Underwater Image Histogram Enhancement With Limited Training Data
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Solution Overview
Problem
Underwater images suffer from poor quality due to light scattering and absorption, and existing deep learning models struggle to enhance these images effectively due to the lack of corresponding training data.
Innovation Solution
An underwater image enhancement method using a deep learning model that processes histograms of underwater images, enabling high-quality restoration and color representation with limited training data, employing transformer-based models and generative adversarial networks for detailed enhancement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional deep learning models are used for underwater image enhancement, then image quality improvement is achieved, but a large amount of training data is required which is difficult to obtain
Solution Approach 1:
The patent extracts the essential statistical characteristics of underwater images by converting images into histograms, which represent the distribution of pixel intensities. This extraction transforms the problem from learning complex image-to-image mappings to learning histogram-to-histogram transformations, significantly reducing the data requirements while preserving the essential degradation patterns caused by water scattering and absorption.
Solution Approach 2:
The patent changes the representation parameters from full-image pixel data to histogram-based statistical parameters. By transforming the input from high-dimensional image data to lower-dimensional histogram data, the model reduces computational complexity and data requirements while maintaining the ability to capture the essential color and contrast deviations characteristic of underwater imaging conditions.
2Ease of manufacture
If deep learning models are trained with limited data, then training requirements are met, but image enhancement performance falls short of expectations
Solution Approach 1:
The patent introduces histograms as an intermediary representation between the original underwater image and the enhanced output. This intermediary captures the essential statistical properties of image degradation (color casts, contrast loss) in a compressed form, allowing the deep learning model to learn effective enhancement patterns with limited data while maintaining high enhancement performance through the histogram specification process.
Data Source
AI summary
An underwater image enhancement method and image processing system using the same are provided. The method includes the following steps. An original underwater image is received. An original histogram of the original underwater image is generated. The original histogram is input into a deep learning model to generate an optimized histogram. An optimized underwater image is generated according to the optimized histogram and the original underwater image.