AI Image Artifact Detection and Remediation in Digital Capture
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Solution Overview
Problem
Existing digital photography systems suffer from various image artifacts such as Moiré patterns, rainbow effects, and horizontal black lines, which are difficult to mitigate through current post-processing techniques and require expensive high-end DSLR cameras with advanced semiconductor circuitry.
Innovation Solution
An image artifact remediation system using a driver-level or application-level recognition algorithm to detect artifacts in real-time, adjusting camera settings, and employing generative AI models to correct residual artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If post processing capabilities are applied to limit artifact visibility, then artifact visibility is reduced, but the remediation is limited and cannot totally eliminate artifacts
Solution Approach 1:
The patent replaces traditional mechanical post-processing methods with machine learning-based artifact detection and remediation. The system uses trained neural network models to automatically identify and correct artifacts, substituting manual or algorithmic image processing with intelligent systems that can fully eliminate artifacts rather than merely reducing their visibility.
Solution Approach 2:
The patent changes the parameters of image capture by dynamically adjusting camera settings based on detected artifact types. The system modifies shutter speed, aperture, ISO, and other capture parameters in real-time to prevent artifact formation at the source, rather than relying on post-processing to reduce visibility after artifacts are already present.
2Difficulty of detecting and measuring
If advanced semiconductor circuitry is used to detect artifacts and adjust capture settings, then artifact detection capability is improved, but the device cost increases significantly
Solution Approach 1:
The patent uses software-based machine learning models that run on standard camera processors to replicate the functionality of expensive advanced semiconductor circuitry. Instead of hardware-based artifact detection requiring superior DSLR cameras, the system copies the detection and remediation capabilities through software algorithms that can operate on conventional camera hardware.
Solution Approach 2:
The patent replaces expensive, complex hardware circuitry with more affordable software solutions. The machine learning models are implemented as software that can be deployed on standard camera devices, eliminating the need for costly specialized semiconductor circuitry while achieving comparable or superior artifact detection and remediation performance.
3Object-affected harmful factors
If photographers avoid certain objects or angles to mitigate artifacts, then artifact occurrence is reduced, but capture flexibility and creativity are limited
Solution Approach 1:
The patent performs preliminary artifact detection and remediation before the image is finalized. The system proactively identifies potential artifacts during capture and automatically applies corrections, allowing photographers to capture images of any subject or angle without concern for artifact formation, as the remediation occurs automatically in the processing pipeline.
Solution Approach 2:
The patent enables the camera system to automatically detect and remediate its own artifacts without requiring photographer intervention or avoidance behavior. The machine learning-based system performs self-diagnosis and self-correction, allowing photographers to freely compose and capture images while the system independently handles artifact mitigation, thus preserving creative flexibility.
Data Source
AI summary
A method includes receiving a digital representation of an image of a subject, applying an image artifact machine learning model to generate a classification of the image as containing an image artifact including an identification of a type of the image artifact, identifying an image remediation process based on the classification, and applying the identified image remediation process to remediate the image artifact.