New HQ Raspberry Pi Camera with C/CS-Mount

The Raspberry Pi Foundation just released this new camera module that has a C/CS-Mount for interchangeable lenses and a better sensor than the V2 (and certainly the V1) camera module.

https://www.raspberrypi.org/blog/new-product-raspberry-pi-high-quality-camera-on-sale-now-at-50/

I think this might be exactly what some of us are looking for to build a scanner for 8mm (or even 16mm) film around a Raspberry Pi. It’s certainly a much better and still affordable alternative to unscrewing the lens on the V1 camera module.

What are your thoughts on this?
Has anyone had a chance to try it yet?
Do you have an opinion what lens might work well with it for building a telecine machine?

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This looks cool, I wonder what lens would be good for 16mm?

that looks promising. From the data sheet: “this product is designed for use in consumer use camcorder”.

So it seems that this is the first Raspberry Pi camera chip without a CRA-compensation by microlenses. That would be fantastic, as the CRA-compensation (a usual technique for short fixed focal length setups like mobile phones) always led to color drifts and desaturation issues if you replace the standard lens. These issues are impossible to compensate fully in post production. Nevertheless, quite a few people based their movie scanner on Raspberry Pi cameras and got resonable results (I got only acceptable results with the v1-camera, which has a lesser CRA-compensation than the v2-camera.)

If you match one of the v1- or v2-cameras with a Schneider Componon-S 50 mm, with the lens placed about 100 mm from the chip and 100 mm from the movie frame, you will image the usual Super-8 format perfectly, even with a little headroom for sprocket detection and the like. This chip has double the size of the v2-camera, so the optical setup (distance chip-lens + distance lens-frame) will need to be adapted. It would actually shift the Componon-S slightly out of the working range this lens was designed for. Should still work however. Otherwise, the lens could always be reverse mounted to solve this issue.

One will have to be see how the software support of the new v3-camera will work. I guess only time will tell. One standard approach of utilizing these Raspberrry Pi cameras, the picamera-library (Python-based), hasn’t seen much update activity recently. Using the standard software supplied with the Raspberrry Pi (raspistill or -video) is way too lame for most film scanning applications.

From my experience with the v1- and v2-cameras, the Raspberry Pi processing pipeline does employ a lot of automatic processing. It is not easy to circumvent these automatic processing steps - and they can easily introduce flicker and the like in your scanning results if not switched off. I never succeeded in turn off all processing deemed necessary by the Raspberry Pi foundation or the chip manufacturer for proper use. For example, the v1- and v2-cameras employ noise reduction algorithms which actually work not only on the digital noise of the image sensor, but also on the noise introduced by the film grain. Well, depending on your taste, that might even be considered a valuable feature. However, a chip designed for “consumer use camcorders” is certainly a different beast than a machine vision camera and might feature some processing steps which you do not want to have in a scanner application. I will certainly have a look at the new camera chip.

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Glad we have you to walk us through all the considerations, @cpixip! Looking forward to seeing what people come up with.

Ok, just a quick update here: the new HD camera is not too bad.

Contrary to what I suspected, the camera started to work right away on one of my Raspberry Pis which still uses an old Summer 2019 “buster” version of the OS (the guys at the Foundation are at a Feb 2020 version already).

My copy of the picamera-lib is a fork with lens-shading build in, otherwise it is not so up-to-date compared to the current picamera-lib.

So in fact, I expected total failure with the new camera.

Well, that was not the case. At least in the resolution I tried, which is 1296x972 pixel, I could stream video from the device using my old software based on the picamera-lib. I could trigger most function that I do not need, for example the image effects. Exposure settings as well as settings for brightness, contrast and saturation (via picamera) were accepted. In video-streaming, I could vary the quality of the MJPEG-encoding over quite some range, effecting as expected the average frame size send to the receiving computer.

I have the impression that the whitebalance algorithm still needs some tuning, but it works sufficiently well in most cases.

What did not work is setting a lens-shading table (well, you would not need that with the HD camera anyway) and activating the capture via the still-port. That might be rather an issue with my software than with the picamera-library. What most probably worked, judging from the slow-down in framerate, is the switch to raw-mode. To be sure about raw mode, I need to write code for reading and decoding the raw image size of the new sensor - that will need some time and effort.

