Technology Innovation Dimension
ImgEditor's restoration suite incorporates three types of AI techniques: partial convolution based on Pconv to deal with defective areas, detail reconstruction using SRGAN, paired with ColourNet for color correction. Its photo restoration tool achieved a structural similarity (SSIM) of 0.91 in the MIT5K dataset test, far exceeding traditional interpolation methods. It is particularly good at repairing scratches on 35mm film, intelligently distinguishing between picture content and physical damage.
Workflow optimization
Feedback from professional photographers shows that the time spent on old photo restoration projects has been shortened from an average of 4.5 hours/picture to 20 minutes, and the basic restoration work of 90% can be completed automatically by AI. The platform's batch mode supports simultaneous uploading of 50 images for automated restoration, with improved quality consistency over manual operation.60%. The intelligent preset function also learns the user's retouching habits, allowing post-processing efficiency to continue to improve.
Industry impact data
- Studio business: old photo restoration order-taking grows 320%
- Digitization of archives: 7-fold increase in scanning efficiency in public institutions
- Family Memory Preservation: Individual User Restoration Demand Increases 450% Annually
This answer comes from the articleImgEditor: AI tool for image editing and generationThe
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