Phone cameras can take in more light than the human eye
theconversation.com
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Its fast food art. Sprinkle the image with sugar and fat
They don’t have the dynamic range of colors to capture all the colors our eyes can see, but they can pick up other types of light that we can’t.
This fact was pointed out in a lot of reviews of the Vision Pro. It’s just about good enough to make you forget you’re in augmented reality but the world looks more dull and lifeless because your eyes see more vibrant colors than small digital sensors.
Marketers of things like CCTV cameras love to sow confusion about these things, as NIR-sensitive cameras are extremely cheap while thermal cameras are comparatively expensive.
Humans are not sensitive to NIR because for the vast majority of human existence, any time there was NIR light there was also an abundance of normal visible light, due to a little thing called "the sun"
NIR sensitivity does not improve night vision. That mostly requires a "tapetum lucidum" or reflective layer, more rods than cones (less helpful when you want to see colors in daylight) or just larger, more biologically expensive and vulnerable eyes than necessary.
Your comment and its grandparent are both excellent, but I would like to nitpick a bit here. Technically, NIR sensitivity does improve night vision through the simple mechanism of "detect more wavelengths = detect more total light". It's not thermal imaging, but it is the reason that CCTV cameras often mechanically move their IR filter out of the way when the light falls below some threshold - at that point, sensitivity is more important than color fidelity, so they accept all the light they can get, even nonvisible. Of course this is also often coupled with NIR illumination LEDs.
I do not see any visible light emitting from the remote. This is in a pitch dark room.
Edit: proof, taken in a semi-lit room for clarity: https://imgur.com/a/63FbXjQ
Edit 2: Tested on iPhone 15 Pro selfie camera. It detects IR at the same intensity.
Edit 3: Same as above with a Pixel 8.
Edit 4: I now have my office involved, this is fun. Same with a Samsung S24.
Final Edit: OK, I just went through about 20 colleague's phones, which are various mixes of iPhone and Android, new and old. Testing the front and back camera, every camera on every phone saw the IR light.
If a smartphone camera does not see IR light, this appears to be the exception, not the rule. OK, back to work!
These are all US devices, in case that matters. We don't seem to have an agency that regulates light emissions from cell phones.
I acknowledge that I may be the only person in the world that is this interested in this fact.
[1] https://alexbock.github.io/blog/nir-water-red-wine-compariso...
The dash cam has a video-out port, but unfortunately it appears to be NTSC resolution. I'd love some sort of setup that outputs to >=8" 1080p display attached to my dash. It would help so much in my rural area with wildlife in the road, as well as the constant random pedestrian walking on an unlit rural highway in dark clothing.
Ideally, if I could get great quality, low noise low light video like the VIOFO, I could then start playing with object identification with OpenCV.
1. I worked on such spectral systems in a past military life but don't want to attach something big like a Cadillac FLIR unit, or something expensive, like nearly every viable consumer FLIR option. All the "affordable" consumer FLIR options suffer from low resolution and/or low response time.
When I experimented with connecting two thermal cameras to a VR headset for stereo thermal vision, I used two Seek CompactPRO FastFrame units. They're 320x240@15Hz for $400 which is a lot more usable than the typical 80x60@9Hz consumer thermal, and it's easy to integrate the Android model into custom applications. They also have a 320x240@25Hz model for $1000.
I'm still impatiently waiting for affordable 640x480 thermal cameras, but in my opinion 320x240 at moderate frame rate is past the good-enough threshold to be legitimately useful for high contrast situations like identifying warm-blooded life on the side of a rural road.
> I'd love some sort of setup that outputs to >=8" 1080p display attached to my dash.
The Tesla Cybertruck has an option to display the view from the front bumper camera on the 18.5" main screen, but front camera display is unfortunately not available in any of Tesla's other models. With the proliferation of large touchscreens and camera arrays, more vehicles may support this from the factory soon.
Are you sure about that?
My understanding is that digital cameras can capture an extremely wide color gamut -- the vibrant colors -- but that the extra information is necessarily thrown away when encoded in sRGB or P3 in your image/video file.
Because we don't have many displays which are capable of showing those ultra-saturated colors, so we don't waste bits on storing them in files either. P3 is an obvious improvement over sRGB that Apple products mostly use now.
If you process RAW files directly from the sensor, I'm pretty sure you get a much wider gamut.
