研究目的
To provide a methodology for underwater image enhancement through applying existing techniques oriented towards the treatment of their main deficiencies: lightning enhancement, contrast enhancement, and noise reduction.
研究成果
The HF-HSV CLAHE-BF, HF-RGBHSV CLAHE-BF, and HF-LAB CLAHE-BF sequences result in improved visibility, more balanced lighting, and reduction of noise. The processed images have a wider range of colors and more contrast between objects in the scene. However, the LAB and HSV results are similar in that they are not capable of fully eliminating color casts typical of many underwater environments. Future research should address the issue of automatically selecting an adequate method depending on the attributes of the input images.
研究不足
The measurement of the quality of the processed images is challenging without ideal, undegraded images for comparison. The techniques may introduce artifacts and noise, especially when manipulating color spaces directly.
1：Experimental Design and Method Selection:
The methodology implements a filter sequence for enhancing underwater images based on a pre-established quality metric. The sequence includes homomorphic filtering for lighting balance, CLAHE for contrast enhancement, and bilateral filtering for noise reduction and edge enhancement.
2：Sample Selection and Data Sources:
Images were taken from the QUT image dataset and other smaller databases used in similar works.
3：List of Experimental Equipment and Materials:
A desktop computer with an i7-4820k 3.70-GHz processor and 32 GB of RAM, with code being executed in high-performance mode.
4：70-GHz processor and 32 GB of RAM, with code being executed in high-performance mode.
Experimental Procedures and Operational Workflow:
4. Experimental Procedures and Operational Workflow: The sequence begins by balancing lighting using homomorphic filtering, then contrast is enhanced through CLAHE, and finally noise is removed by bilateral filtering.
5：Data Analysis Methods:
The effectiveness of the methods was measured through histogram analysis and border detection using the Sobel border-detection algorithm.
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