Micro Encrypted Code Decoding with AI Image Repair
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods struggle to accurately and efficiently decode very small encrypted codes, such as micro QR codes, due to limitations in sensor resolution, inadequate image processing, and environmental conditions, leading to blurred or pixelated images and inconsistent decoding results.
Innovation Solution
An electronic device equipped with high-resolution image sensors, multi-sensor fusion, and AI-driven processing techniques, including super-resolution, noise reduction, and edge sharpening, dynamically adjusts magnification and decoding parameters based on real-time feedback, and provides AR guidance for optimal image capture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard image sensors are used to capture encrypted codes, then device complexity is reduced, but measurement precision deteriorates due to insufficient resolution and sensitivity
Solution Approach 1:
The patent combines multiple sensor types (camera sensor, depth sensor, LiDAR) into a unified sensing system. The camera captures 2D images while the depth sensor or LiDAR provides 3D distance and size information. By merging these sensor data streams, the system achieves high measurement precision for code detection without requiring a single overly complex sensor, resolving the contradiction between precision and complexity.
Solution Approach 2:
The patent introduces an intermediary processing layer that receives data from multiple sensors and synthesizes it into accurate code measurements. The processor acts as an intermediary, combining image data with depth information to determine code size and distance, achieving high precision while keeping individual sensor components relatively simple.
2Ease of operation
If digital zoom is used to enlarge the encrypted code image, then ease of operation is improved, but measurement precision deteriorates due to pixelation and loss of clarity
Solution Approach 1:
The depth sensor acts as an intermediary that provides accurate distance information without requiring optical zoom. By using the depth data to calculate code size and position, the system achieves precise measurements while maintaining ease of operation, avoiding the pixelation problems of digital zoom.
Solution Approach 2:
The patent replaces the mechanical/optical zoom system with a computational approach using depth sensor data. Instead of physically enlarging the image through optics (which has limitations), the system uses depth information to virtually reconstruct code dimensions, maintaining clarity while improving ease of operation.
3Adaptability or versatility
If fixed decoding settings are used, then device complexity is reduced, but adaptability deteriorates when facing different code types or environmental conditions
Solution Approach 1:
The patent implements dynamic decoding settings that automatically adjust based on detected code characteristics and environmental conditions. The system analyzes code size, distance, lighting, and angle to dynamically select appropriate decoding parameters, achieving high adaptability without requiring manual configuration complexity.
Solution Approach 2:
The system performs self-configuration by automatically analyzing the captured code and environmental conditions to determine optimal decoding settings. This self-service approach provides adaptability to different code types and conditions while keeping the user interface simple, resolving the contradiction between versatility and complexity.
4Measurement precision
If manual adjustment of position, lighting, and zoom is required, then measurement precision can be optimized, but productivity deteriorates due to time-consuming operations
Solution Approach 1:
The system automatically performs positioning, lighting adjustment, and zoom optimization without user intervention. The processor analyzes the captured image and depth data to automatically adjust focus, exposure, and magnification settings, achieving high measurement precision while maintaining fast decoding speed, thus resolving the contradiction between accuracy and productivity.
Solution Approach 2:
The patent implements real-time feedback loops where the system continuously monitors code detection quality and automatically adjusts capture parameters. Based on feedback from initial code detection attempts, the system refines positioning and imaging parameters automatically, achieving high precision without manual adjustment delays.
Data Source
AI summary
The present disclosure relates to an electronic device for decoding encrypted codes, particularly micro QR codes, using advanced imaging, AI neural networks, and processing techniques. The device includes a high-performance processor, high-resolution image sensors, a LIDAR sensor, and a magnification unit with digital and optical zoom capabilities. The device captures images of encrypted codes, determines their size, and performs iterative magnification and enhancement to improve legibility. The decoding process is powered by a pre-stored database and dynamically adjusts settings based on real-time feedback and AI-driven analysis. The AI neural network is trained to repair incomplete or partially captured codes, enhancing the decoding accuracy even under suboptimal conditions. The device includes an AR unit for visual guidance to help the user position the device correctly. A notification unit provides visual and auditory cues. This combination of hardware, software, and AI components ensures efficient and accurate decoding of very small codes.