Local Reference Feature Transfer (LRFT): A simple pre-processing step for image enhancement

Abstract

Low-light, nighttime haze, and underwater images captured in harsh environments typically exhibit color deviations and reduced visibility due to light scattering and absorption. Additionally, we observe an almost complete loss of information in at least one color channel in these degraded images. To repair the lost information in each channel, we present an image preprocessing strategy called Local Reference Feature Transfer (LRFT), which employs the local feature to compensate for the color loss automatically. Specifically, we design a dedicated reference image by fusing the detail, salience, and uniform grayscale images of the raw image that ensures a balanced chromaticity distribution. Subsequently, we employ the local reference feature transfer strategy to migrate the local mean and variance of the reference image to the raw image to get a color-corrected image. Extensive evaluation experiments demonstrate that our proposed LRFT method has good preprocessing performance for the subsequent enhancement of images of different degradation types. The code is publicly available at: https://www.researchgate.net/publication/383528251_2024-LRFT.