![topaz denoise 6 webinars topaz denoise 6 webinars](https://edwinjonesphotography.com/img/s/v-3/p54319622-4.jpg)
This is critical: You need to send your RAW file to DeNoise AI at the very beginning of your editing workflow to get the most benefit from the RAW model. When should I send my RAW files to DeNoise AI? The new DeNoise AI RAW model is the next step to provide outstanding noise reduction results without sacrificing image quality, but we’re still working to improve workflow functionality. We’ve extensively discussed photo editing workflows on the Learning Center, and it is, after all, one of the most popular topics that we keep hearing.
![topaz denoise 6 webinars topaz denoise 6 webinars](https://i.ytimg.com/vi/6WTKr0nPtHs/maxresdefault.jpg)
We also have this great article that dives into the differences between RAW and JPEG files.
TOPAZ DENOISE 6 WEBINARS FULL
In other words, we designed our new RAW model to take full advantage of your unprocessed RAW files, allowing us to deliver superior noise reduction, especially when compared with using JPEG files. The result is an output image that is more detailed with fewer artifacts and aliasing. Using the Bayer filter data allows DeNoise AI to produce better image detail while minimizing residual patterns. Typically, a demosaicing algorithm is then applied to interpolate a red, green, or blue value for each pixel, resulting in a 3-channel processed image file.
![topaz denoise 6 webinars topaz denoise 6 webinars](http://lh4.ggpht.com/-5ztBORhshGE/VByllRjmc-I/AAAAAAAAKWg/hgJX4hsn8D8/image%255B13%255D.png)
The Bayer filter pattern contains four channels for each pixel on the camera sensor: two green, one red, and one blue. The primary benefit of using the Bayer filter data as a file input is the number of color channels, and most cameras use it when saving RAW files. The RAW model also does the demosaicing itself, which should be much better than traditional demosaicing algorithms because we use the original Bayer filter data that the camera sensor records as the input source. Using Bayer filter data for improved results The RAW model can now learn the unchanged noise patterns and apply better noise reduction without the inherent flaws of processed RGB files. We can eliminate this extra effort with our new RAW model by utilizing the pure RAW data from the camera, where noise is ideal. Also, the RGB images can go through additional editing processes by the users, which we also account for in our RGB model training.Īs a result, a great deal of effort is required to cover all possible variances when using RGB models to apply noise reduction. When we train a model for RGB images, we take the variations of these RAW converters into account. Different RAW converters use varying conversion methods that change the noise pattern of the sensor data. Unfortunately, RAW converters introduce clipping, demosaicing, and other post-processing steps when converting the RAW data to an RGB image. Currently, the supported workflow requires you to convert your RAW file to a processed RGB file format that DeNoise AI uses as the input source. Let’s say you want to use DeNoise AI to apply noise reduction to a RAW photo you have stored in your Adobe Lightroom Classic catalog. The best way to explain the benefits of the new RAW model is to compare how it works with our four existing DeNoise AI models: Standard, Clear, Low Light, and Severe Noise. Here are some example photos that showcase just how powerful the new RAW model is. Our RAW model uses all of that rich sensor data to provide results that are cleaner than anything else out there, even our existing models! We also made serious improvements to our DNG output support, so you’ll still be able to edit your saved image files with the same precision as your RAW files. We use whatever image data you send to DeNoise AI, so you can imagine how much cleaner your noise reduction results could be when you send RAW sensor data as opposed to processed RGB data (like you’d find in a JPEG file). We’re SO excited about our newest model in DeNoise AI v.3.3, built specifically to take advantage of the vast amount of image data in your RAW files.