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#1
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"Digital ICE" without Digital ICE
First, sorry for cross-posting, I admit I just posted to the two
groups where the topic of Digital ICE seems to come up most often. I'm a newcomer in digital photography and scanning, so the ideas I'll discuss below might very well be badly flawed - please bear with me. Ok, you have a flatbed scanner with a transparency adaptor; it's got two lamps, one below the glass and one above. Film is supposed to be scanned with the top lamp. But what happend if you scan film with the bottom lamp, possibly with the lid open and in a dark room? The scan will show an almost completely black film, since nearly no light passed through it. However, dust particles and white-ish scratches on the film will (or might) reflect the light from the bottom lamp, and thus will easily be spotted in the scan! (or will be after some histogram normalization). The rest is just a software process of applying dust-removal filters on the spots in the image we know dust lies. What do you think? I *have* actually tried this stuff and it *appears* to work and remove a number of defects from the final image - although many dust spots remains, and I haven't been able to find out whether the system works differently with dust that's *on the film* and dust that's *on the scanner*. However, after cleaning the scanner's glass as carefully as I could, I still think I'm spotting particles that do lie on the film surface. Then maybe it also depends which side of the film dust lies on. I'll post sample pictures and some sketched Bash scripts using NetPBM for the actual picture cleaning if anybody's interested. by LjL |
#2
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Lorenzo J. Lucchini wrote:
What do you think? I *have* actually tried this stuff and it *appears* to work and remove a number of defects from the final image - although many dust spots remains, and I haven't been able to find out whether the system works differently with dust that's *on the film* and dust that's *on the scanner*. Interesting concept, and should somewhat work in theory, although it will only work for "noise" that's between the film/negative and the scanner head. Dust that's on "top" of the film (facing the upper light head) won't be detected by the reflective scan. Also, if the noise is severe enough to block light on a transmissive scan, the reflective scan may give you a "cancellation" image for it, but the information in the film itself will still be obscured by it. |
#3
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Interesting idea, but you'd need your object to be in the exact same place
when you lifted the lid. Maybe it could work if you put glass over your orig. Cheers, Jason (remove ... to reply) Video & Gaming: http://gadgetaus.com |
#4
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Matt Ion wrote in message news:9nhfd.23441$%k.13864@pd7tw2no...
Lorenzo J. Lucchini wrote: What do you think? I *have* actually tried this stuff and it *appears* to work and remove a number of defects from the final image - although many dust spots remains, and I haven't been able to find out whether the system works differently with dust that's *on the film* and dust that's *on the scanner*. Interesting concept, and should somewhat work in theory, although it will only work for "noise" that's between the film/negative and the scanner head. Dust that's on "top" of the film (facing the upper light head) won't be detected by the reflective scan. I'm not sure. It's true that there is dust that doesn't get detected, but after a few experiments my opinion is that it's either because they're too small, too dark to reflect enough light, or out-of-focus (ie on the glass). What you say is probably true if I do the "defect scan" with the lid closed, which reduces contrast between image data and noise data, so making the particles that lie on top of the film too dim for detection; with the lid open, however, that doesn't seem to be the case. (Actually, it obviously *will* be the case for particles that lie on top of a *dark region* of the film, but those particles won't do much damage to the final image anyway) Also, if the noise is severe enough to block light on a transmissive scan, the reflective scan may give you a "cancellation" image for it, but the information in the film itself will still be obscured by it. Sure, but AFAICS this applies to real Digital ICE as well - if information is lost, it's lost, but we can try to mask the loss by interpolation or some like technique. On the topic of the noise-masking algorithm... what I do now is simply - create a very blurred version of the scan - superimpose the blurred version onto the original version, using the "defect scan" as an alpha channel so that the blurred version is only shown where there are defects However, this doesn't seem to work very well, since the color of the final image looks quite a bit different from what you'd expect in the noise spots. What's a better idea? I've read Digital ICE simply divides every image pixel by the corresponding pixel in the "defect scan" (an IR scan in ICE's case). After trying this, though, it doesn't seem to work well at all: the features of dust spots in my "defect scans" probably differ a lot from ICE's IR scans. Now I'm entertaing myself with the idea of using a median filter, which seems to preserve pixel colors fairly well while removing most defects; what do you think? I'm not at home at the moment, but I'll upload some of my scans as soon as I'm back. by LjL |
#5
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Matt Ion wrote in message news:9nhfd.23441$%k.13864@pd7tw2no...
