel values, which amounts to "fuzzier".
Cheers.
What is the efficient way to compare two images in visual c..?
Also in which format images has to be stored.(bmp, gif , jpeg.....)?
Please provide some suggestions
If the images you are trying to compare have distinctive characteristics that you are trying to differentiate then PCA is an excellent way to go. The question of what format of the file you need is irrelevant really; you need to load it into the program as an array of numbers and do analysis.
Your question opens a can of worms in terms of complexity.
If you want to compare two images to check if they are the same, then you need to perform an md5 on the file (removing possible metainfos which could distort your result).
If you want to compare if they look the same, then it's a completely different story altogether. "Look the same" is intended in a very loose meaning (e.g. they are exactly the same image but stored with two different file formats). For this, you need advanced algorithms, which will give you a probability for two images to be the same. Not being an expert in the field, I would perform the following "invented out of my head" algorithm:
take an arbitrary set of pixel points from the image.
for each pixel "grow" a polygon out of the surrounding pixels which are near in color (according to HSV colorspace)
do the same for the other image
for each polygon of one image, check the geometrical similitude with all the other polygons in the other image, and pick the highest value. Divide this value by the area of the polygon (to normalize).
create a vector out of the highest values obtained
the higher is the norm of this vector, the higher is the chance that the two images are the same.
This algorithm should be insensitive to color drift and image rotation. Maybe also scaling (you normalize against the area). But I restate: not an expert, there's probably much better, and it could make kittens cry.
I did something similar to detect movement from a MJPEG stream and record images only when movement occurs.
For each decoded image, I compared to the previous using the following method.
Resize the image to effectively thumbnail size (I resized fairly hi-res images down by a factor of ten
Compare the brightness of each pixel to the previous image and flag if it is much lighter or darker (threshold value 1)
Once you've done that for each pixel, you can use the count of different pixels to determine whether the image is the same or different (threshold value 2)
Then it was just a matter of tuning the two threshold values.
I did the comparisons using System.Drawing.Bitmap, but as my source images were jpg, there were some artifacting.
It's a nice simple way to compare images for differences if you're going to roll it yourself.
If you want to determine if 2 images are the same perceptually, I believe the best way to do it is using an Image Hashing algorithm. You'd compute the hash of both images and you'd be able to use the hashes to get a confidence rating of how much they match.
One that I've had some success with is pHash, though I don't know how easy it would be to use with Visual C. Searching for "Geometric Hashing" or "Image Hashing" might be helpful.
Testing for strict identity is simple: Just compare every pixel in source image A to the corresponding pixel value in image B. If all pixels are identical, the images are identical.
But I guess don't want this kind of strict identity. You probably want images to be "identical" even if certain transformations have been applied to image B. Examples for these transformations might be:
changing image brightness globally (for every pixel)
changing image brightness locally (for every pixel in a certain area)
changing image saturation golbally or locally
gamma correction
applying some kind of filter to the image (e.g. blurring, sharpening)
changing the size of the image
rotation
e.g. printing an image and scanning it again would probably include all of the above.
In a nutshell, you have to decide which transformations you want to treat as "identical" and then find image measures that are invariant to those transformations. (Alternatively, you could try to revert the translations, but that's not possible if the transformation removes information from the image, like e.g. blurring or clipping the image)