LiCENt: Low-Light Image Enhancement Using the Light Channel of HSL

Abstract

Images captured in low-brightness environments often lead to poor visibility and exhibit artifacts such as low brightness, low contrast, and color distortion. These artifacts not only affect the visual perception of the human eye but also decrease the performance of computer vision algorithms. Existing deep learning-based image enhancements studies are quite slow and usually require extensive hardware specifications. Conversely, lightweight enhancement approaches do not provide satisfactory performance as compared to state-of-the-art methods. Therefore, we proposed a fast and lightweight deep learning-based algorithm for performing low-light image enhancement using the light channel of Hue Saturation Lightness (HSL). LiCENt stands for Light Channel Enhancement Network that uses a combination of an autoencoder and convolutional neural network (CNN) to train a low-light enhancer to first improve the illumination and later improve the details of the low-light image in a unified framework. This method used a single channel lightness 'L' of HSL color space instead of traditional RGB color channels which helps in reducing the number of learnable parameters by a factor of 8.92, at the most. LiCENt also has significant advantages for the Brilliance Perception Adjustment, which enables the model to avoid issues including over-enhancement and color distortion. The experimental results demonstrate that our approach generalizes well in synthetic and natural low-light images and outperforms other methods in terms of qualitative and quantitative metrics.

Enhancement and Noise Suppression of Single Low-Light Grayscale Images

“…In this section, the parameters of the proposed method were first analyzed, and then the proposed method was compared with eight state-of-the-art algorithms [6,8,9,12,16,18,24,31] in the aspects of visual effect and objective evaluation indices. On the basis of image dehazing, reference [12] proposed a model that can directly use the DCP-based method to deal with the inverted image.…”

“…Reference [6] is proposed based on the bilateral gamma adjustment function and combined with the particle swarm optimization (PSO), and the algorithm significantly enhanced the visual effect of the low illumination gray images. Reference [16] proposed a fast and lightweight deep learningbased algorithm for performing low-light image enhancement using the light channel of hue saturation lightness (HSL). This method used a single channel lightness 'L' of HSL color space instead of traditional RGB color channels to reduce time consumption.…”

“…BIMEF [24] Multi-exposure fusion framework LIME [8] Retinex DCP [12] DCP Retinex DIP [31] Retinex + learning BGA [6] Bilateral gamma adjustment function + PSO (one channel) HSL [16] Deep learning(one channel) DNS [9] Retinex + deep noise suppression GAN [18] Retinex + deep learning The proposed method Multi-exposure fusion framework…”

“…ZH proposed a twostage low-light image signal processing network and a two-branch network to reconstruct the low-light images and enhance textural details [15]. An auto-encoder and convolutional neural network (CNN) to train a low-light enhancer to first improve the illumination and later improve the details of the low-light image in a unified framework was proposed to avoid issues such as over-enhancement and color distortion [16]. By treating the low-light enhancement as a residual learning problem, that is, to estimate the residual between lowand normal-light images, Wang et al proposed a novel deep lightening network that benefited from the recent advancement of CNNs [17].…”

Low Light Image Enhancement Algorithm Based on Detail Prediction and Attention Mechanism

“…Li, Chongyi, et al proposed LightenNet: A convolutional neural network for weakly illuminated image enhancement [ 10 ]. LiCENt, a fast and lightweight algorithm is proposed [ 11 ], and the combination of automatic encoder and convolutional neural network (CNN) is used to train the weak light intensifier. Among them, Zero-DCE and Zero-DCE++ first put forward the idea of the zero-reference curve [ 12 , 13 ].…”