Learned Filter Dictionary for Bad Pixel Correction
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Solution Overview
Problem
Existing pixel correction methods in digital cameras often result in undesirable artifacts like broken lines and low resolution due to inefficient correction of bad pixels, which are not adequately addressed by conventional techniques.
Innovation Solution
A learning-based approach that utilizes a dictionary of filters learned offline through self-supervised algorithms, allowing cameras to efficiently correct bad pixels in real-time by encoding image patches and applying matching filters, independent of sensor type, thus reducing computational and memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional pixel correction methods are used, then bad pixels can be corrected, but undesirable artifacts like broken lines and low resolution occur
Solution Approach 1:
The patent pre-computes and stores correction filters for various patch types in a dictionary before actual image processing. During runtime, the system only needs to look up and apply the appropriate pre-computed filter based on patch similarity, avoiding complex real-time calculations and reducing artifacts from inadequate correction methods
Solution Approach 2:
The system creates multiple copies of correction filters organized in a dictionary structure, where each filter represents a corrected version of a specific patch type. Instead of computing corrections from scratch, the system copies and applies the most similar pre-computed filter to the current bad pixel patch, ensuring consistent and artifact-free correction
2Measurement precision
If learning-based methods with offline dictionary generation are used, then bad pixel correction accuracy improves, but computational and memory requirements increase
Solution Approach 1:
The patent segments the image into patches and further segments the correction approach by organizing filters into a dictionary based on patch similarity. This segmentation allows the system to process only relevant local regions with appropriate pre-computed filters, reducing both computational load and memory requirements compared to processing entire images with complex algorithms
Solution Approach 2:
The system changes the parameter of filter organization from unstructured to a dictionary-based structured format with keys representing patch descriptors. This parameter change enables efficient lookup and retrieval of appropriate filters, reducing memory access time and computational overhead while maintaining high correction precision
3Manufacturing precision
If complex correction algorithms are applied, then bad pixel correction quality improves, but processing speed decreases
Solution Approach 1:
The system performs complex filter computation in advance during an offline training phase and stores the results in a dictionary. During actual image processing, only simple lookup and filter application operations are needed, dramatically increasing processing speed while maintaining high correction quality through the pre-computed optimal filters
Solution Approach 2:
The patent extracts the computationally intensive filter computation step from the real-time image processing pipeline and moves it to an offline preprocessing stage. This extraction allows the main processing pipeline to operate at high speed using only lightweight operations, while still benefiting from the quality of complex algorithms through the pre-computed filters
Data Source
AI summary
Methods and apparatuses for correcting bad image pixels are described. The described sensor-independent image processing techniques leverage one or more dynamic dictionaries of learned filters for bad pixel correction (e.g., where a camera leverages such dictionaries to efficiently identify filters to accurately adjust and correct bad pixel values). For example, a dictionary may store filters that are learned offline (via a self-supervised learning algorithm implemented at a server using known images and ground truth bad pixel correction values). To select a filter for a bad pixel correction operation, a camera may encode an image patch surrounding a bad pixel (into an encoded patch descriptor) and search the dictionary for a matching patch descriptor key. The camera may then apply the filter (value) corresponding to the searched patch descriptor (key) of the dictionary to the image patch to correct the bad pixel and generate a corrected output image.