AI Image Correction for Natural Face and Detail Enhancement
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Solution Overview
Problem
Existing electronic devices struggle to enhance image quality uniformly across the entire image, often resulting in unnatural alterations to specific parts like faces or severe blurring of other details, and lack an efficient method for automatically recommending necessary corrections.
Innovation Solution
An electronic device equipped with a processor that uses artificial intelligence to identify and recommend correction functions, such as scaling, brightness adjustment, and sharpness enhancement, while highlighting improved parts through indicators, ensuring stable detail enhancement and noise reduction across the entire image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If an application accumulates and learns face data to redraw only the face in the photo, then image quality of the face is improved, but textures of the face change unnaturally and details in parts other than the face become severely blurred
Solution Approach 1:
The patent applies local quality by differentiating processing between face regions and non-face regions. The system identifies face areas and applies super-resolution processing specifically to these regions while preserving other areas, thereby improving face image quality without causing unnatural texture changes or blurring in non-face portions.
Solution Approach 2:
The patent segments the image into face regions and non-face regions through face detection. This segmentation allows the system to apply different processing strategies to different parts of the image, improving overall image quality while maintaining natural appearance in non-face areas.
2Manufacturing precision
If image correction is applied to enhance specific parts like faces, then detail enhancement is achieved in those parts, but other parts of the image become severely blurred
Solution Approach 1:
The patent implements local quality by applying detail enhancement selectively to identified face regions rather than uniformly across the entire image. This approach enhances details in face areas while preserving the original quality in non-face areas, avoiding the blurring effect that would result from uniform processing.
Solution Approach 2:
The system segments the image to identify face regions and applies correction functions specifically to these segmented areas. This segmentation-based approach ensures that detail enhancement is concentrated where needed while maintaining uniformity and avoiding blurring in other parts of the image.
3Manufacturing precision
If manual image correction is required to achieve desired quality improvements, then image quality can be improved, but user operation becomes complex and time-consuming
Solution Approach 1:
The patent implements self-service through automated face detection and automatic recommendation of appropriate correction functions. The system independently identifies face regions, analyzes image quality requirements, and suggests suitable processing parameters without requiring manual user intervention, thereby maintaining high image quality improvement while simplifying user operation.
Solution Approach 2:
The system provides feedback by automatically analyzing the input image, identifying face regions, and recommending specific correction functions with predicted outcomes. This feedback mechanism allows users to make informed decisions about image correction without needing to manually adjust multiple parameters, reducing operational complexity while achieving desired quality improvements.
Data Source
AI summary
An example image processing method may include determining a first image to be corrected; obtaining a second image by correcting the first image; and identifying at least one part, from among a plurality of parts in the second image, for recommending an effect caused by correction. The method may include displaying an indicator indicating at least one part in the second image and correction may be performed by automatically recommending and applying one or more correction functions for image enhancement to each image. The method may provide an original comparison display function or an enlarged display function for an enhanced image.