Adaptive Noise Reduction for CCD Image Edge Preservation
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Solution Overview
Problem
Existing image capturing systems face challenges in effectively reducing noise in image signals, particularly due to fixed pattern and random noise components, which affect the quality of captured images.
Innovation Solution
An image capturing system that includes local region extraction, adaptive noise reduction processing, noise estimation, and subsequent noise reduction processing to optimize noise reduction based on the target pixel and its neighboring pixels, using a combination of noise reduction units and noise estimation units to calculate and apply weighting factors for effective noise suppression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If noise reduction processing is applied to image signals, then noise components are suppressed, but edge details may be degraded
Solution Approach 1:
The patent applies different processing strategies to different regions of the image. Edge regions are identified and processed differently from non-edge regions, preserving edge sharpness while reducing noise in smooth areas. This is achieved through edge detection and adaptive filtering that adjusts its strength based on local image characteristics.
Solution Approach 2:
The noise reduction processing is made adaptive and dynamic rather than uniform. The filtering strength and parameters are adjusted based on local image properties such as edge presence, noise level estimation, and signal strength. This allows the system to optimize noise reduction while preserving important image features in different regions.
2Object-affected harmful factors
If adaptive noise reduction processing is performed on target pixels, then noise is reduced, but processing complexity increases
Solution Approach 1:
The image processing is divided into distinct stages: edge detection, noise level estimation, adaptive parameter selection, and filtering. Each stage processes specific aspects independently, making the overall complex system manageable and efficient. Local regions are segmented and processed with appropriate parameters based on their characteristics.
Solution Approach 2:
The system performs preliminary actions such as edge detection and noise level estimation before applying the main noise reduction filtering. This allows the filtering parameters to be pre-determined based on local image properties, making the actual filtering operation more efficient and adaptive without requiring complex real-time adjustments during processing.
3Measurement precision
If noise amount estimation is performed based on target pixel values, then noise reduction accuracy is improved, but processing time increases
Solution Approach 1:
The system performs noise amount estimation selectively rather than uniformly across all pixels. Full estimation is performed on target pixels and their neighbors, while other areas use simplified approaches or inherit parameters from adjacent regions. This partial processing approach maintains accuracy where needed while reducing overall processing time.
Data Source
AI summary
An image capturing system includes: a first noise reduction unit which roughly removes the effects of an edge component by performing edge-preserving adaptive noise reduction processing on a target pixel within a local region including the target pixel and the neighboring pixels extracted from an image signal acquired from a CCD; a noise estimation unit which dynamically estimates the noise amount with respect to the target pixel based upon the target pixel value thus subjected to the noise reduction processing by the first noise reduction unit; and a second noise reduction unit which performs noise reduction processing on the target pixel based upon the target pixel value thus subjected to the noise reduction processing by the first noise reduction unit and the noise amount thus estimated by the noise estimation unit.