Image Processing Noise Reduction via Local Window Substitution
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Solution Overview
Problem
Conventional noise removal methods are ineffective for low-frequency noise, such as speckle noise, and often result in adverse effects like resolution drop and false colors, especially in digital cameras with large pixel counts, where low-frequency noise is prominent and difficult to address.
Innovation Solution
An image processing method that involves obtaining a reduced image, extracting pixels within a window around the input pixel, generating substitute data based on these pixels, calculating differences, and substituting the input pixel value when the difference is below a threshold, effectively reducing low-frequency noise while maintaining image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If conventional noise removal methods (median filter, low-pass filter) are used, then noise components are removed, but resolution drops and false colors occur
Solution Approach 1:
The patent applies different processing strategies to different regions of the image based on local characteristics. It calculates the standard deviation of pixel values in a window around each pixel to determine whether the region is uniform or contains edges/textures, and applies substitution only in uniform regions where noise removal is safe, preserving resolution in detailed regions
Solution Approach 2:
The patent changes the parameter being filtered by substituting pixel values with averaged values from neighboring pixels only when the local variance is below a threshold. This parameter-based approach (using standard deviation as a criterion) allows selective noise removal that preserves image resolution while removing noise in uniform regions
2Object-affected harmful factors
If conventional noise removal methods are used, then noise components are removed, but false colors are generated
Solution Approach 1:
The patent analyzes local image characteristics by calculating standard deviation in a window around each pixel. In uniform regions (low standard deviation), it safely applies pixel value substitution without causing false colors. In regions with edges or textures (high standard deviation), it avoids substitution, preventing false color generation while maintaining noise removal effectiveness
3Productivity
If large memory is allocated for noise removal processing, then processing capability is improved, but device complexity increases
Solution Approach 1:
The patent segments the image processing into independent pixel-by-pixel operations with a local window approach. Each pixel is processed independently by examining only its local neighborhood, avoiding the need to load or process the entire image in memory. This segmentation enables efficient noise removal with minimal memory requirements
Solution Approach 2:
The patent performs partial processing by applying noise removal only to pixels in uniform regions (where standard deviation is below threshold) rather than processing the entire image uniformly. This selective approach reduces computational overhead and memory usage while maintaining effective noise removal where needed
Data Source
AI summary
Pixels in a window, which corresponds to the position of an input pixel and has a predetermined size, are extracted from a reduced image obtained by reducing an input image to a predetermined scale. Substitute data used to substitute the value of the input pixel is generated based on the extracted pixels in the window. The difference value between the substitute data and input pixel value is calculated and is compared with a first threshold value. When the difference value is smaller than the first threshold value, the input pixel value is substituted by the substitute data. Thus, low-frequency noise reduction can be attained using a minimum required memory size while suppressing adverse effects such as a resolution drop and the like.