对于现实世界杂的摄像头图像来说,一个简单的否定算法
1Institute of Biology, University Graz, 8010 Graz, Austria.
Journal of imaging
|September 27, 2023
概括
一种新的生物算法使用两步过器来增强噪音的摄像头图像. 这种方法的性能与非局部介质 (NL-means) 和总变化 (TV) 无声化相美,为图像质量改进提供了可行的替代方案.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 计算机摄影的使用
背景情况:
- 现实世界的摄像头图像显示出复杂的噪声模式,挑战了标准的无声化算法.
- 现有的拒绝方法,如BM3D,波形,非局部介质 (NL-介质),总变异 (TV) 拒绝和双边过,具有不同的性能特征.
- 保持精细的图像细节和对象轮,同时减少噪音,对于图像质量至关重要.
研究的目的:
- 引入和评估一种修改后的生物算法,以提高现实世界噪音摄像头图像的质量.
- 为了比较拟议的两步无声化算法的性能与几种已知的方法.
- 通过参数控制和并行处理,优化除过程以提高效率.
主要方法:
- 实施了一种两步的消噪方法,首先是自适应局部平均波器,以减少传感器噪声和保存细节.
- 在第二个步骤中应用了一个不清晰的面膜过器来增强图像的清晰度,旨在实现基准真相质量.
- 该算法在公开可用的图像数据集上进行了测试,其性能与BM3D,波形,NL-means,电视无声化和双边过进行了基准测试.
主要成果:
- 拟议的两步过器表现出与NL-means和电视反噪方法相提并论的性能.
- BM3D实现了优异的无色化,但偶尔会导致图像模糊.
- 这种新的过器依赖于从全球图像统计数据中获得的单个参数,并利用YUV颜色空间转换来提高计算效率.
结论:
- 修改后的生物算法提供了一种有效的方法来消除现实世界摄像头图像的噪声,平衡降低噪声与保存细节.
- 该方法为现有技术提供了竞争力的替代方案,特别是NL-means和电视无声化,具有单个可调节参数的优势.
- 通过仅处理Y通道和在多核处理器上采用并行计算来提高计算效率.
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