Sharpness-aware evaluation methodology for haze-removal processing in automotive systems

Abstract

This paper presents a new comparison method for haze-removal algorithms in nextgeneration automotive systems. Compared to previous peak signal-to-noise ratio–based comparisons, which measure similarity, the proposed modulation transfer function–based method checks sharpness to select a more suitable haze-removal algorithm for lane detection. Among the practical filtering schemes used for a haze-removal algorithm, experimental results show that Gaussian filtering effectively preserves the sharpness of road images, enhancing lane detection accuracy.