Image Sensor Neural Network Correction for Cluster Bad Pixels
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Solution Overview
Problem
Existing image sensors face challenges in correcting cluster bad pixels due to process issues, which are frequent at fixed positions, and artificial intelligence-based solutions struggle with real-time processing due to excessive arithmetic operations.
Innovation Solution
An image sensor equipped with a pixel array, readout circuit, and bad pixel correction circuit that uses a neural network to correct cluster bad pixels by flipping input data based on deep learning, reducing the number of networks and arithmetic operations through a weight-lightened neural network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If artificial intelligence (deep learning) technology is used to correct cluster bad pixels, then correction accuracy is improved, but processing speed deteriorates due to excessive arithmetic operations
Solution Approach 1:
The patent segments the pixel array into multiple blocks and processes each block independently using separate neural networks. This segmentation allows parallel processing of different regions, reducing the total arithmetic operations required while maintaining correction accuracy for each local area.
Solution Approach 2:
The patent performs preliminary detection of bad pixels and their patterns before applying correction. By pre-identifying cluster bad pixel locations and characteristics, the system can apply targeted corrections only where needed, avoiding unnecessary arithmetic operations on good pixels and improving overall processing speed.
2Manufacturing precision
If the number of sensing pixels is increased to improve resolution, then image quality is improved, but the frequency of cluster bad pixels worsens due to process issues at miniaturized scales
Solution Approach 1:
The patent merges multiple detection results from different neural networks processing different blocks to generate a comprehensive correction map. This merging approach allows the system to handle cluster bad pixels that span multiple blocks, maintaining high reliability despite increased pixel density and process variability.
Solution Approach 2:
The patent creates correction patterns based on detected bad pixel clusters and applies these corrected patterns across the image. By copying and adapting correction patterns from detected cluster locations, the system efficiently handles multiple defects without requiring separate processing for each individual bad pixel, maintaining reliability at high resolutions.
Data Source
AI summary
An image sensor includes: a pixel array including a plurality of sensing pixels each configured to convert a received light signal into an electrical signal; a readout circuit configured to convert the electrical signals into image data and output the image data; and a bad pixel correction circuit configured to: input, to a neural network, first input data comprising a first cluster bad pixel of the image data in a first direction to generate a first corrected pixel data, and flip second input data comprising a second cluster bad pixel of the image data in a second direction to generate third input data, and input, to the neural network, the third input data to generate a second corrected pixel data.