Below, the image on the right is the raw NEF file with Lightroom adjustments. A little darker and more saturated perhaps. The color differences aren’t bad, just different. Once you do this, you’ll probably notice some slight color differences between your original developed raw image and the denoised dng file with the settings synced. But all that is simple to fix by syncing the metadata and the develop settings from your original raw file to the newly saved dng file. Also, any metadata you may have added, like keywords and captions, will not come along for the ride. Since the RAW model in DeNoise AI is using the raw sensor data, when the file is saved as a DNG file, it will look like the original raw file, even if you’ve edited the original raw file in Lightroom’s Develop module. See this article on the Topaz site for the two workflows. On a Mac, one can simply drag the raw image thumbnail from Lightroom’s Grid view to the DeNoise AI app icon on the Mac.In Windows, it’s a little convoluted.
#TOPAZ DENOISE AI BEFORE SHARPEN AI MAC#
The workflow for using the RAW model is different on a Mac than it is on a PC. Starting now, I’ll no longer convert images to DNG, at least not all of them. And who knows, they might even develop a RAW model for their other applications like GigaPixel AI and Sharpen AI. While I still prefer using DNG for the reasons mentioned above, I want to be able to take advantage of the new RAW model in DeNoise AI.
DeNoise AI did an amazing job on those images already, so I’m not complaining.īut going forward, I’m going to want to change my workflow when it comes to converting to DNG files. This means I cannot go back and use the new RAW model of DeNoise AI on my existing aurora and other night images. The downside, you may ask? The new RAW model does not yet work with all raw files (Fuji), nor does it work with DNG files. Image on left treated with DeNoise AI RAW model. DeNoise AI 3.3 used for image on the left. See the comparison images below, in both images, the deNoise treated image is on the left (click on images to enlarge): Mountain detail. (And I thought I was thrilled with DeNoise AI before this new version.) You can then save the image as DNG if you wish to take the greatest advantage of the Lightroom Develop module or Camera Raw in Photoshop. All using the raw sensor data from the camera. Not only does it do a great job on the noise, but it does an amazing job of clarifying and sharpening images at the same time. The new RAW model works from the original raw sensor data. As the name implies, it works on RAW files that can then be saved as tiff, psd, jpg, png, or dng. Topaz has added a new noise reduction model to DeNoise AI called RAW. That workflow just got a kick in the pants with the latest version of Topaz DeNoise AI (3.3). The reasons I do this are that DNGs don’t need xmp sidecar files and they have a smaller file size than the raw files from the camera, in my case, Nikon NEF files. The last thing I do before I send the images off to their archiving home is to convert them to DNG (deleting the original raw files). Mostly I just want to get the images imported, culled, and add metadata and ratings. But I usually don’t do a lot of image editing at this point. Some will go to Photoshop for anything that can’t be done in Lightroom. I usually will do some image editing at this point, mostly in Lightroom but some will go to DeNoise AI and saved as tiff files. I’ll delete all the images marked with an X and then make sure all remaining images have keywords, captions, and location information. Subsequent passes may increase star ratings and usually add a few more images to the delete pile.
And I add bulk and then specific metadata.
#TOPAZ DENOISE AI BEFORE SHARPEN AI SERIES#
I add color labels to images that are to become part of a panorama, exposure or focus blending series of images, teaching images, etc. Up until now my workflow for new images brought into Lightroom has been to do a few passes of culling, which involves marking some images with either a star for early favorites, a reject tag (X) for definite deletes, and no markings for decent but not outstanding images.
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