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In this quick tutorial, I compare DCB and IGV demosaicing (debayer) algorithms in MLVApp for 3x3 1080p Magic Lantern RAW footage, showing how each affects sharpness, detail, color, moiré, and aliasing. After processing, I also demonstrate a simple enhancement workflow using Topaz AI to upscale and refine the final image. Perfect for Canon EOS M and other Magic Lantern users looking to minimize artifacts and maximize image quality from their RAW footage. 00:00 Intro 00:22 MLVapp workflow 13:30 Topaz AI workflow 23:48 Final result Some AI stuff i found: 1) DCB Directional Correlation-Based (DCB) algorithm during the debayering (or demosaicing) process of a digital image. The DCB algorithm is one of several available methods for this interpolation process. Performance: It produces results that are similar to the high-quality AMaZE (Aliasing Minimization and Zipper Elimination) method. Application: It is particularly effective for daylight images and tends to produce images with minimal color artifacts or "ringing" (unwanted lines or halos near edges). Considerations: Like AMaZE, the DCB method might produce "mazing" artifacts (specific color issues) when working with certain wide-angle lenses that cause light crosstalk between pixels. 2) IGV IGV algorithm, a de-bayering (demosaicing) algorithm used in astrophotography to process images from a camera sensor's raw data. In the context of image processing, the IGV de-bayering algorithm is known for its performance on noisy images, but it can lose some color information, and it is a computationally expensive option. Application: It is often used in astrophotography, especially for processing noisy images, as it is effective at handling noise. Trade-offs: It can sometimes lose color information and is computationally expensive compared to other algorithms. Wanna support the channel / Buy LUT? patreon.com/magiclanterncentral #magiclantern #gyroflow #topazai