Related topaz gigapixel ai
Going from four megapixels to 96 is a stretch, but just now found that a six megapixel Canon 300D from 2003 mostly beats a 20 megapixel 1" sensor camera from 2016 when AIG resized them both to 24 megapixels (I suspect a soft lens on the 1" camera, but still.) I've been upsampling from camera ranging from four megapixels to 24 megapixels, to 24-96 megapixels. When it works well, it's incredibly impressive. What is your workflow? What quality are the images you are resizing? What were the inconsistent results? How are you evaluating the results? On a 100 PPI monitor? (24" 1920x1080), 300+ PPI monitor? (4K laptop, or standing back from a larger 4K display) Making large prints at at least 300 PPI? I got some very impressive, but inconsistent results. Yes, it has been discussed in several threads. It upscales your images using neural networks to fill in the details. I think you'd agree in all of the originals above, the ID is quite clear without any processing work done.īTW - I did try it out on some much worse photos - really bad environmental conditions, terrible lighting, extreme distances, and crops of more than 100%.and yes, it did improve the cropped results.but those would definitely not be reliable for ID purposes, as the original photos were already borderline for IDs.I decided to do a quick test with the Topaz A.I. Basically, I think the original before any processing has to be clear enough for a confirmed ID in order for any version of the photo to be presented as an ID proof. However, I'd also point to the other side in the above use-cases, that the originals before processing were still clear enough and large enough for ID purposes, so that original could be used for the ID credit or proof, and then the more presentable version used for display.
#Related topaz gigapixel ai software
I agree in principal with that - certainly a shot which is a tiny speck of a bird barely even visible, then enlarged with software creating pixels that weren't there, can badly skew or create patterns or colors that aren't there and shouldn't be relied on for proper IDs. Original of a blue grosbeak - fairly far away and simple crop yielded low resolution and not much detail: Some examples (I have resized all of the shots for posting online, so the originals were all in the 6-8MP I mentioned - I resized them to no more than 1800 pixels on the long side for this thread): In all these cases, I was working with JPG originals, not RAW. I also had a few shots where the final crop might have been around 6MP, but were a little soft or lacking in fine detail.I used the same process, enlarging to 4x and resizing back down. I used Gigapixel to enlarge them 4x, then resized back down to the 6-8MP range. My typical workflow was to take shots that involved heavy crops - where the bird was a small part of the frame, and see if I could get a photo that had a reasonable resolution for display or print - at least in the realm of 6-8MP.many of these after the original crop would have been down to 3-4MP. The newer uprez programs like Gigapixel claim to do a more intelligent job at predicting and calculating those additional pixels in the enlargement so that you can get more resolution that's close to the original pixels - and can be resized back down to reasonable sizes for nice detail and sharpness improvements, removing noise, and even processing as if it were an original photo - brighten, white balance changes, etc.
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Normal enlargement doesn't create new pixels to build up a shot - they just enlarge the pixels there, or duplicate them. I recently picked up Gigapixel AI to see if it could do what I hoped, which is take shots that were fairly heavy crops, therefore low resolution and/or missing fine details, and try up-rezzing them with an intelligent enlargement tool. I have been using Topaz's Denoise and Sharpen for a while, and both are good tools for cleaning up shots especially for birders, when we have to go to very high ISOs or deal with a little motion blur.