Image Processing Circuit Noise Suppression via Adaptive Variance Thresholding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional image processing technologies often degrade image quality by mistakenly removing details and edge features while suppressing noise, due to the influence of image brightness on gradient and variance calculations.
Innovation Solution
An image adjustment method that processes pixels in blocks, adjusts region variance based on grayscale values, and dynamically sets threshold values according to brightness to differentiate between noise and edge features, thereby optimizing noise suppression without losing details.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If conventional gradient or variance calculation methods are used for noise suppression, then noise can be removed, but image details and edge features are lost
Solution Approach 1:
The patent applies local quality by using block-based processing where each block's noise suppression is determined independently based on its local grayscale characteristics. The variance adjustment parameter is calculated specifically for each block according to its local brightness, allowing different regions to have different noise suppression strengths tailored to their local properties, thus preserving edges while removing noise.
Solution Approach 2:
The patent changes the parameter of variance calculation by introducing a grayscale-dependent adjustment mechanism. Instead of using fixed variance thresholds, the system dynamically adjusts the variance parameter based on the local grayscale value of each block, enabling adaptive noise suppression that distinguishes between noise and actual image features through parameter modification.
2Device complexity
If fixed threshold values are used for noise suppression, then processing is simple, but brightness variations cause incorrect noise detection
Solution Approach 1:
The patent applies dynamics by making the noise suppression threshold dynamic rather than fixed. The variance adjustment parameter changes dynamically based on the local grayscale value of each block, allowing the system to adapt to different brightness conditions automatically. This dynamic adjustment ensures accurate noise detection across varying brightness levels without requiring complex manual threshold tuning.
3Object-affected harmful factors
If strong noise suppression is applied to high brightness regions, then noise is effectively removed, but image quality deteriorates due to detail loss
Solution Approach 1:
The patent applies local quality by calculating the variance adjustment parameter specifically for each block based on its local grayscale characteristics. High brightness regions receive appropriate noise suppression strength tailored to their local properties, while edge regions maintain higher detail preservation. This localized approach ensures that noise suppression does not uniformly degrade image quality across different regions.
Solution Approach 2:
The patent modifies the variance parameter based on local grayscale values, changing the suppression strength adaptively. In high brightness regions, the adjusted variance allows for effective noise removal, while in edge regions, the parameter adjustment preserves detail integrity. This parameter adaptation maintains image quality reliability while achieving noise suppression.
Data Source
AI summary
An image adjustment method includes: sequentially processing a plurality of pixels in at least one frame, wherein a pixel under processing is a current pixel, the current pixel and multiple adjacent pixels form a current block, and each current block is performed with following operations: reading a grayscale value of each pixel in the current block; determining a region grayscale value and a region variance of the current block according to the grayscale values of the pixels; generating a variance adjustment parameter via a variance adjustment function, wherein the region grayscale value is a variable of the variance adjustment function; generating an adjusted region variance according to the variance adjustment parameter and the region variance; and comparing the adjusted region variance with a variance threshold to determine whether to perform a noise suppression operation on the current pixel.