Robust object detection in images corrupted by impulse noise
Rykhard Petrovich Bohush, Sergey Ablameyko, Yahor Ruslanovich Adamovskiy · 2020
This paper proposes two effective normalized similarity functions for robust object detection in very high density impulse noisy images.These functions form an integral similarity estimate based on relations of minimum by maximum values for all pairs of analyzed image features.To provide invariance under the constant brightness changes, zero-mean additive modification is used.We explore properties of our functions and compare them with other commonly used for object detection in images corrupted by impulse noise.The efficiency of our approach is illustrated and confirmed by experimental results.