基于增强背景滤波和小波融合的水下机器人深海图像高可视性和高检出率

A. A. Ghani, A. Nasir, W. Tarmizi
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引用次数: 13

摘要

本文提出了一种用于水下机器人的深海水下图像对比度和可见度的增强技术。该技术采用增强背景滤波和小波融合方法(EBFWF)相结合的方法。与其他最先进的方法相比,这种方法的新颖之处在于它的方法和所提出的方法的能力,可以最大限度地减少水下的负面影响,如蓝色和绿色偏色、低对比度和低能见度。该方法由几个步骤组成,旨在消除负面影响,从而提高水下图像的对比度和可见度。这样做的目的是为目标检测和识别过程提供一个更好的平台。在移除低频背景之前,首先锐化输入图像。这最大限度地减少了在结果处理步骤中图像数据被视为噪声的概率。然后基于中间颜色通道映射图像直方图,以减小主色通道和次色通道之间的差距。采用小波融合,然后进行自适应局部直方图规范处理。实验结果表明,所提出的EBFWF技术在提高整体水下图像质量方面具有较好的计算效果和显著性。通过该方法处理得到的图像可以进一步用于检测和识别,以提取更多有价值的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integration of enhanced background filtering and wavelet fusion for high visibility and detection rate of deep sea underwater image of underwater vehicle
This paper presents an enhanced technique for contrast and visibility improvement for deep sea underwater image which is normally used for underwater robot. The proposed technique uses an integration approach of enhanced background filtering and wavelet fusion methods (EBFWF). The novelty lies in this case in its methodology and capability of the proposed approach to minimize negative underwater effects such as blue and green color casts, low contrast, and low visibility in comparison with other state-of-the-art methods. The proposed method consists of a few steps that aims to eliminate negative effects and thus improving the contrast and visibility of underwater image. This purpose is carried out to provide a better platform for object detection and recognition processes. The input image is first sharpen before the low frequency background is removed. This minimizes the probability of image data to be regarded as noise in the consequences processes' steps. Image histograms are then mapped based on the intermediate color channel to reduce the gap between the inferior and dominant color channels. Wavelet fusion is applied followed by adaptive local histogram specification process. Based on the conduced tests, the proposed EBFWF technique, computationally, more effective and significant in improving the overall underwater image quality. The resultant images processed through the proposed approach could be further used for detection and recognition to extract more valuable information.
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