Spatial Noise Reduction via Local Variance Estimation

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing image processing systems cannot effectively eliminate spatial noises without blurring the image, failing to preserve image details.

Innovation Solution

An image processing system comprising a local variance estimator, a noise detector, and a spatial de-noise filter that calculates local variance values, sets noise thresholds, and adjusts pixel weights based on flag distribution signals to distinguish between noise and image details, allowing for simultaneous noise reduction and detail preservation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Object-affected harmful factors

If a low-pass filter is used to remove spatial noises, then noise elimination is improved, but image details are blurred

Engineering Contradiction:

Improvespatial noiseVSAvoidimage detail clarity

Core Design Contradiction:

Object-affected harmful factorsVSManufacturing precision

Solution Approach 1:

The patent applies local quality by calculating local variance values for each pixel to distinguish between noise and image details. Pixels with local variance below the threshold are identified as noise and filtered, while pixels with local variance above the threshold (indicating image details or edges) are preserved. This localized differentiation allows selective noise removal without blurring image details.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The patent changes the parameter approach from using a fixed low-pass filter to dynamically calculating local variance values for each pixel. By computing local variance and comparing it against a threshold, the system adapts its filtering behavior pixel-by-pixel, removing noise where appropriate while preserving edges and details where the variance indicates significant image content.

Inventive Principle:

Principle #35Parameter changes

2Object-affected harmful factors

If signal processing is applied to eliminate spatial noises, then noise reduction is improved, but image quality deteriorates due to blurring

Engineering Contradiction:

Improvespatial noiseVSAvoidimage quality

Core Design Contradiction:

Object-affected harmful factorsVSReliability

Solution Approach 1:

The system applies local quality by computing local variance values for each pixel and using these values to make localized decisions about noise versus detail. This allows the image processing to adapt its behavior at each pixel location, preserving edges and details while removing noise, thereby maintaining overall image quality.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The patent replaces the conventional mechanical low-pass filter approach with a statistical method based on local variance calculation. Instead of uniformly filtering all frequencies, the system uses statistical properties (variance) of pixel neighborhoods to intelligently distinguish noise from meaningful image content, achieving superior results.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS8577167B2Image processing system and spatial noise reducing method

Publication Date: 2013.11.05 REALTEK SEMICON CORP

AI summary

The invention discloses an image processing system and method thereof. The image processing system includes an local variance estimator, a noise detector, a spatial de-noise filter. The local variance estimator estimates each pixel of an input image signal to separately output a local variance value of each pixel, and generates a noise threshold according to the local variance values. The noise detector determines which pixel indicates noise or image according to the noise threshold. The spatial de-noise filter filers the pixel indicating noise to generate an output image signal.