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.
Related
I'm trying to create a model that segment various part of an aerial image.
I'm using a dataset found in kaggle: https://www.kaggle.com/datasets/bulentsiyah/semantic-drone-dataset
My question regards about the right way of treat images for semantic segmentation.
In this case is it better to simply resize the images (e.g. 6000x4000 to 256x256 pixel) or is it better to resize them less but then create patches from it (e.g. 6000x4000 to 1024x1024 pixel and then patches in 256x256 pixel).
I think that resizing too much an image may cause the loss of information but at the same time patching could not guarantee a full view of the image.
I also found a notebook that got 96% accuracy just by resizing so i'm not sure how to proceed:
https://www.kaggle.com/code/yesa911/aerial-semantic-segmentation-96-acc/notebook
I think there is not one correct answer to this. Dependant on the amount and size of the areas you want to segmentate, it seems unlikely to get a proper/accurate segemantion with images of your size. However, if there are only easy detectable and big areas in the image I would definetly go for the approach without patches, since the patch-approach is way more complex as it has more variables to consider (size of patches, overlapping patches, edge treatment). It would save you a lot of implementation time for preprocessing and stichting afterwards.
TLDR: I would start without patching and - if the result is sufficient - stop there. Else, try the patching approach afterwards.
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.)
I think I know the answer already, but I'd love to be surprised and learn. I'm trying to figure out what the rule of thumb usually is for something like this.
I have an application where users can scale how much bigger or smaller they want an image resized to, for it to later be printed on paper. I would love to warn them that depending on the size of the image in question (and each would be different), going above or below a certain resize point is going to make the image look very bad and they shouldn't continue.
Keep in mind that aspect ratios will always be preserved.
Is there any way to figure this out programmatically? I don't mean an exact science here, but I'm hoping it's more than just the human eye test. Is it safe to say anytime a raster image is shrunk or stretched by 50% (or 25%?, 75%?), the user should be warned?
Thanks,
Sam
The quality is likely to be judged by the sharpness of the result. This can be estimated by using the DPI of the image, that is the number of pixels in the original (either dimension) divided by the number of inches it will be printed at; the resize ratio really doesn't matter except that it determines the printed size. The actual limits will depend on your application, but I'd start with a limit of 150 DPI or above and adjust from there.
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.
I look for an appropriate image scaling algorithm and wondered why supersampling is not as popular as bicubic, bilinear or even lanczos.
By supersampling I mean a method that divides the source image into equal rectangles, each rectangle corresponding to a pixel in the destination image. In my opinion, this is the most natural and accurate method. It takes into account all pixels of the source image, while bilinear might skip some pixels. As far as I can see, the quality is also very high, comparable with lanczos.
Why do popular image libraries (such as GraphicsMagic, GD or PIL) not implement this algorithm? I found realizations only in Intel IPP and AMD Framewave projects. I know at least one disadvantage: it can only be used for downscaling, but am I missing something else?
For comparison, this is a 4.26x scaled down image. From left to right: GraphicsMagic Sinc filter (910ms), Framewave Super method (350ms), GraphicsMagic Triangle filter (320ms):
Now I know the answer. Because pixel is not a little square. And that is why supersampling resizing gives aliased result. This can be seen on thin water jets on sample image. This is not fatal and supersampling can be used for scaling to 2x, 3x and so on to dramatically reduce picture size before resize to exact dimensions with another method. This technique is used in jpeglib to open images in smaller size.
Of course we still can think about pixels as squares and actually, GD library does. It's imagecopyresampled is true supersampling.
You are a bit mistaken (when saying that linear rescaling misses pixels). Assuming You are rescaling the image by at most factor of 2, Bilinear interpolation takes into account all the pixels of the source image. If you smooth the image a bit and use bilinear interpolation this gives you high quality results. For most practical cases even bi-qubic interpolation is not needed.
Since bi-linear interpolation is extremely fast (can be easily executed in fixed point calculations) it is by far the best image rescaling algorithm when dealing with real time processing.
If you intend to shrink the image by more than factor of 2 than bilinear interpolation is mathematically wrong and with larger factors even bi-cubic starts to make mistakes. That is why in image processing software (like photoshop) we use better algorithms (yet much more CPU demanding).
The answer to your question is speed consideration.
Given the speed of your CPU/GPU, the image size and desired frame rate you can easily compute how many operations you can do for every pixel. For example - with 2GHZ CPU and 1[Gpix] image size, you can only make few calculations for each pixel every second.
Given the amount of allowed calculations - you select the best algorithms. So the decision is usually not driven by image quality but rather by speed considerations.
Another issue about super sampling - Sometimes if you do it in frequency domain, it works much better. This is called frequency interpolation. But you will not want to calculate FFT just for rescaling an image.
Moreover - I don't know if you are familiar with back projection. This is a way to interpolate the image from destination to source instead of from source to destination. Using back projection you can enlarge the image by a factor of 10, use bilinear interpolation and still be mathematically correct.
Computational burden and increased memory demand is most likely the answer you are looking for. That's why adaptive supersampling was introduced which compromises between burden/memory demand and effectiveness.
I guess supersampling is still too heavy even for today's hardware.
Short answer: They are super-sampling. I think the problem is terminology.
In your example, you are scaling down. This means decimating, not interpolating. Decimation will produce aliasing if no super-sampling is used. I don't see aliasing in the images you posted.
A sinc filter involves super-sampling. It is especially good for decimation because it specifically cuts off frequencies above those that can be seen in the final image. Judging from the name, I suspect the triangle filter also is a form of super-sampling. The second image you show is blurry, but I see no aliasing. So my guess is that it also uses some form of super-sampling.
Personally, I have always been confused by Adobe Photoshop, which asks me if I want "bicubic" or "bilinear" when I am scaling. But Bilinear, Bicubic, and Lanczos are interpolation methods, not decimation methods.
I can also tell you that modern video games also use super-sampling. Mipmapping is a commonly-used shortcut to realtime decimation by pre-decimating individual images by powers of two.