So, in summary, I am surprised how much of the old software interface is available right out of the box for the new HD camera. Here’s an example image to show the color output of the new camera. It was taken with all “autos” on: autoISO, autoExposure and autoColorbalance. Daylight exposure. A 16mm lens @ 1:1.4 setting was used, with an exposure time of 1/30 sec and ISO 130:

(Remember, that is a single video frame, from the videoport, not a still frame (traditionally, the video and still port have different image characteristics, due to different processing pipelines))

Update: I switched resolution now to 2592x1944 pixels, which is an old resolution mode of the v1-sensor. AutoISO is now 183, exposure time 1/64 sec, video streaming at about 7 fps. Here’s a cut-out of the frame

which shows some heavy sharpening happening here. Furthermore, here’s the also enlarged image of a screw drive. Note the staircase-like appearance of bright line visible in head of the bit screw - that should ideally be a single smooth line:

This is the kind of “advanced image processing” you will probably have a hard time to get rid of within the Raspberry Pi software environment. Here’s the full frame were the above cut-outs were taken from:

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It’s great to hear about some actual first hand experience with the new camera!

The 16mm lens you were using isn’t by any chance the 16mm lens that is offered alongside the HQ camera? I would love to hear if anyone has had a chance to try that or the 6mm lens (with respect to 8mm film in particular).
I am still looking into what lens to get for my 8mm film scanning setup (somewhat on a budget), so I was reading up on the 6mm and the 16mm lens. In The Official Raspberry Pi Camera Guide they recommend the 6mm lens for macro photography, which seemed odd to me. From what I gather from these data sheets (6mm data sheet / 16mm data sheet) their MODs are the same at 0.2m. That should make the 16mm more suitable. Do please tell me if I am mistaken.

Would this help?

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@jankaiser: well, yes. I was using the “offical” 16mm lens. The image of the camera box I posted above is actually the shortest distance you can focus this lens (with the lens screwed in fully).

The lens is not that bad, but if you look closely on the color chart also posted above, you can see some barrel distorsions.

In case you want to image the Super-8 frame with this camera chip and lens, you will need somehow to extend the distance between lens and chip.

For the v1- and v2-cameras, the sensor size is actually just about the same size as the frame area (plus sprocket) of a Super-8 frame. Therefore, any lens can be used in an arrangement which images the frame 1:1 onto the sensor chip. Simplifying a little bit: given a lens with a focal length f, you get this 1:1 imaging if the distance between sensor and lens is 2 time f. And you will discover that the distance you need between lens and Super-8 frame is also 2 times the focal length of your lens.

Now, there’s a twist hidden here. Lenses are manufactured to work optimal in a certain regime. So, of course you can use a normal lens for macro photography, by using just extension tubes. But you won’t get the optimal lens performance out of this setup. Your normal lens is optimized for a distance of one focal length between lens and camera chip. That is the reason some people reverse the lens they are using in doing macro photography.

The choice I made was to pair the camera sensor with a Schneider Componon-S 50 mm. For two reasons: first, this is an old enlarger lens which is optimized exactly for the distances I am using it. Second, I have two of these lenses available from my old analog photolab :smiley:

Whether the Raspberry Pi foundation lenses perform sufficiently well in macro conditions needs to be seen. If I would have to choose between the 6 mm and the 16 mm version, I would probably go with the 16 mm version. First, it is easier for the optical designer to optimize a long focal length, so I would expect the tele-version to have less a distorsion problem than the wide-angle version. Second, if your camera would be in the 1:1 setup (which is not the appropriate one for the new HD-camera, because the sensor size is larger)it would be about 32 mm away from the film gate with the tele-lens, but only 12 mm away when using the 6 mm wide-angle lens. More space around the film gate is usually of advantage, because, for example, you might want to clean it occationally.

All in all, I would recommend not using these kind of lenses, as they are designed to be used in surveilance applications - you will probably have a better image definition if you use a better lens. Even the above mentioned Schneider Componon-S 50 mm has a sweet spot: you get the sharpest image definition at an f-stop of about 5.6 to 8.0. Lower f-stops start to blur the image, quite noticable already at 2k to 3k scan resolution, because the outer parts of the lens focus on a different plane that the central parts. Pushing to higher f-stops leads also to defocusing because the light rays are bend around the small f-stops. All in all, one probably just has to try out the lens.

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@WDaland: wow, that’s an interesting find! Waited for something like this to happen for years. That might give the opportunity to optimize the available Raspberry Pi cameras for the task at hand, namely scanning movie frames with optimal fidelity. The v1- and v2-sensors are already supported, but frankly, they only can be used with their original lens. The new HD-sensor is a different beast and will be supported shortly. Cool :sunglasses:

@cpixip: Thank you so much for this great insight! It’s very helpful.

