Neural Network Image Segmentation for eCommerce Product Photography
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Solution Overview
Problem
Conventional methods for improving digital image quality in eCommerce product photography, especially in home setups, often result in sub-optimal images due to varying lighting conditions and require significant time and expertise for editing, which many sellers cannot afford or manage effectively.
Innovation Solution
A computer-implemented method and cloud-based system using an artificial neural network for segmenting images, removing boundary artifacts through curve fitting, and enhancing images with glare correction and artificial lighting, allowing for professional-grade image enhancement without the need for specialized equipment or expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If sellers use less expensive lighting setups to reduce costs, then capital expenditure is reduced, but image quality deteriorates and photographs appear dull
Solution Approach 1:
The patent replaces physical lighting equipment (mechanical/optical system) with computational image processing algorithms. Instead of investing in expensive professional lighting hardware, the system uses software-based enhancement techniques including neural network segmentation, boundary artifact removal, and synthetic lighting application to achieve professional-quality images from budget equipment
Solution Approach 2:
The system changes the parameters of the captured image through digital processing - adjusting lighting parameters, color balance, and boundary characteristics computationally. This allows the same image to be transformed from dull/low-quality appearance to professional-grade appearance without changing the physical capture equipment
2Manufacturing precision
If sellers manually edit photographs using photo editing applications to improve quality, then image quality can be enhanced, but time consumption increases and requires specialized expertise
Solution Approach 1:
The system enables automatic self-service image enhancement without requiring seller intervention or expertise. The neural network automatically segments images, identifies boundary artifacts, removes artifacts, and applies appropriate lighting corrections, freeing sellers from time-consuming manual editing while maintaining high image quality
Solution Approach 2:
Manual photo editing operations are replaced with automated neural network processing. The system substitutes human expertise and manual manipulation with algorithmic image processing that performs segmentation, artifact removal, and enhancement automatically, eliminating both time loss and expertise requirements
3Manufacturing precision
If professional photographers are hired to perform product photography, then image quality improves significantly, but costs increase beyond what many sellers can afford
Solution Approach 1:
The system creates a computational copy of professional photography effects. Instead of hiring professional photographers who capture images with professional equipment, the system applies computational algorithms that replicate and enhance the appearance of professionally captured images, making professional-quality results accessible to budget-conscious sellers
Solution Approach 2:
The patent substitutes the entire professional photography workflow (professional equipment + professional photographer expertise) with an automated computational system. This replacement achieves comparable or superior image quality through algorithms while eliminating the high costs associated with professional services
4Manufacturing precision
If images are segmented using neural networks to remove boundary artifacts, then image quality and realism improve, but processing complexity increases
Solution Approach 1:
The patent applies segmentation to divide the image processing task into distinct components: background segmentation, object segmentation, and boundary identification. This segmentation allows targeted removal of boundary artifacts while preserving object integrity, improving image quality through systematic processing of different image regions
Solution Approach 2:
The neural network acts as an intermediary that automatically handles the complex task of boundary artifact identification and removal. Rather than requiring complex manual processing, the neural network mediates between the raw captured image and the final enhanced image, automatically performing the sophisticated operations needed for artifact removal
Data Source
AI summary
The present disclosure relates to a computer-implemented method for generating an enhanced image from an original image, the method including segmenting the original image into a segmented image using an artificial neural network; curve fitting the segmented image to determine boundary artifacts; removing the determined boundary artifacts to generate a smoothed boundary image; and generating the enhanced image from the original image and the smoothed boundary image. The image maybe enhanced further by correcting for glare and adding artificial light.