Intelligent Vehicular Image Dehazing via Haze Type Classification

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Solution Overview

Problem

Existing image dehazing methods and datasets lack diversity in haze types and intensity levels, limiting the development of robust dehazing techniques and failing to provide effective haze type classification.

Innovation Solution

The proposed system generates a comprehensive hazy dataset and uses advanced deep learning algorithms for intelligent haze type classification and specialized dehazing. It employs a hybrid conditional classifier and allows for flexible adjustment of pre-defined models, intensity levels, and haze types based on user requirements.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If existing image dehazing methods are used, then dehazing can be performed, but the methods lack diversity in haze types and intensity levels, limiting robustness

Engineering Contradiction:

Improverobustness of dehazing techniqueVSAvoiddiversity in haze types and intensity levels

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The patent segments the dehazing problem by creating specialized dehazers for different haze types (fog, smoke, cloud, rain) and intensity levels. Each haze type has its own dedicated processing model trained on specific datasets, allowing each segment to be optimized independently for its particular characteristics rather than using a single generalized approach

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system dynamically selects and switches between different haze type classifiers and specialized dehazers based on the input image characteristics. The classifier identifies the current haze type and intensity level, then the system adapts by applying the appropriate specialized dehazer, making the overall system flexible and responsive to varying conditions

Inventive Principle:

Principle #15Dynamics

2Measurement precision

If generalized dehazing methods are used, then processing can be simplified, but accuracy in specific haze type classification deteriorates

Engineering Contradiction:

Improvehaze type classification accuracyVSAvoidnumber of specialized models and classifiers

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent introduces haze type classifiers as intermediary components that mediate between the input hazy image and the specialized dehazers. The classifier first identifies the haze type and intensity level, then routes the image to the appropriate specialized dehazer, enabling accurate classification without requiring each dehazer to handle all haze types directly

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system performs preliminary classification of haze type and intensity level before applying dehazing processing. By pre-identifying the specific haze characteristics through classification, the system can then select and apply the most appropriate specialized dehazer, ensuring accurate processing while organizing the complexity in a structured sequence

Inventive Principle:

Principle #10Preliminary action

3Measurement precision

If multiple specialized dehazers are deployed, then dehazing accuracy improves, but system complexity increases

Engineering Contradiction:

Improvedehazing accuracyVSAvoidnumber of classifier models and dehazer models

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments the complex dehazing system into distinct modular components: multiple haze type classifiers and multiple specialized dehazers, each handling specific haze types. This segmentation allows independent training and optimization of each component while maintaining overall system accuracy, and enables selective deployment based on needs

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system achieves multi-functionality by creating a universal framework that can handle multiple haze types (fog, smoke, cloud, rain) and intensity levels through specialized models. Each specialized dehazer is trained on diverse datasets covering various conditions, making the collective system universally applicable to different haze scenarios while maintaining specialized accuracy

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12277682B1Method for image dehazing of vehicular images

Publication Date: 2025.04.15 IMAM MOHAMMAD IBN SAUD ISLAMIC UNIV

AI summary

A system for intelligent image dehazing based on haze type classification. The system receives a hazy image and classifies the haze type using either a Single Selective Classifier (SSC) or a Hybrid Conditional Classifier (HCC), based on user preference. If SSC is selected, a specific single model is used for classification. If HCC is selected, all pre-trained and pre-defined models are used. The system then selects a suitable specialized dehazer based on the predicted haze type and applies it to the input image to mitigate the effects of the haze. The system is flexible, allowing for adjustments to various parameters as per the users' requirements and target application domains.