51–60 of 61 posts
This is all really cool, but, there's one thing you can't make up for enough with processing: optical zoom, (digital zoom, however much temporal super resolution trickery, has a different angle).
As a result I started using my DSLR again, and I rediscovered how beautiful the photos were. They also print nicer.
This fall I finally replaced the busted phone with an iPhone Pro. The camera on this thing is great, but the computational photography enhancements are particularly nice.
But my DSLR still smokes it.
There’s the old maxim of “the best camera is the one you have on you.” I’m happy I went with the pro, and in isolation, it’s an amazing all around snapshot camera. Occasionally I get lucky with some stunning shot.
But the DSLR just wins hands down when it comes to shooting something like the foliage of a Japanese maple. The bokeh is beautiful straight out of the camera. The iPhone still struggles, and there’s a ton of matte noise around edges that needs to be cleaned up. It’s much better at things it was designed for, like portraits.
So, anecdotally, for me at least, it’s a mixed bag. I love both cameras for different reasons but ultimately the DSLR still has the edge on quality. But the iPhone is always there, and has some tricks (especially low light) that the DSLR can’t compete with.
For the foreseeable future, I don’t see my phone fully replacing my DSLR.
In extreme cases, it even not "photo" as some information about photons recieved by some optic system with noise reduction afterwards. Not, it just pictures, based on recognised faces, objects and stars. And I don't know why, but I feel panifully bad about it. It is not approximation of world-how-it-is, but some expectation about world-how-people-want-it. It can recognise constellation based on few stars, and will draw nice picture of great stary sky, but will delete starlink sattelite, meteora or supernova as some unexpected noise.
Usually I don’t mind reposting my articles but with a direct reference to original at the beginning https://vas3k.com/blog/computational_photography/ Let’s be respectful to the original author, I spent couple of months writing it:(
Earlier quoted context omitted.
Hi Vas3k! My name is Kate and I am Head of Content at Let's Enhance. We are very thankful for the given material, because it's highly related to us. And I want to clarify this unpleasant situation: I've directly talked with the author of the article - Vas3k and asked for the permission to publish this awesome material. I can attach the screenshots of our conversation. All the copyrights are reserved. We mentioned you…
Yes, I get this all the time too. Vaguely worded emails for permission to rip my stuff and any answer will be used as permission granted. You're in violation of copyright, and the author is right here to dispute your claim. As you are no doubt aware as students of copyright law you've now been told that your permission to copy has been rescinded which leaves you with only one option.
Sorry for bad english, and maybe I am deeply wrong, but: In extreme cases, it even not "photo" as some information about photons recieved by some optic system with noise reduction afterwards. Not, it just pictures, based on recognised faces, objects and stars. And I don't know why, but I feel panifully bad about it. It is not approximation of world-how-it-is, but some expectation about world-how-people-want-it. It ca…
Unprocessed camera modes will continue to exist for people who want that. Maybe with some built-in digital signature in case it is used as proof.
Photography, and earlier that that, drawing, has always been world-how-people-want-it. Accuracy is just one of the things you may want.
This is all really cool, but, there's one thing you can't make up for enough with processing: optical zoom, (digital zoom, however much temporal super resolution trickery, has a different angle).
What's the difference? (Other than needing a really densely-packed sensor, of course)
The only way would be a completely fake ML based manipulation of the image to distort the image based on object/scene detection.
This article is completely, utterly wrong as soon as it starts talking about anything plenoptic. Please disregard after that point as there are serious factual errors, particularly regarding what Google did or didn't do with Lytro. (Source: I'm in the VR / plenoptic space, knew a bunch of people at Lytro, some of whom are now at Google. Timelines and facts do not match this article's assertions.)
That comment would be a lot more valuable if you corrected the record. To just gainsay what is written here accompanied by a strong reference to your authority is not how it is done imo, and if you can't or don't want to talk about it then you also shouldn't comment like this.
Phase detection is not the same thing at plenoptic; the article implies that phase detection operates in the same way as a microlens array. It doesn't; microlens arrays for plenoptic imaging have many pixels underneath, and perform different operations on them. This line:
"With only two pixels in one, there's still enough to calculate a fair optical depth of field map without having a second camera like everyone else."
This is categorically false. You need dozens of pixels underneath a microlens array to do digital refocusing; phase detection is not remotely the same thing.
Later, the author conflates microlens-based refocusing with camera-array based plenoptic imaging. These are wildly different disciplines; the work done in VR does not include microlens arrays anywhere. Not any. But they do include large arrays of cameras (see volumetric work from Microsoft HoloCapture, Intel's capture studio in Manhattan Beach, and my own company Visby's work — cameras at Radiant Images in LA... all arrays of cameras, no microlenses anywhere).
"Apparently, if you take only one central pixel from each cluster and build the image only from them, it won't be any different from one taken with a standard camera." This is false; taking the central pixel will give you an aliased image (or an image as though taken with a very, very high f-number lens, if the pixels correspond to the same zone of the aperture). To make an image as though you didn't use a microlens array, you sum all the pixels under the microlens to produce a macropixel. This is why light field cameras have lower effective resolution than traditional cameras. The remainder of the speculation about 'sneaky plenoptic JPEGs' has no basis in light field imaging; refocusing is not achieved by throwing away (or binning) pixels, it's done by summing different sets of pixels. Lytro founder Ren Ng's PhD thesis was about a way to do it faster by using the Fourier Slice Theorem. (Beautiful paper, if you have time to read it.)
Google did not "buy and kill Lytro." Lytro's assets were sold and Google bought some of them, but none of that tech made it into the Pixel line of cameras, period. The Google portrait mode is done using phase detect pixels as a cue for segmentation, as discussed in the article this blog links to. Nothing plenoptic required, and certainly no Lytro tech there. The blog post implies that the Pixel is using Lytro-like techniques rather than phase detect pixels to hint at segmentation for synthetic blur.
I have never heard of plenoptic cameras being used for image stabilization. In my personal experience (5 years of stabilizing cinema cameras for two different companies), virtually all stabilization issues derive from rotational blur, not translational movement. Your ability to stabilize translational movement would be limited to the aperture size, and translations of that magnitude have vanishingly small effects on the image unless the objects are very, very close to the lens (like macro). I have no idea where the blogger came up with this idea but there is no reference provided and I have never heard of it (nor would it work, for the above reasons).
The section "Fighting the Bayer Filter" miscasts the challenges of a Bayer pattern under a microlens; naively summing adjacent pixels under other microlenses would blur an image. You do get a boost to dynamic range from the synthesized images — you're binning a bunch of pixels to create your image, so you reduce noise as 1/root(n) in the number of samples. That could lead to better color in dark, noisy areas, but it will not increase the gamut of your sensor.
It goes on... but since this article was already off the front page by the time I posed my comments, this is probably enough.
(As an aside, Jacques — I really enjoy your writing!)
This article is completely, utterly wrong as soon as it starts talking about anything plenoptic. Please disregard after that point as there are serious factual errors, particularly regarding what Google did or didn't do with Lytro. (Source: I'm in the VR / plenoptic space, knew a bunch of people at Lytro, some of whom are now at Google. Timelines and facts do not match this article's assertions.)
So it is your word against the OP's. Why should we believe you more than the OP? Please present some facts and citations that we can verify ourselves. Appeal to authority is a fallacious form of argument.