Studio Image Retouching for Prop Seam and Joint Removal
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Solution Overview
Problem
Existing image retouching systems for studio images, particularly in online commerce platforms, struggle to achieve a consistent appearance by effectively removing noisy backgrounds, aligning products and props, adjusting aspect ratios, and eliminating prop seams and joints, which affects consumer confidence and product presentation.
Innovation Solution
An image retouching system utilizing machine learning models, including background removal, alignment, vertical cropping, joint and seam recognition, segmentation, and aspect ratio adjustment, to transform studio images into a consistent and appealing format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If automated retouching processes are applied to remove prop seams and joints, then the visual appeal and consistency of product images is improved, but the complexity of the image processing system increases
Solution Approach 1:
The image processing system is divided into multiple specialized machine learning models, each handling a specific retouching task such as seam removal, joint removal, background removal, and alignment. This segmentation allows complex image retouching to be achieved through coordinated simple operations from multiple models.
Solution Approach 2:
The machine learning models automatically detect and correct image defects without human intervention. The system self-adjusts by identifying prop seams, joints, and misalignments, then applying appropriate corrections autonomously to achieve consistent product imagery.
2Productivity
If machine learning models are used for automated retouching, then productivity and efficiency are improved, but the computational resources and processing time required increase
Solution Approach 1:
Multiple machine learning models are trained in advance on large datasets of product images with various defects. This preliminary training enables the models to quickly and accurately detect and correct issues like prop seams, joints, and misalignments during actual image processing without requiring extensive real-time computation.
3Manufacturing precision
If prop joints and seams are removed from images, then the visual appeal and consumer confidence are improved, but the ability to verify product authenticity and details is reduced
Solution Approach 1:
The machine learning models apply selective retouching only to specific regions containing prop seams and joints, while leaving the rest of the product image unchanged. This localized approach removes unwanted artifacts from support props while preserving all important product details, features, and authentic characteristics.
Data Source
AI summary
Methods and apparatus for automated retouching of studio images are disclosed. In one embodiment, a method is provided that includes receiving an image that shows an item and a prop. The prop supports the item and includes a joint that forms a seam that is visible in the image. The method also includes modifying the image such that the seam is not visible in the image and the item in the image is unaltered.