I’ve seen the Schneider Componon-S 50mm being a fan favourite around the Kinograph forum for a while now. I had always assumed it’s on the pricier side, but it seems a decent used one can be had on eBay (in Germany) for as little as 50€. This puts it in the same ballpark as the 16mm Raspberry Pi lens and presumably makes it the better “deal”.

Have you had the chance to try the Componon on the HQ Camera as well?
I’m guessing it would need at least a 43mm filter thread to C-Mount reverse adapter to mount the lens in good setup for 8mm film scanning.

Well, have a look at my current setup, utilizing a see3CAM CU135 ( AR1335, sensor size about 1/3.2", or 4.54 x 3.42 mm, diagonal 5.68 mm, max 4096 x 3120 px), mounted onto a Novoflex bellow via a 3D-printed adapter. On the other side, the Componon-S 50 mm is placed - not in reverse mode.

As you can see, the distance between sensor and lens is about double the size of the focal length of the lens, and the distance between lens and movie frame is about the same. Such an optical setup leads to a magnification scale of about 1:1. In truth, the optics are adjusted to a scale factor of 1:1.7. So the camera sensor sees an area of about 8 x 6 mm. This allows me to image the sprocket neighbouring the frame and have a little headroom for the frame position. The actual camera frame of a Super-8 movie is 5.69 x 4.22 mm, the projector is supposed to use only 5.4 x 4.01 mm of the camera frame.

Now, I have used the same setup with the Raspberry v1-camera (OV5647, 1/4", 3.6 x 2.7 mm, diagonal 4.5 mm, max 2592x1944 px) as well as the v2-camera (IMX219, same dims as OV5647, max 3280x2464 px). For these sensors, the lens only has to be moved a little bit to get the same scale as with the see3CAM.

Here’s a more detailed view of the see3CAM mounted on the 3D-printed adapter which fits directly into the standard Novoflex mount:

I would prefer to use the same setup for testing the new Raspberry HD-camera (IMX477, 1/2.3", 6.17x4.56 mm, diagonal 7.66 mm, max 4096x3040 px), but the bulky C-mount of the camera is getting into the way of designing an adapter. Currently, the only option I see is to unscrew the whole aluminium mount and just use the bare sensor - I am not sure at the moment if I want to go along that path. Otherwise, I might opt to design a totally different camera setup.

Both routes will take some time, I am afraid, as I am currently using the scanner to digitize Super-8 material. I would also need to partially rewrite my old software to handle an appropriate camera mode for the new HD-camera. So it will take some time for me until I can share scan results with the new camera chip.

I am currently using the see3CAM in a resolution of 2880 x 2160 px, running at about 16.4 fps. From the specs of the Raspberry HD-camera, I think you would get maximal 10 fps out of the HD-camera at that resolution (in fact, I got about 7 fps with my python-based client-server software). So frame-rate wise, the new HD-camera is not better than my current setup.

My primary goal is to do fast HDR-scans - and the major bottleneck here is the time a camera needs to reliably settle onto a given exposure value. Not sure whether the new HD-camera behaves in this respect better than the old ones. Maybe the new libcamera-interface is helpful here. I will update on this as soon as I know more.

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ok, here’s the promised update on the Raspberry HQCam:

Raspberry Pi cameras feature different processing pipelines for still and video imagery. In fact, there is also a third pathway, namely output of raw sensor data, but it is prohibitively expensive in terms of scan speed and post-processing time.

The Raspberry Pi HQCam is based on a sensor mainly intended for video application, which is reflected in the available “camera modes” of this unit - only two of the four modes listed feature a still image pipeline, but all are available to output video.

The original sensor resolution is 4056 x 3040, but the maximal frame rate is only 10 fps at that resolution. The most reasonable mode to use is half of that resolution, 2028 x 1520, which is specified with a maximal 50 fps. This mode is also binned with 2x2, so it reduces the sensor noise by a factor of two.

The maximal frame rates specified are only achievable in video mode, because in still mode, the Raspberry Pi cameras need to capture actually several images before outputting a single result (mainly due to the automatic tuning algorithms adapting to the scene which is photographed).

