Surveillance Image Focus State Analysis via Wavelet Transform

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Solution Overview

Problem

Conventional surveillance cameras struggle to accurately and efficiently determine the focus state of images captured in low illumination environments, due to the large amount of spatial domain information required and the impact of noise on frequency domain analysis.

Innovation Solution

The proposed image analysis method utilizes wavelet transforms to decompose surveillance images into low frequency and high frequency components, which are then processed using standardization, discrete cosine transform, and feature extraction processes. This allows for the extraction of frequency domain features and spatial domain features, which are integrated to accurately classify the focus state of the image.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If spatial domain information analysis is used to determine focus state, then measurement precision is improved, but device complexity and computation time increase significantly

Engineering Contradiction:

Improvefocus state determination accuracyVSAvoidmemory capacity and computation complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent extracts only the essential frequency domain features (low frequency and high frequency components) from the complete spatial domain image data, rather than analyzing all spatial information. This extraction approach reduces memory requirements and computational complexity while maintaining focus state determination accuracy.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent replaces direct spatial domain analysis with frequency domain analysis using wavelet transform. This substitution allows focus state determination without processing the entire spatial domain dataset, thereby reducing computational burden and memory usage while preserving measurement precision.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Loss of time

If frequency domain analysis is used to determine focus state, then computation time is reduced, but measurement precision deteriorates in low illumination environments due to noise

Engineering Contradiction:

Improvecomputation timeVSAvoidfocus state determination accuracy

Core Design Contradiction:

Loss of timeVSMeasurement precision

Solution Approach 1:

The patent segments the frequency domain analysis into distinct low frequency and high frequency components using wavelet transform. By processing these segments separately and integrating their features, the method achieves both computational efficiency and robustness against noise in low illumination conditions, as each frequency band contributes complementary information for focus state determination.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent creates a composite feature representation by integrating low frequency features and high frequency features. This composite approach combines the advantages of both frequency bands: low frequency provides structural information robust to noise, while high frequency provides edge and detail information, together achieving accurate focus state determination in low illumination environments.

Inventive Principle:

Principle #40Composite materials

3Loss of information

If high frequency data is used for focus analysis in low illumination, then spatial detail information is improved, but noise interference increases making determination difficult

Engineering Contradiction:

Improvespatial detail informationVSAvoidnoise interference

Core Design Contradiction:

Loss of informationVSObject-affected harmful factors

Solution Approach 1:

The patent merges low frequency features and high frequency features into an integrated feature set for focus state determination. This combination allows the system to utilize spatial detail information from high frequency data while compensating for noise interference through the complementary low frequency information, achieving robust focus analysis in low illumination conditions.

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS20250173995A1Image analysis method and image analysis apparatus

Publication Date: 2025.05.29 VIVOTEK INC

AI summary

An image analysis method is applied to an image analysis apparatus with an operation processor and includes receiving a surveillance image acquired by an image receiver, transforming the surveillance image into a first low frequency image and a plurality of first high frequency images via wavelet transform, transforming the first low frequency image into a second low frequency image and a plurality of second high frequency images via another wavelet transform, applying down sampling process to first high frequency group data generated by the first high frequency images, and applying depth integration to the first high frequency group data after the down sampling process and second high frequency group data generated by the second high frequency images and low frequency group data generated by the second low frequency image for acquiring concatenation data.