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  • TopaTopaz Labs - Topaz Momentz Labs - Topaz DeJPEG
  • Topaz Labs - Topaz DeJPEG
  • Topaz Labs - Topaz DeJPEG

Topaz Labs - Topaz DeJPEG

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Product Description

Topaz DeJPEG is the best way to enhance JPEG picture quality by eliminating JPEG artifacts, fuzziness, and image blur. The intuitive but powerful tools in Topaz DeJPEG ensures that each JPEG image looks as good as it can possibly be.

What are JPEG artifacts? The JPEG file format is very popular because of its small file sizes and decent quality. However, every single JPEG image always has a few problems associated with it. The most prominent of these problems are "mosquito noise" around edges, "checkerboarding," and color degradation. Every single JPEG image exhibits these characteristics to some degree. Topaz DeJPEG specializes in removing these artifacts without removing image detail. In fact, Topaz DeJPEG is the theoretically optimal way to eliminate these compression artifacts.

Use the Best in JPEG Enhancement Technology: Topaz DeJPEG is the first of its kind to use optimal algorithms to efficiently remove JPEG compression artifacts while preserving image detail. It provides unparalleled image enhancement features and combines them with powerful artifact recognition capabilities - giving you ultimate control over the quality of your JPEG image. Topaz DeJPEG v3.0 offers unique new features including rapid processing speeds, noise suppression tools, incredible detail preservation, and multiple color views that work together to make your JPEG images look great.

Clarify Original Detail and Preserve Image Integrity: DeJPEG's underlying algorithm examines the entire image and removes all artifacts while preserving detail and enhancing an image's natural qualities. Topaz DeJPEG is specifically designed to target all aspects of JPEG quality including sharpness, noise, and color integrity. The sophisticated tools in Topaz DeJPEG ensure that any processed JPEG image appear clear, clean, and crisp.

Sharpen Color Edges: DeJPEG works to counteract the unwanted "bleeding" and color information loss that occurs even in high-quality JPEG images as a result of the compression process. Because of the limitations in storage and bandwidth that accompany JPEGs, image color quality is frequently lost. With revolutionary color sharpening and edge restoration tools, Topaz DeJPEG actually reconstructs softened color edges, bringing out previously unseen image details, raising existing ones to their optimal quality, and restoring sharpness to diminished edges. This is highly useful in many Photoshop tasks like masking, but also makes the image look much cleaner in general.

Topaz DeJPEG combines a well-developed sensitivity to image detail with keen power in artifact reduction to bring clarity, balance, and precision to the JPEG image restoration and enhancement process.

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By clicking the Buy Now button you will be forwarded to Topaz Labs' website to make your purchase. A percentage of each sale goes to support the NatureScapes website and photography community.


Additional Details:
  • 16-bit Support
    Topaz DeJPEG supports both 8-bit and 16-bit RGB color modes.

  • PS CS4 Compatible
    Topaz DeJPEG is fully compatible with the latest Photoshop versions on both Mac and Windows. Topaz DeJPEG is also fully compatible with Windows 64-bit Photoshop CS5.

  • Smart Filter
    Topaz DeJPEG is a smart filter and supports non-destructive editing by using Photoshop smart objects.

  • Multi-core Optimized
    Topaz DeJPEG is completely optimized for computers with multiple CPUs for faster processing.

System Requirements for Windows & Mac:


  • Windows XP, Windows Vista, or Windows 7 (32-bit and 64-bit)
  • 1 GB RAM
  • Adobe Photoshop 7 through CS5, Adobe Photoshop Elements 1-8, or another editor that supports Photoshop plug-ins. These include: Irfanview, PaintShop Pro, etc.
  • Intel-based Mac OS 10.4-10.6. PowerPC processors like G4 or G5 are not supported.
  • 1 GB RAM
  • Adobe Photoshop CS3-CS5, Adobe Photoshop Elements 6-8. Apple Aperture 2, Lightroom for Mac, and iPhoto now supported.

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