Neural Network Bad Pixel Correction in Image Sensors
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Solution Overview
Problem
Image processing apparatuses face performance degradation due to bad pixels occurring in arbitrary shapes and positions, which are difficult to correct effectively, especially when they appear in isolation or clusters, affecting the quality of high-resolution images.
Innovation Solution
An image processing system utilizing a neural network processor performs cluster-level bad pixel correction based on coordinate information for bad pixel clusters and pixel-level correction for isolated bad pixels, followed by post-processing to generate high-quality images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a relatively large number of bad pixels are present in the pixel array, then the image processing apparatus cannot generate desired images, but increasing the number of pixels to achieve high-resolution images increases the likelihood of bad pixel occurrence
Solution Approach 1:
The patent segments the bad pixel correction process into two distinct levels: pixel-level correction for isolated bad pixels and cluster-level correction for groups of bad pixels. This segmentation allows each correction method to be optimized for its specific target, improving overall correction effectiveness while maintaining high-resolution image quality
Solution Approach 2:
The patent introduces a neural network processor as an intermediary component between the pixel array and the main processor. This neural network processor specifically handles bad pixel correction tasks, acting as a mediator that filters out bad pixel effects before the main image processing pipeline, thereby protecting the overall system from performance degradation
2Adaptability or versatility
If conventional bad pixel correction techniques are used, then isolated bad pixels can be corrected, but clusters of bad pixels cannot be effectively corrected
Solution Approach 1:
The patent implements a dynamic correction approach where the system adaptively selects between pixel-level and cluster-level correction methods based on the detected bad pixel pattern. The neural network processor dynamically identifies whether bad pixels are isolated or form clusters and applies the appropriate correction strategy, making the system versatile across different bad pixel scenarios
Solution Approach 2:
The patent applies different correction qualities to different regions of the image data. For isolated bad pixels, pixel-level correction is applied; for cluster-type bad pixels, cluster-level correction using neural networks is applied. This local differentiation of correction quality ensures optimal accuracy for each bad pixel pattern without wasting computational resources
3Reliability
If pixel-level correction is applied to all bad pixels, then isolated bad pixels can be corrected, but cluster-type bad pixels require excessive processing resources
Solution Approach 1:
The patent applies partial action by using pixel-level correction only where necessary (for isolated bad pixels) and reserving cluster-level neural network correction for specific cluster-type patterns. This selective application of correction methods reduces overall processing complexity while maintaining reliability for both bad pixel types
Data Source
AI summary
An image processing apparatus includes; an image sensor including pixels that generate first image data, and an image processing system. The image processing system includes; a neural network processor that performs a cluster-level bad pixel correction operation on the first image data based on first coordinate information associated with first-type bad pixel clusters to generate second image data, and a main processor that performs a post-processing operation on the second image data to generate third image data.