Blurred Image Correction Using ML Blur Detection and Stitching
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Solution Overview
Problem
Existing artificial intelligence systems struggle with identifying and correcting blurs in images, leading to inefficient manual review processes and security risks, particularly in client communications, due to the lack of a framework to detect blur locations, determine appropriate responses, and automatically correct errors.
Innovation Solution
A system using machine learning models to identify blur types, apply filters or stitching techniques to correct blurs, and request additional images when necessary, ensuring efficient and accurate image rectification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review is used to handle blurred images, then accuracy in determining whether text extraction is feasible is improved, but productivity and efficiency deteriorate due to time and resource consumption
Solution Approach 1:
The system enables automatic self-assessment of image quality through machine learning models that autonomously detect blur, evaluate text extractability, and determine appropriate actions without requiring manual human review, thereby maintaining accuracy while dramatically improving processing efficiency
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated machine learning-based system that uses trained models to detect blur characteristics and make determination decisions, substituting human labor with computational algorithms to achieve both accuracy and efficiency
2Reliability
If all blurred images are flagged for manual review, then security risks are reduced through human oversight, but device complexity and operational overhead increase
Solution Approach 1:
The system applies differentiated handling based on local image characteristics by analyzing specific blur properties and locations to determine whether each image requires manual review or can be processed automatically, rather than applying a uniform review requirement to all blurred images
Solution Approach 2:
The machine learning system autonomously assesses security risks by evaluating blur characteristics and making determination about text extractability, eliminating the need for complex manual review workflows while maintaining security through accurate automated assessment
3Ease of manufacture
If existing blur detection systems are used, then simple blur identification is achieved, but the ability to identify specific blur types and locations deteriorates due to lack of detailed analysis framework
Solution Approach 1:
The system segments the image analysis process into distinct components: blur detection, blur type classification, and location identification, with each component handled by specialized machine learning models that work together to provide comprehensive and precise blur analysis
Solution Approach 2:
The machine learning system is designed with multi-functional capability to perform multiple tasks including blur detection, type identification, and location determination within a unified framework, maintaining ease of use while achieving high measurement precision through versatile model architecture
Data Source
AI summary
Methods and systems are described herein for identifying the location and nature of any blur within one or more images received as a user communication and generating an appropriate correction. The system utilizes a first machine learning model, which is trained to identify blurred components of inputted images and determine whether the blurred components are located in portions of the inputted images comprising textual information. The system may apply a corrective action selected by the first machine learning model, which may comprise stitching blurred images together to a sharp product image and/or some other method appropriate for rectifying images received.