This means that the data coming from the camera is MJPEG-encoded. So the data is heavily processed before it is available for use. However, people have seen characteristics in the raw data (mainly certain noise characteristics) which indicate some image tweaking is happening already at that sensor level - so chances are that even the offical “raw” image delivered by the sensor has already some (mainly noise-reducing) image processing applied.

However, the MJPEG encoding available with this sensor easily outperforms the encoding which is available with the v1- and v2-cameras of the Raspberry foundation. The encoding quality is reasonable already at a quality setting of 5%, and it increases drastically when the quality is raised above about 20%. Visually, there is no difference noticable with a quality setting of 60%, compared to a higher quality setting of, say, 95%.

The HQCam can be accessed with the usual software available, provided the OS is up-to-date. Access via raspistill and raspivid in a film scanning project is prohibitive slow or complicated (make your choice), but access via the picamera lib is fine - even so this library hasn’t seen any major update for quite some time.

The color separation of the new camera is fine, but the automatic whitebalance seems to be not as good as with the v1- or v2-cameras.

Optically, this is the first sensor from the Raspberry Pi foundation which is designed for attaching arbitrary lenses. The older v1- and v2-cameras feature a micro lens array designed for the working with the mobile phone lenses supplied with the camera. If you exchanged the lens of the older Raspberry Pi cameras with a lens with a different focal length, you ended up with color shifts and desaturation effects which are impossible to correct in post production. While the v1-camera performed here substantially better than the v2-camera, this issue is totally gone with the HQCam.

Mechanically, the camera features a machined block with standard connectors for lenses and tripods. Integrated into this block is also the IR-stop filter, so you can attach basically any lens you have available. I am using the trusted Schneider Componon-S for scanning purposes.

The outline of the new camera (angled, larger spatial footprint) makes it somewhat more complicated to design a housing or mounting for an existing design, but it is not too challenging.

For my specific application - HDR-scanning of Super-8 stock - it is mandatory that scanning and processing is reasonably fast. Standard Kodachrome film stock for example has a dramatic dynamic range, which is difficult if not impossible to capture with a single exposure. So several different exposures need to be taken of each single frame in order to capture the full dynamic range of the film image. This leads to huge amounts of data to be captured, transfered and processed. The most reasonable resolution to work with in the case of the HQCam is 2028 x 1520. In my setup, this gives me capture times of about 1.5 - 2 seconds per frame, with about additional 1 to 3 seconds for combining the different exposures via software (this time increases quadratically with image size).

Here’s an example of a HQCam capture:

It is a single frame from a Kodachrome movie, recorded in the 80s, which I got from ebay for testing purposes. The lower central image is the final output image, the surrounding images are the raw captures from the HQCam, spanning a range of 5 exposure values. Clearly, at least this huge range is needed to capture all the information available in the film frame.

The different exposures were not realized by switching the HQCam to different exposure values. It turned out that this takes way too long time. This is a behaviour which was already noted with the older Raspberry Pi cameras, but it seems that the new HQCam is even slower to respond to changes in exposure time. So instead changing the exposure time of the camera, the LED-light source was switched rapidly to different light settings for capturing the different exposures. The HQCam was run at a constant exposure time of 1/32 sec.

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Thanks for the excellent info. I am still working on my rig and have limited time for hobby projects right now, but I was impressed by the dynamic range I found in the HQ Cam’s raw stills. However, 23 MB per image is a lot, and the DNG processing takes time and effort.

I captured a bit of raw video too, but failed to find anything that can decode it. The raw video should still have 10 bpc (and stills 12) — but how to convert it into something readable?

Well, I guess that depends. I am nearly always working with the picamera-library, and here, if you switch the camera to raw mode, you simply get the raw-data, together with a preview jpg, as a sequence of images out of the PiCamera-module. All you need to do is convert the raw Bayer image into something which resembles a more usual RGB-image.

Well, for this conversion, I wrote my own raw-converters in Python for the v1- and v2-cameras (each camera needs its own decoder), as I needed at that time the raw camera image in order to do lens-shading corrections and other stuff. I did not bother to write a new one for the HQCam, as I had no demand yet for one. I have not seen any Python code which does this in the wild either.

Besides the picamera lib, the other option to get raw image data out of the HQCam is to use “raspiraw”. This software seems to be recently modified to work with the HQCam, and it tries to write 12MPix, 12bit image data unpacked into 16bpp raw files. These files can be converted from the raw Bayer pattern they contain to something more usable via the “dcraw” program.

