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6 Best Image Upscalers Tested: Photo Upscaling Without the Plastic Look

6 Best Image Upscalers Tested: Photo Upscaling Without the Plastic Look

For most of the history of digital imaging, enlarging a photo meant accepting a loss of sharpness and detail. Early interpolation methods like bicubic resampling could only do so much, and the results at aggressive scales were predictably soft or blocky. AI-based upscalers changed the equation by learning from vast datasets of images, allowing them to reconstruct plausible fine detail rather than simply averaging surrounding pixels.

The benchmark has shifted accordingly. Today, virtually every serious AI upscaler can produce a technically sharp result at 2x, 4x, or even higher magnifications. What separates them is how that sharpness is achieved. Some tools tend to hallucinate texture - generating detail that looks crisp but reads as synthetic, with skin appearing waxy and fabric losing its weave character. Others err in the opposite direction, preserving tonal authenticity at the cost of genuine resolution gain.

PetaPixel's hands-on comparison of six tools examines this quality axis directly, putting the focus on perceptual fidelity rather than raw pixel counts. The contenders span a range of approaches and price points, from standalone desktop applications to cloud-based services and plugin integrations for Lightroom and Photoshop. That breadth matters because the right tool often depends on workflow context - a studio photographer batch-processing RAW files has different needs than someone restoring a single scanned print.

For photographers, the practical takeaway from testing like this is that model generation and default settings matter as much as the underlying technology. Many tools have introduced multiple processing modes - some optimized for faces, others for landscapes or fine art reproduction - and choosing the wrong mode can produce worse results than an older, simpler tool used correctly. As these models continue to improve, the ceiling will likely keep rising, but the risk of over-processed output is not going away, making critical evaluation an ongoing necessity rather than a one-time decision.

Read at PetaPixel →
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