FDMLNet: A Frequency-Division and Multiscale Learning Network for Enhancing Low-Light Image

Abstract

Low-illumination images exhibit low brightness, blurry details, and color casts, which present us an unnatural visual experience and further have a negative effect on other visual applications. Data-driven approaches show tremendous potential for lighting up the image brightness while preserving its visual naturalness. However, these methods introduce hand-crafted holes and noise enlargement or over/under enhancement and color deviation. For mitigating these challenging issues, this paper presents a frequency division and multiscale learning network named FDMLNet, including two subnets, DetNet and StruNet. This design first applies the guided filter to separate the high and low frequencies of authentic images, then DetNet and StruNet are, respectively, developed to process them, to fully explore their information at different frequencies. In StruNet, a feasible feature extraction module (FFEM), grouped by multiscale learning block (MSL) and a dual-branch channel attention mechanism (DCAM), is injected to promote its multiscale representation ability. In addition, three FFEMs are connected in a new dense connectivity meant to utilize multilevel features. Extensive quantitative and qualitative experiments on public benchmarks demonstrate that our FDMLNet outperforms state-of-the-art approaches benefiting from its stronger multiscale feature expression and extraction ability.

Enhancement of Mine Images through Reflectance Estimation of V Channel Using Retinex Theory

“…Supervised methods require collecting paired images in advance to establish the mapping relationship between normal and low-light images [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. Zhang et al [18] proposed the KinD++ algorithm, which makes the processed images more realistic.…”

“…Wu et al [25] presented URetinex Net, a deep unrolling network based on Retinex theory, which combines an implicit prior regularization model with Retinex theory to better suppress noise and preserve details. Lu et al [27] proposed a frequency-divided multiscale learning network that decomposes low-light images into high-frequency and low-frequency components. They employed multiscale feature extraction and attention mechanisms to enhance the images.…”

DBENet: Dual-Branch Brightness Enhancement Fusion Network for Low-Light Image Enhancement

“…Currently, various methods have been proposed for LLIE, including histogram equalization (HE) [5,6], non-local means filtering [7], Retinex-based methods [8,9], multi-exposure fusion [10][11][12], and deep-learning-based methods [13][14][15], among others. While these approaches have achieved remarkable progress, two main challenges impede their practical deployment in real-world scenarios.…”