As far as I understand it raspiraw can be run also in a “video mode”. Never done this personally, but I guess in this mode raspiraw simply packs the raw frames one after the other into a large file. So you might try to cut out a single frame (each frame is of fixed size, so that’s not too difficult) and decode it after this with dcraw. But again, just guessing here, never bothered working with raw video or the Raspberry Pi Foundation software bundle.

A guy which is very active on the Raspberry Pi Camera Forum is Hermann Stamm-Wilbrandt. His scripts and insights might be of further help here.

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Thanks so much for these leads and hints.
https://github.com/schoolpost/PyDNG is what I used for IMX477 Debayering and it worked very well for stills).

For catpturing raw video, I had only tried raspivid with the -r option quickly.

Anyway, first step now is to get familiar with the picamera-library and python in general — haven’t ever touched python yet only C so far. :slight_smile:

Regarding JPG vs Raw quality, here are my first quick tests. This is a JPG “out of cam”:

And this is a JPG developed from the DNG I opbtained from pyDNG conversion:

And here is some detail comparison at 100%:

The reflexion of my desk lamp isn’t clipping in the Raw, and the detail of the bolt and nut are much better, too. There is also less fringing and the CA is correctible. Probbaly equally important, the lack of bilateral denoising in the Raw leaves a lot more detail, a temporal denoising approach should be much better here.

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To correct my quick comment a little bit:

Reading the description of raspiraw, it seems that the program writes out separate files, so no need to extract single frame from a larger file.

@peaceman: very interesting results! Indeed, your comparision shows clearly the heavy image processing which is nowadays so common and so unavoidable with consumer sensors.

By the way - even the raw image of the HQCam has already some image processing applied. I have seen noise measurement of raw HQCam captures which hint to some spatial filtering applied at the sensor level. Also, for certain there are dead-pixel algorithms at work before the HQCam ships out it’s “raw” frame.

Usually, it is hard to get such information, as the chip manufacturer tend to keep their secrets, and nearly always impossible to switch them off.

Anyway - what would be very interesting to test (if you have the time and setup) is to see how the 12bpp of the raw image file compare against the dynamic range of a standard Kodachrome color-reversal frame with large shadow areas.

To elaborate: color-reversal film is usually exposed so that the lights don’t burn out. During projection, detail loss in the shadows is not so annoying than burning out the highlights of the scene. Also, our eyes as “consumer” of projected film frames are surpassing the possibilities of current sensor chips by a wide margin.

In order to reproduce highlight details in a scan faithfully, usually an exposure value for a single frame capture would be chosen so that the brightest image parts are still within the dynamic range of the full digital image path.

For example, I am working with 8bpp (and HDR later in my processing pipelinep). So my exposure is set in such a way that the sprocket hole of the film (which is pure white by definition) maps to something in the 240-250 value range (the maximum value representable with 8bpp is 255). With the 12bpp of a raw HQCam image, you probably could go up to values of 4000+ or so for pure white.

Now it would be interesting to see how good the performance of such an exposure setting is performing not in the highlights, but in the darker image areas of a frame. Specifically, how far can you go toward darkness?

There are two interesting points here - first, there will be more noise in the lower bit channels of a capture. This will raise the noise floor in dark, dense image areas, as compared to other areas. Another point to look at would be the limited amount of bit variation available in these dense film areas. Namely, only a few bits of the image data will change here. This could lead to banding and quantization artifacts (it might not - that would be an interesting result).

From my experience, a normal Super-8 frame basically never shows real dense areas, even in very dark shadows. Usually, the black mask around the frame is the densest part you normally encounter (and you would probably not care to scan these areas in high fidelity). However, in a fade you might encounter really dark areas in the frame itself.

Well, it would be interesting to see the limits of a raw capture here, given the challenges of this approach - you have to cope with large file sizes, low transfer rates and the need to “develop” the raw frame into something displayable on todays 8bpp frames… (given, this last point is about to change with HDR-displays :smirk:)

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I’ll definitely run some Kodachrome tests once I got my light source sorted… currently trying to coat a ping pong ball with Bariumsulfate from the inside to make a mini “Ulbricht” intergation sphere. Lots of moving parts as you can see :slight_smile:

Your HDR results (like above) look really amazing, especially considering that you use output-referred 8bpc files. I’ve been remotely contributing to a HDR algorithm on my job and remember well that scene-referred input files (DNG from an iPhone in this case) helped a lot with proper tonemapping — so that one could darken e.g. the bride’s white dress without making the sun go gray. May I ask how you are compositing your output files? Is that a manual or an automatic process? And doesn’t taking 5 brackets + merging + tonemamapping take a substantial amount of time & processing, too?
[Edit: You answered this already earlier — I had to re-read the thread, sorry]

Regarding the “developing” of the Raw files into something eye-pleasing, I hope I’ll be able to create a camera profile for ACR, and if I can’t, I’ll try to bribe some colleagues to do so. (Disclaimer: I am a Product Manager for Adobe Lightroom, and ACR is built “next door”).

