红外小目标检测NARM文献 | 方法对比 |数据集描述

WaterontheMoom

于 2021-03-30 11:45:41 发布

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分类专栏：红外小目标检测文献红外小目标检测

本文链接：https://blog.youkuaiyun.com/yellow_we/article/details/115307029

该博客讨论了多种基于目标与背景分离的方法在处理红外小目标检测时的优缺点。针对强边缘和非目标干扰的问题，提出了一种结合非凸秩近似最小化(NRAM)和l2,1范数的新方法，旨在改善目标恢复并抑制背景噪声。文章通过实例比较了不同方法的性能，并指出新方法在处理复杂背景和边缘问题上的优势。

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对比基于目标与背景分离的典型方法特点

Methods	Advantages	Disadvantages
IPI	Works well with uniform scenes.	Over-shrinks the small targets, leaving residuals inthe target image, time consuming.
NIPPS	Works well when strong edges and non-target interferences are few.	Difficult to estimate rank of data, fails to eliminate strong edges and non-target interference.
ReWIPI	Works well when background changes slowly.	Sensitive to rare highlight borders, performance degrading with the increasing of complexity.
LRR	Works well with simple scenes.	Cannot handle complex backgrounds.
LRSR	Works well with homogeneous backgrounds.	Difficult to choose two dictionaries simultaneously, leaving noise in target component.
FBOD +GGTOD	Work well with sky backg