Edge Parameter Computing for Bayer Pattern Image Noise Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image processing systems do not effectively address image noise reduction without consuming significant system resources and time, particularly in images captured using Bayer patterns.
Innovation Solution
An edge parameter computing method that calculates a ratio of maximum grey level difference to average grey level to determine edge intensity, allowing for selective weighting processes to reduce noise in images by identifying and adjusting pixel values based on edge types (strong, weak, or non-edges) within Bayer pattern images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If traditional noise processing methods are applied to Bayer pattern images, then image noise is reduced, but system resources and processing time are consumed excessively
Solution Approach 1:
The patent applies different noise reduction strategies to different regions of the image based on edge detection. Strong edges are processed with one weighting approach while weak edges use another, allowing selective noise reduction that preserves processing efficiency while addressing noise in critical regions
Solution Approach 2:
The image is segmented into regions based on edge strength classification. By dividing the image into strong edge regions, weak edge regions, and non-edge regions, the patent can apply appropriate noise reduction techniques to each segment, avoiding unnecessary processing across the entire image
2Object-affected harmful factors
If edge parameter computing is performed to enable selective weighting, then noise reduction effectiveness is improved, but computing complexity increases
Solution Approach 1:
The edge parameter computation is performed as a preliminary step before the main noise reduction processing. By pre-computing edge strength values and classifying regions in advance, the patent enables efficient selective weighting during the actual noise reduction phase without increasing overall computing complexity
Solution Approach 2:
The patent uses edge strength as a parameter to change the weighting values applied during noise reduction. By adjusting weighting parameters based on edge classification (strong, weak, or non-edges), the system achieves effective noise reduction with manageable computational complexity
Data Source
AI summary
An edge parameter computing method for an image, wherein the image includes a plurality of pixels forming a Bayer pattern. The edge parameter computing method comprises: (a) computing an average grey level of at least one specific type pixels in a specific region of the image; (b) computing each grey level difference value between the average grey level and the specific type pixels in the specific region to generate a plurality of grey level difference values; (c) finding a specific pixel with a maximum grey level difference value according to the grey level difference values; and (d) computing a ratio value between the average grey level and the maximum grey level difference value as the edge parameter.