Blur Value Detection for Automatic Image Focus Adjustment
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Solution Overview
Problem
Conventional image capture systems struggle to ensure all objects in a captured image are in focus, requiring manual adjustments and multiple shots, which can be time-consuming and frustrating for users.
Innovation Solution
An image processing device that determines the blur value of objects in a captured image, automatically adjusts the focus of the image capture device, and recaptures the image to improve the focus of blurry objects, generating a well-focused output image with all objects sharp.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual focus adjustment is used to ensure all objects are in focus, then image quality is improved, but time consumption and operation complexity increase
Solution Approach 1:
The system automatically detects blurry objects and adjusts focus without user intervention. The processor identifies objects with blur values below the threshold and autonomously recalibrates camera parameters to refocus those objects, eliminating the need for manual focus adjustment by the user.
Solution Approach 2:
The system calculates blur values for detected objects and uses this feedback to determine whether focus adjustment is needed. If any object's blur value is below the threshold, the system adjusts focus and recaptures the image, creating a closed-loop feedback system that automatically ensures all objects are in focus.
2Manufacturing precision
If manual focus adjustment is used to ensure all objects are in focus, then image quality is improved, but ease of operation deteriorates
Solution Approach 1:
The system performs automatic focus adjustment without requiring user expertise or manual intervention. The processor autonomously identifies blurry objects, calculates their blur values, and adjusts camera parameters to refocus them, making the system self-sufficient and eliminating the need for user knowledge of focus calibration.
Solution Approach 2:
The system replaces manual mechanical focus adjustment with automated electronic control. Instead of requiring users to physically adjust focus rings or buttons, the processor electronically controls camera parameters based on calculated blur values, substituting automated computation and control for manual mechanical operation.
3Manufacturing precision
If multiple shots are taken to ensure at least one image has all objects in focus, then image quality is improved, but productivity decreases
Solution Approach 1:
The system performs preliminary detection of object blur values before finalizing the image capture. By calculating blur values for all detected objects and comparing them against the threshold, the system determines in advance whether the current focus settings are adequate, preventing unnecessary multiple shots.
Solution Approach 2:
The system uses blur value calculations as feedback to determine whether to recapture the image. If any object's blur value is below the threshold, the system automatically adjusts focus and recaptures; if all objects meet the threshold, the image is finalized. This feedback mechanism eliminates redundant multiple shots.
4Ease of operation
If automatic blur detection and focus adjustment is implemented, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The processor performs multiple functions: detecting objects, calculating blur values, determining focus adequacy, adjusting camera parameters, and controlling image recapture. By consolidating these functions into a single processing unit, the system achieves automatic focus adjustment without proportionally increasing overall device complexity.
Solution Approach 2:
The system combines object detection, blur analysis, and focus control into an integrated automated process. The processor merges multiple operations—detecting objects, calculating blur values, comparing against thresholds, and adjusting parameters—into a unified workflow that simplifies the user interface while managing internal complexity through functional integration.
Data Source
AI summary
An image processing device is disclosed. The image processing device controls an image capture device to capture at least first image that comprises one or more objects. The image processing device identifies at least a first object in the first image. The image processing device determines a first blur value of identified first object and determines the identified first object as a blur object based on the first blur value. The image processing device adjusts a focal point of the image capture device to focus on the blurred first object. The image processing device controls the image capture device to capture a second image of the first object based on adjusted focal point. The image processing device replaces the first object in first image with the first object in the second image to generate an output image and further controls the display screen to display well-focused output image.