What I'm not entirely sure about is how that gamut maps to the colors our eyes can see -- it's not going to be exactly the same coverage. So I'm not sure where it goes beyond human eye sensitivity, or where it doesn't quite reach it, and to what extent this depends on the sensor technology (e.g. what's in your phone vs. an expensive DSLR vs. a professional cinema camera). Ultimately it's going to come down to the exact precise shape of the frequency curve in each of the R, G and B filters in the color filter array, and how they can be mathematically translated to reflect the human eye's [1, 2].
(And so color vibrancy limitations with the Vision Pro are absolutely going to be coming from the displays before they come from the cameras.)
[1] https://en.wikipedia.org/wiki/Color_filter_array#Image_senso...
[2] https://en.wikipedia.org/wiki/Color_management#Color_transfo...
It requires a surprising amount of expertise, effort and money to assemble, calibrate and feed content to a high-end home theater sufficient to correctly deliver even the (currently modest) full capabilities of 4k HDR10+ / DolbyVi$ion. And the home theater content ecosystem is still a mess of mixed, partial implementation of supposed standards.
Implementation of HDR/WCG for web media is currently even less evolved. Despite many newer, high-end laptop, computer and phone screens having decent capabilities, inconsistent file formats, browsers, drivers and OS implementations seem far away from this "just working" for end users.
That's not to say that there aren't some amazing aurorae that you can experience with the naked eye, if you go closer to the poles (well, practically just the Northern one) on a good night you won't need a camera!
Even for those, presumably a camera will catch more stuff in the background though.
Quite unnerving
"As a professor of computational photography, I’ve seen how the latest smartphone features overcome the limitations of human vision."
combined with:
"AI allow these devices to capture stunning images"
?
I'm not an expert, but every time I casually read about how computational photography works, I get the impression it is some very clever image processing algorithms (eg, pixel alignment of multiple images, anti shake, combining multiple images to enhance dynamic range, depth perception, detecting under and over exposure in parts of the image) put together by a software engineering team made up of entirely of humans. Occasionally there may be an AI detecting faces, smiles and blinks for things like timing and framing - but not in the aurora photos the article features.
Which means I'm looking at an instance of the term "AI" evolving in meaning in popular culture. Software engineers have a very particular set of statistical techniques and algorithm's when they use the term AI. The new meaning seems to be "any set of computational gymnastics it would require an expert to understand", or worse just: "big complex algorithm".
Sigh. It makes me sad. But then so did OS changing it's meaning from "a layer of software that abstracted the details of the underlying hardware from user space programs" to "Android" / "iOS" / "Windows". The old, more formal, precise and limited definition we software engineers invented is now at best jargon, but in reality probably gone. When I see younger programmers use the term "OS" here, they invariably mean the common definition.
I stoped using my Canon DSLR 80D this holiday and we took a handful benchmark picture I will analyse later.
The night mode in dark areas wow. Tele? Wow. Macro? Awesome.
When a picture of me is taken with a Pixel, I have unnaturally uniform tan skin and blemish free. And this is without any 'beauty filter' or whatnot.
Even at 105mm f4 the difference is night and day. Portrait mode is good enough for casual use, but the real thing still looks better.
Please, don't confuse your misconception of what dragging the shutter speed would do in your day to day usage vs what professions do with long exposure.
Sure, adding motion blur is an effect one can achieve with long exposure. You need some extra things like ND filters to avoid over exposing, but that a technique someone is specifically looking to achieve. It's not a mistake from something my mom would do.
We've been using long exposure for things like light painting well before Apple/Google. We've been using long exposure for astrophotography for a long time as well. I still feel utter respect for Hubble et al night after night manually guiding the scope to keep the image in frame. In fact, the same frame of film would be exposed for multiple hours each night for multiple nights before finally being developed.
What you've associated things with in your mind does not mean the rest of the world only associates the same things in the same way.
Light painting is not about compensating for low light. On the contrary, it typically involves reducing the ambient light in order to manually "paint" with a light bulb for artistic effect.
Which part of the above are you debating?
I like your "reducing the ambient" to really let me know you have no clue what I'm referring to, but that's okay. this whole thread is just wasted energy
Blurring is frequently the goal. Blurring waterfalls is a popular technique. As is blurring the ocean. Or streaking of light from cars. Searching for "long exposure" shows these techniques.
They use tripods to keep some things static. Smartphones can make those photos just as well as cameras with the computational photography turned off. The limitation is using neutral density filter to make exposure work.
um, ackshully, lots of computational editing is being done now. The image of Sag A* was heavily computed. They are now having to use computed images to remove all of the made on earth objects in the sky from Starlink to planes. There's a lot of stuff done to astroimages now and computational editing is making its way there.