An Experiment-Based Review of Low-Light Image Enhancement Methods

Abstract

Images captured under poor illumination conditions often exhibit characteristics such as low brightness, low contrast, a narrow gray range, and color distortion, as well as considerable noise, which seriously affect the subjective visual effect on human eyes and greatly limit the performance of various machine vision systems. The role of low-light image enhancement is to improve the visual effect of such images for the benefit of subsequent processing. This paper reviews the main techniques of low-light image enhancement developed over the past decades. First, we present a new classification of these algorithms, dividing them into seven categories: gray transformation methods, histogram equalization methods, Retinex methods, frequency-domain methods, image fusion methods, defogging model methods and machine learning methods. Then, all the categories of methods, including subcategories, are introduced in accordance with their principles and characteristics. In addition, various quality evaluation methods for enhanced images are detailed, and comparisons of different algorithms are discussed. Finally, the current research progress is summarized, and future research directions are suggested. INDEX TERMS Review, survey, low-light image enhancement, Retinex method, image enhancement, quality evaluation.

A Novel Two-Step Strategy Based on White-Balancing and Fusion for Underwater Image Enhancement

“…The gamma-correction operation is generally used for enhancing or reducing global-contrast of images. In a similar way we utilize the gamma-correction operation to adjust the global intensity on images by a power-function transform, as illustrated in (10):…”

“…Hence, underexposure versions can be obtained by setting different values of in (10). Although, reducing-exposure decreases brightness of the image, the fusion result of multiple underexposure versions can recover details well in underwater images, which can be shown in Fig.…”

“…Besides photography and video recording, underwater imaging has been applied to various work tasks and scientific discoveries, such as underwater artificial-facility monitoring [1], underwater object detection [2], marine creatures discovering [3], and underwater vehicles controlling [4]. However, images directly captured from underwater environment always suffer from severe degradation, such as undesired color-cast, contrast reduction, and detail loss [5], [6] caused by light scattering and absorption, which seriously limit the acquisition of available information from the image [7]- [10]. Therefore, acquisition of clear and accurate images is an important prerequisite to help scientists understand the underwater environment.…”

LLISP: Low-Light Image Signal Processing Net via Two-Stage Network

“…And the key steps in the ISP include: ISO gain, denoising, demosaicing, detail enhancing, white balance, color manipulation and color mapping. The quality of these JPEG-format images is very important both for our daily life and for many computer vision tasks, e.g., video surveillance, segmentation, and object detection [1], [2]. However, images captured in low-light environments suffer from various problems such as heavy noise, color distortion and blur.…”

Fuzzy Mapped Histogram Equalization Method for Contrast Enhancement of Remotely Sensed Images

“…The images received at the earth station might not be visually pleasing due to undesirable atmospheric conditions such as haze, fog, excess darkness etc. The dynamic range of intensity of images under extreme weather conditions is generally much narrower than its desirable value, which largely impacts the visibility of such poor contrast images [8]- [10]. The information extracted from such images is limited because of insufficient visual perception of the images.…”