Lorenzo J. Lucchini wrote: What do you think? I *have* actually tried this stuff and it *appears* to work and remove a number of defects from the final image - although many dust spots remains, and I haven't been able to find out whether the system works differently with dust that's *on the film* and dust that's *on the scanner*. Interesting concept, and should somewhat work in theory, although it will only work for "noise" that's between the film/negative and the scanner head. Dust that's on "top" of the film (facing the upper light head) won't be detected by the reflective scan. I'm not sure. It's true that there is dust that doesn't get detected, but after a few experiments my opinion is that it's either because they're too small, too dark to reflect enough light, or out-of-focus (ie on the glass). What you say is probably true if I do the "defect scan" with the lid closed, which reduces contrast between image data and noise data, so making the particles that lie on top of the film too dim for detection; with the lid open, however, that doesn't seem to be the case. (Actually, it obviously *will* be the case for particles that lie on top of a *dark region* of the film, but those particles won't do much damage to the final image anyway) Also, if the noise is severe enough to block light on a transmissive scan, the reflective scan may give you a "cancellation" image for it, but the information in the film itself will still be obscured by it. Sure, but AFAICS this applies to real Digital ICE as well - if information is lost, it's lost, but we can try to mask the loss by interpolation or some like technique. On the topic of the noise-masking algorithm... what I do now is simply - create a very blurred version of the scan - superimpose the blurred version onto the original version, using the "defect scan" as an alpha channel so that the blurred version is only shown where there are defects However, this doesn't seem to work very well, since the color of the final image looks quite a bit different from what you'd expect in the noise spots. What's a better idea? I've read Digital ICE simply divides every image pixel by the corresponding pixel in the "defect scan" (an IR scan in ICE's case). After trying this, though, it doesn't seem to work well at all: the features of dust spots in my "defect scans" probably differ a lot from ICE's IR scans. Now I'm entertaing myself with the idea of using a median filter, which seems to preserve pixel colors fairly well while removing most defects; what do you think? I'm not at home at the moment, but I'll upload some of my scans as soon as I'm back. by LjL |
#6
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I've uploaded a picture cleaned using my method.
You can find the original slide as scanned at http://ljl.150m.com/slide_original.jpg The cleaned picture is at http://ljl.150m.com/slide_clean.jpg The "noise map" as scanned (lid open, little ambient light) is at http://ljl.150m.com/slide_map.png There is post-processed version of the noise map, which is the one actually used as the alpha channel, at http://ljl.150m.com/slide_nmap.png All the scans were made at 1200 dpi, 24-bit color with an Epson RX500. The "image scan" and the "noise scan" have been hand-aligned, since my scanner doesn't pick up the same image area in film mode as in flatbed mode. The final images have been scaled down (Paint Shop Pro, "Pixel Resize") to be 800 pixels wide. As you can see, I didn't get the histogram stretching quite right for the "normalized noise map": I made the darkest 97% of pixels black, and the brightest 0.1% white, which seems to be definitely overkill. I need to experiment a bit more with this. You can see how I also applied a convolution (whatever a convolution is) to dilate the noise spots, in order to avoid the edges of the dust particles to show up in the final image. by LjL |
#7
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I've uploaded a picture cleaned using my method.
You can find the original slide as scanned at http://ljl.150m.com/slide_original.jpg The cleaned picture is at http://ljl.150m.com/slide_clean.jpg The "noise map" as scanned (lid open, little ambient light) is at http://ljl.150m.com/slide_map.png There is post-processed version of the noise map, which is the one actually used as the alpha channel, at http://ljl.150m.com/slide_nmap.png All the scans were made at 1200 dpi, 24-bit color with an Epson RX500. The "image scan" and the "noise scan" have been hand-aligned, since my scanner doesn't pick up the same image area in film mode as in flatbed mode. The final images have been scaled down (Paint Shop Pro, "Pixel Resize") to be 800 pixels wide. As you can see, I didn't get the histogram stretching quite right for the "normalized noise map": I made the darkest 97% of pixels black, and the brightest 0.1% white, which seems to be definitely overkill. I need to experiment a bit more with this. You can see how I also applied a convolution (whatever a convolution is) to dilate the noise spots, in order to avoid the edges of the dust particles to show up in the final image. by LjL |
#8
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Lorenzo J. Lucchini wrote: The cleaned picture is at http://ljl.150m.com/slide_clean.jpg Hi Lorenzo... It appeasts that slide_clean.jpg "cannot be shown because it contains errors" Perhaps upload it again? Ken |
#9
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Lorenzo J. Lucchini wrote: The cleaned picture is at http://ljl.150m.com/slide_clean.jpg Hi Lorenzo... It appeasts that slide_clean.jpg "cannot be shown because it contains errors" Perhaps upload it again? Ken |
#10
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