# Tag Info

19

The depth of field is a characteristic of a camera lens setting, although the name "Depth of Field" is commonly used to describe the effect caused by such characteristic. Camera lenses can only perfectly focus on one single point, but there is a distance for which the image will still look reasonably sharp. Such distance is what actually the Depth Of Field ...

9

it is quite easy to measure the local max frequency in an image (at least as a low resolution mask, with some regularization). Several papers of the MIT graphics group have been around detecting and processing from this kind of clue, with regular or coded aperture cameras. e.g. Defocus Magnification and Image and Depth from a Conventional Camera with a Coded ...

8

Sigma and kernel size of Gaussian filter Regarding how to choose the sigma and the kernel size (pixels) of the Gaussian: you set the sigma based on how wide of a blur you want (judging it visually) and then choose the kernel size based on the sigma. It's a game of finding a kernel size that captures enough of the (mathematically infinite) bell curve to look ...

8

There are several techniques used. A simple, but limited, post-process approach that is not really used any more consists in reconstructing the world space position of a pixel using both the view projection matrix from current and previous frame. Using these two values you can compute the velocity at a pixel and blur accordingly, sampling along the ...

7

Yes, your theory is correct. A gamma-correct blur entails converting the input pixels to linear color space, performing the blur weighting and accumulation in that space, and then converting back to gamma space at the end. As noted in the comments, the actual transform is not literally squaring and square-rooting, that's just an approximation (and not that ...

7

Two suggestions: If your data is from an image you are displaying on a standard monitor, the chances are it is (or you are implicitly assuming that it's) in sRGB format. This means that the colour components are not linear. Ideally, you should first map into a linear colour space, do your filtering (e.g. blurring) operations, and then map back. *(If you ...

5

The blog post that you talked about, is not about generating bokeh for a computer generated image. It is instead about generating a believable depth of field effect from an image captured by a smartphone camera, as the effect is desired for portraits to make the subject stand out. It generally works by splitting the image in to two parts. One part is the ...

4

If I understand your question, you are asking how to actually perform said directional blur in code? A Gaussian blur is typically done by sampling your image in all directions around your current point (or if in 2 passes, one vertical and one horizontal which equates to the same thing), with a specific set of weights for the falloff. For a directional blur ...

3

This looks like JPEG artefacts so I guess the image went through a JPEG compression step at one stage and that permanently introduced noise into the image. The red channel is blurred stronger because JPEG additionally applies chroma subsampling. I suspect the original image was subpixel rendered as you shouldn't really have anything in the chroma channels ...

3

First we can calculate the physical diameter of CoC in the image plane, given the lens parameters. This equation is from Wikipedia – Circle of confusion: $$c = {|S_2 - S_1| \over S_2} {f^2 \over N(S_1 - f)}$$ where the variables are: $c$: the physical CoC diameter in the image plane $S_1$: focal distance (the distance at which a subject would be in ...

2

While clamping or saturating may be the most common way of handling this situation, it's usually not the best. (But wrapping/modulus is one of the worst.) Another way of handling it is to either convert the input to 16 or 32-bit ints or floats and do all calculations in the higher bit depth. You may want to down-convert the final output, if required by your ...

2

When doing a filter operation such as a blur, in many cases the filter itself will be normalized so its values sum to 1.0, precisely to avoid this problem. In cases where out-of-range outputs do need to be handled, the most common way is what you called capping. In graphics it's more commonly known as clamping or saturating. This is the default behavior if, ...

2

If I blur the whole image and apply the result on the sphere, the white background will bleed onto the sphere shape and I want to avoid that. I also don't want that the blue (3) and yellow (4) sphere merges with the red (1) and green (2) ones. But I would like that the green and red ones merges. Again This could be done using the depth but if you have more ...

2

Standard blur removes high frequency content from the signal, whereas edge detection usually look into high frequency to detect edges. Be careful on how much blurring to apply to ensure that you don't lose desirable edges. The goal of blurring is to perform noise reduction, so the best would be to come up with a model of the noise present in your images and ...

1

It would be helpful if you posted some more information about what you're trying to do (rather than problem links) and some screenshots of what is actually happening. Being here I basically see "My code doesn't work, fix it.", which is not motivating to answer your question... That being said, you calculate an offset for reading out from your texture. ...

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