Adaptive Pixel Replacement for Image Noise Reduction

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Solution Overview

Problem

Existing image processing systems face challenges with poor quality images, particularly when objects are moving, leading to blurry images and inappropriate brightness levels, necessitating improved noise reduction and pixel replacement techniques.

Innovation Solution

The system employs methods for noise reduction by using frame averaging and motion detection to generate adaptive pixel replacement masks, combining techniques like left good pixel, right good pixel, local average, spiral search, upper replacement, or lower replacement to optimize pixel values based on the location and context of bad pixels.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If frame averaging is applied to reduce noise in static image portions, then signal-to-noise ratio is improved, but motion areas become blurred

Engineering Contradiction:

Improvesignal-to-noise ratioVSAvoidmotion rendering quality

Core Design Contradiction:

Measurement precisionVSShape

Solution Approach 1:

The image is divided into static portions and motion areas through motion detection. Frame averaging is applied selectively only to static portions while motion areas are excluded from averaging, thus improving signal-to-noise ratio in static regions without blurring moving objects.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Different processing strategies are applied to different regions of the image based on motion detection results. Static portions receive noise reduction through frame averaging, while motion areas maintain their original quality without averaging, achieving local optimization of both noise reduction and motion preservation.

Inventive Principle:

Principle #3Local quality

2Measurement precision

If multiple pixel replacement techniques are applied to bad pixels, then image quality is improved, but processing complexity increases

Engineering Contradiction:

Improveimage qualityVSAvoidprocessing complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system dynamically selects and applies different pixel replacement techniques based on the characteristics of bad pixels and their surrounding context. By changing the processing parameter (replacement technique) according to local image characteristics, optimal image quality is achieved without uniformly applying all techniques, thus managing complexity.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The pixel replacement system automatically identifies bad pixels and selects appropriate replacement techniques without manual intervention. The algorithm self-adapts to different scenarios by evaluating local image characteristics and applying the most suitable replacement method, reducing the need for complex manual configuration.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS9881358B2Method and system for adaptive pixel replacement

Publication Date: 2018.01.30 DRS NETWORK & IMAGING SYSTEMS LLC

AI summary

A system and method of image processing is provided, including implementing adaptive pixel replacement techniques or reducing noise. The method includes obtaining a data map of an image frame, wherein the data map comprises good pixels and bad pixels at locations associated with the data map. The method also includes assigning different techniques to the bad pixels, wherein a first technique is assigned to a first bad pixel and a second technique is assigned to a second bad pixel. The method further includes adjusting information associated with the bad pixels for a chosen technique for each of the bad pixels.