Multi-layer Image Processing for Edge Preservation in Low-Brightness Noise
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Solution Overview
Problem
Existing image processing technologies fail to effectively remove low-frequency noise while preserving edges in low-brightness regions, leading to either noise retention or edge degradation.
Innovation Solution
An image processing method that calculates pixel statistical values and edge information across multiple layers with progressively decreasing ranges, corrects edge information based on wider area statistics, and iteratively refines pixel values using post-correction techniques to maintain edge integrity and suppress noise across varying frequency ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If moving average processing is applied to reduce noise, then noise removal is improved, but edge preservation in low-brightness regions deteriorates
Solution Approach 1:
The patent applies different processing strategies to different regions of the image based on local characteristics. It calculates edge information and brightness values for each pixel, then selectively applies moving average processing only to regions where noise is detected and edges are preserved. The processing strength is adjusted locally based on edge strength and brightness conditions, allowing effective noise removal in flat regions while preserving edges in low-brightness regions.
Solution Approach 2:
The patent dynamically adjusts the processing parameters based on real-time analysis of edge information and brightness values. The moving average processing is not applied uniformly but is dynamically controlled based on the local edge strength and brightness conditions. When edges are detected or brightness is low, the processing is adjusted to preserve these features while still removing noise.
2Reliability
If smoothing filter size is increased to remove low frequency noise, then noise rejection is improved, but edge blurring increases
Solution Approach 1:
The patent calculates edge information and applies different processing strengths to different regions. In regions with strong edges, the processing preserves edge sharpness even when removing low frequency noise. In regions without edges, the processing applies stronger smoothing to remove noise. This local adaptation allows effective low frequency noise rejection without uniform edge blurring.
Solution Approach 2:
The patent changes the processing parameters dynamically based on local conditions. The moving average processing strength, filter size, and correction coefficients are adjusted according to the detected edge strength and brightness values. This parameter adaptation enables the system to remove low frequency noise effectively in flat regions while maintaining edge sharpness in regions with edges.
Data Source
AI summary
A method includes: calculating a pixel statistical value and edge of pixels for each of areas of a multi-layer, the areas each containing a target pixel and having a successively decreased range; correcting the edge based on a pixel statistical value of an area that is wider than an area of a specific layer; correcting difference between a pixel statistical value of the specific layer and the pixel statistical value of a layer that is wider than the specific layer using the post-correction edge; correcting the pixel statistical value of the specific layer using post-correction difference and the pixel statistical value of the layer that is wider than the specific layer; and correcting the target pixel by repeating correction of the pixel statistical value successively in each layer until the area reduces its range from the maximum range to the minimum range.