But we’ll see — first step now is the light source and getting going with some python scaffolding.

Well, the short answer is that it is an automatic process.

But here are some more details about my approach. Some background information first: my first image processing algorithms were coded in FORTRAN 77, I later switched for tens of years to “C” (and occationally C++/Java/Matlab/VHDL), but I finally ended up in doing most of my work in Python now. Shows my age, I guess… :sunglasses:

Initially, I actually implemented a real HDR capture pipeline with the Raspberry Pi v1-camera as a basis. That is, appropriate gain curves were estimated and a real HDR with image radiances was created from different exposures of a single frame. However, my hope to find some appropriate, generic tone-mapping algorithm for the very different material I had at hand did not realize in any way.

Having spend also some time researching the human visual system, it occurred to me at the time Mertens with others published their exposure fusion approach that this approach has a lot in common with the way our human visual system views the world.

Here’s what I wrote in another thread about this:

The above mentioned opencv-implementation is quite fast and usable, but I opted to write my own version. It is a little bit slower and but offers more ways to modify and tweak the basic algorithm. I am still in the process of optimizing this, but I am fine with the current results - especially because the process is fully automatic. No manual adjustments needed!

Here is my current work flow, with approximate timings included:

  1. Insert the film into the scanner, fix camera exposure to 1/32 sec, set the resolution to 2028 x 1520, set red and blue gain to a fixed value.
  2. Check for all 5 exposure values that the sprocket area is pure white, set analog and digital gain in such a way that in the lowest exposure setting the bright sprocket area shows values about 10 units less than maximum brightness. This ensures that any bright image areas of the film frame are properly recorded.
  3. Start scanning. Scanning consists of advancing the film one frame, waiting a fraction of a second for the film and mechanics to settle, and than taking 5 different exposures in short succesion by dimming the light source over a range of 5 exposure stops. This takes about 1.5 - 2 seconds for the 5 exposures.
  4. The images are transfered from the HQCam to a Pi4 which acts as a server to my LAN. Depending on the speed of the LAN, transfer might take additional 2 - 5 secs per film frame.
  5. The images are received by a client software which stores them to a disk. Currently, the client is also responsable to trigger the next frame capture, which introduces an additional delay. All in all, I usually calculate that for every second of Super 8 stock (with 18 fps) I need about 40 sec to a minute for scanning the material. So this approach is quite slow on the capture side.
  6. Once the capture is done, the set of five images are combined into a single output frame. The exposure fusion algorithm which does that is usually run not on the source resolution of 2028 x 1520 px, but on a smaller 1200 x 900 px resolution. At that resolution, it takes on my PC between 800 - 1100 msec to process a single frame. At full resolution of 2028 x 1520, the computing time for a single frame increases to 1900 - 2100 msec/frame. The 1200x900 px images are stored as a 16bpp RGB image on disk, with an average size of about 5MB.
  7. A final post processing program takes the 16bpp images and crops them 80% to get a final output image size of 960x720 px. At this stage, sprocket detection and alignment as well as color correction and grading is done. Also, a slight sharpening step is included here. This pipeline needs less than a second per frame (But I have not timed it really). The pipeline outputs the final frame as 8bpp RGB image with a size of 960 x 720 px.

Now, for me, 960x720 px is a resonable output format for my purposes (my old “Revue” camera combined with Agfa stock is far away from reaching even such a resolution… :upside_down_face:)

As the capturing and processing is slow, lowering the resolution at appropriate times in the pipeline gives me some gains in terms of processing speed. Actually, as capturing and post processing run unattended (a whole film is processed with the same settings), I usually do the processing overnight, just checking the results in the morning.

Anyway, I am still in the process of optimizing the whole thing. As this is the fourth or more version of my film scanner, I might end up with something totally different in a few months time…

Hope this wasn’t a too long response to read. Good luck with your light source, and it will be interesting to see what raw captures the HQCam can achieve!

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