Automated Image Quality Correction Using Machine Learning
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Solution Overview
Problem
Manual review of product images for quality standards on e-commerce websites is time-consuming, costly, and prone to subjective inaccuracies due to the high volume of images that need to be evaluated.
Innovation Solution
A machine learning model, such as a convolutional neural network, is trained to detect and correct image quality issues based on predefined standards, including resolution, blurriness, and textual information, automatically adjusting image features to meet quality thresholds and indicating unusable images when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of images is performed to ensure quality standards, then measurement precision of image quality is improved, but productivity deteriorates due to time-consuming processes
Solution Approach 1:
The patent replaces the manual mechanical review process with an automated machine learning-based image processing system. The system uses trained models to automatically assess image quality parameters (sharpness, exposure, composition, etc.) and perform corrections, substituting human operators with computational algorithms that process images at machine speed while maintaining consistent quality standards.
2Reliability
If manual review of images is performed to ensure quality standards, then reliability of quality assessment is improved, but loss of time increases due to high volume of images
Solution Approach 1:
The system performs preliminary automated quality assessment and correction before images are fully processed or displayed. By pre-evaluating images against quality standards using machine learning models and applying corrections in advance, the system ensures reliability of quality compliance while eliminating the need for time-consuming manual review at later stages.
3Productivity
If automated machine learning model is used to process images, then productivity is improved, but manufacturing precision deteriorates due to automated processing
Solution Approach 1:
The system incorporates feedback mechanisms where machine learning models continuously learn from processed images and correction outcomes. The automated system uses feedback loops to refine quality assessment criteria and correction algorithms, improving manufacturing precision over time while maintaining high productivity through automated processing of large image volumes.
Data Source
AI summary
This application relates to systems and methods for automatically detecting and correcting image quality based on a set of quality standards. In some examples, a plurality of quality parameters of an image are determined based on receiving an image. It is then determined that at least one of the plurality of quality parameters is below a predetermined threshold. The predetermined threshold may be based on a required quality standard for images. In response to determining that the at least one of the plurality of quality parameters is below the predetermined threshold, feature of the image is adjusted such that the at least one of the plurality of quality parameters is at or above the predetermined threshold.