AI Image Generation With Face Correction and Quality Grading
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Solution Overview
Problem
The existing process of generating digital banners is cumbersome and limiting due to the manual selection of images from a finite pool, which restricts creativity and is time-consuming and costly.
Innovation Solution
An AI-driven system for generating digital images, automatically correcting deformities, ranking images, and integrating them into banners, using a modular approach that allows for customizable, royalty-free image creation without the need for purchasing images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual image selection from a finite pool is used, then image quality control is possible, but creativity is restricted and the process is time-consuming
Solution Approach 1:
The system performs automatic image generation, quality assessment, and banner assembly without human intervention. The AI model generates images from text prompts, automatically evaluates them using multiple metrics, and integrates selected images into banners, eliminating the need for manual searching and selection while maintaining quality control through automated grading mechanisms.
Solution Approach 2:
The manual mechanical process of searching, selecting, and assembling images is replaced with an automated AI-driven system. The machine learning model substitutes human creativity and judgment, generating images algorithmically and using automated quality assessment tools to evaluate and select the best images for banner integration.
2Reliability
If images are purchased from existing pools, then usage rights are secured, but costs and usage limits are imposed
Solution Approach 1:
Instead of purchasing and licensing existing images, the system generates original images through AI that can be used without restriction. The generated images serve as unique copies created specifically for each banner, eliminating the need to purchase licenses while providing unlimited usage rights for the generated content.
Solution Approach 2:
The system changes the fundamental parameter of image acquisition from purchasing pre-existing images to generating new images algorithmically. This parameter change transforms the cost structure from paid licensing to free generation, while providing unlimited usage rights for the AI-generated content.
3Productivity
If automated image generation is implemented, then creativity and speed are improved, but image quality control becomes more challenging
Solution Approach 1:
The system implements automated feedback loops where generated images are evaluated using multiple quality metrics including aesthetic assessment, relevance to the prompt, and technical quality measures. This feedback mechanism allows the system to automatically identify and select high-quality images while rejecting poor generations, maintaining quality control despite high-speed automated generation.
Solution Approach 2:
The AI system performs multiple functions simultaneously: it generates images from text prompts, evaluates their quality using multiple metrics, selects the best images, and integrates them into banners. This multi-functional approach ensures both high productivity and quality control by combining generation and assessment capabilities in a single integrated system.
Data Source
AI summary
A computer-implemented method is provided for generating a digital image. The method includes inputting a prompt that provides a textual description of the image into a trained machine learning model to generate the image based on the prompt, and processing the image. Processing the image includes determining presence of one or more humans in the image by detecting one or more human faces using a face detection algorithm, if at least one human face is detected, which is indicative of at least one human present in the image, determining whether there is at least one anatomical deformity associated with the at least one human, and if at least one anatomical deformity is detected, performing correction of the at least one anatomical deformity. The method further includes grading, by the computing device, the image to generate a final score to evaluate image quality.