Bad Pixel Mapping for Multi-Condition Image Frame Correction
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Solution Overview
Problem
Mobile electronic devices' cameras suffer from unpredictable defective pixels, leading to noticeable artifacts in images, especially in dark or bright portions, resulting in poor user experiences.
Innovation Solution
Obtain multiple input image frames captured under different conditions, separate color channels, generate bad pixel maps by identifying outliers based on intensity distributions, combine and refine these maps using morphological operations, and update pixel values to correct defective pixels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple input image frames are processed with adaptive window sizes to identify bad pixels, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent implements dynamic window sizes that adapt based on capture conditions (exposure time, ISO) and local image content characteristics. Instead of using a fixed window size, the system adjusts the operation window size dynamically to match the statistical properties of different imaging scenarios, thereby improving bad pixel detection accuracy without requiring manual configuration for each condition
Solution Approach 2:
The system changes processing parameters (window size, threshold values) based on input conditions such as exposure time, ISO sensitivity, and local image statistics. By modifying these parameters adaptively, the system optimizes detection precision for varying capture conditions while maintaining a unified processing framework that doesn't significantly increase device complexity
2Manufacturing precision
If morphological operations are applied to refine bad pixel maps, then manufacturing precision improves, but loss of time increases
Solution Approach 1:
The patent applies morphological operations selectively and partially to refine bad pixel maps. Instead of exhaustive processing, the system performs targeted morphological operations only where needed to eliminate false positives and fill gaps in bad pixel detection, achieving sufficient precision without unnecessary computational overhead that would significantly increase processing time
Data Source
AI summary
A method includes obtaining input image frames, including at least two captured using different capture conditions, separating color channels of each image frame, and generating at least one bad pixel map for each color channel of each image frame. Each bad pixel map is generated by identifying one or more outliers in pixel values in the color channel based on an intensity distribution of pixel values in an operation window within the color channel. The operation window has a window size based on the capture condition and/or local image content in the corresponding image frame. The method also includes combining the bad pixel maps and performing a morphological operation to refine the combined bad pixel map and using one or more coordinates of one or more bad pixels in the refined bad pixel map to update one or more pixel values of at least one image frame.