Single-Pass Image Noise Filtering and Defect Correction
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Solution Overview
Problem
Existing image processing methods are inefficient in correcting defective pixels and filtering Gaussian noise in digital images, particularly under low light conditions, as they often require multiple scans and can generate artifacts or delete image details.
Innovation Solution
A method that identifies defective pixels by calculating the absolute differences between neighboring pixels and applies a threshold to determine if a pixel is defective, allowing for single-pass correction and filtering without generating artifacts, using a combination of median intensity and weighted sums to correct pixel values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple scans are used to correct defective pixels and filter Gaussian noise, then the correction effectiveness is improved, but the processing time and complexity increase
Solution Approach 1:
The patent merges defective pixel correction and Gaussian noise filtering into a single integrated processing pass. The filter simultaneously identifies defective pixels using statistical methods and applies appropriate filtering algorithms, eliminating the need for separate correction and filtering passes while maintaining correction effectiveness.
Solution Approach 2:
The patent performs preliminary identification and classification of defective pixels during the same pass as noise filtering. By pre-identifying defective pixels and their types (dead, spiked, or random noise) before applying filtering, the system prepares correction data in advance, enabling effective correction without requiring additional processing passes.
2Reliability
If aggressive filtering is applied to remove defective pixels, then the noise reduction is improved, but image details and edges may be deleted
Solution Approach 1:
The patent applies different filtering strategies based on local pixel characteristics. For each pixel, the system evaluates neighborhood statistics and applies appropriate filtering only where needed, preserving edges and fine details in regions with legitimate high-frequency content while removing noise in homogeneous regions. The filter adapts its behavior to local image content rather than applying uniform aggressive filtering.
Solution Approach 2:
The patent uses dynamic thresholding and adaptive filtering parameters that adjust based on local image statistics. The filtering strength and methodology change dynamically according to the local variance, edge presence, and pixel type identification, allowing aggressive filtering where safe and conservative filtering where details must be preserved.
3Measurement precision
If separate processing modes are used for defective pixel correction and noise filtering, then the correction precision is improved, but the device complexity increases
Solution Approach 1:
The patent implements a universal filtering system that performs multiple functions within a single processing framework. The same filter structure handles defective pixel identification, classification, correction, and Gaussian noise filtering simultaneously, eliminating the need for separate processing modes or dedicated hardware for each function while maintaining high precision through integrated statistical analysis.
Data Source
AI summary
A method for correcting an image from defects and filtering from Gaussian noise corrects each pixel of the image when it is considered defective and filters it from Gaussian noise in one-pass. The one-pass improves the speed for performing the correcting and filtering. The drawbacks associated with choosing incompatible defect correction and filtering operations are overcome.