Eye Region Artifact Correction via Layered Image Processing
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Solution Overview
Problem
Conventional image editing systems are inefficient and inflexible in correcting eye region artifacts in digital images, often producing unnatural results due to inaccurate user interactions and requiring significant computing resources, especially when used on handheld devices.
Innovation Solution
An eye region correction system that utilizes facial landmark detection algorithms and color matching processes to automatically identify and correct dark eye regions, wrinkles, and eye bags by separating digital images into layers and applying smoothing algorithms tailored to individual characteristics, allowing for efficient and accurate corrections on various devices without user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems rely on voluminous user inputs via graphical user interfaces to correct eye region artifacts, then users can perform retouching operations, but the systems produce inaccurate results that appear unnatural and artificial
Solution Approach 1:
The system performs automatic detection and correction of eye region artifacts without requiring user interaction. The algorithm independently identifies dark regions, wrinkles, and eye bags, then applies appropriate corrections, making the system self-sufficient and eliminating the need for manual user inputs while maintaining high accuracy
Solution Approach 2:
The patent replaces the mechanical interaction system (user clicking and manipulating graphical interface tools) with an automated algorithmic system. The correction is achieved through computer vision algorithms that detect and correct artifacts automatically, substituting manual mechanical operations with intelligent automated processing
2Productivity
If conventional systems require significant user interactions and time to correct dark eyes or wrinkles, then users can attempt retouching, but substantial computing resources are wasted in detecting, displaying, and correcting artifacts
Solution Approach 1:
The system performs preliminary detection and classification of eye region artifacts before correction. By pre-identifying the types and locations of artifacts (dark regions, wrinkles, eye bags) and pre-planning the correction strategy, the system avoids repeated detection and correction cycles, thereby improving productivity and reducing computing resource waste
Solution Approach 2:
The patent segments the correction process into distinct stages: detection of eye region, classification of artifact types, selection of correction parameters, and execution of correction. This segmentation allows each stage to be optimized independently, improving overall efficiency and reducing redundant computing operations
3Adaptability or versatility
If conventional systems require voluminous user interactions and selections, then correction can be performed on traditional desktop computers, but it becomes difficult or impossible to perform retouching operations using hand-held devices
Solution Approach 1:
The automated correction system requires no user input, making it equally suitable for hand-held devices with limited screen space and input capabilities. The system independently performs all detection and correction operations, adapting seamlessly to different device types without requiring traditional graphical interface interactions
Solution Approach 2:
The patent creates a universal correction system that functions across multiple device types (desktop computers, tablets, smartphones). By eliminating device-specific interface requirements and using standardized image processing algorithms, the system achieves multi-functionality and can be deployed on any device with sufficient processing capability
Data Source
AI summary
Methods, systems, and non-transitory computer readable media are disclosed for automatically, accurately, and efficiently correcting eye region artifacts including dark eye regions and wrinkles within a digital image portraying a human face. In particular, in one or more embodiments the disclosed systems localize areas within a digital image to identify eye region artifacts including dark eye regions and wrinkles. In one or more embodiments, the disclosed systems generate a corrected color image by correcting dark eye regions in a low frequency layer of the digital image by replacing the dark eye regions with candidate eye regions. Furthermore, in one or more embodiments the disclosed systems generate a corrected texture image by correcting wrinkles in a high frequency layer by processing the digital image utilizing a smoothing algorithm. The disclosed systems further generate a corrected digital image by combining the corrected color image and the corrected texture image.