Underwater Image Enhancement Based on Global and Local Equalization of Histogram and Dual-Image Multi-Scale Fusion

Abstract

Underwater images suffer from color cast and low visibility caused by the medium scattering and absorption, which will reduce the use of valuable information from the image. In this paper, we propose a novel method which includes four stages of pixel intensity center regionalization, global equalization of histogram, local equalization of histogram and multi-scale fusion. Additionally, this method uses a pixel intensity center regionalization strategy to perform centralization of the image histogram on the overall image. Global equalization of histogram is employed to correct color of the image according to the characteristics of each channel. Local equalization of dual-interval histogram based on average of peak and mean values is used to improve contrast of the image according to the characteristics of each channel. Dual-image multiscale fusion to integrate the contrast, saliency and exposure weight maps of the color corrected and contrast enhanced images. Experiments on variety types of degraded underwater images show that the proposed method produces better output results in both qualitative and quantitative analysis, thus, the proposed method outperforms other state-of-the-art methods. INDEX TERMS Underwater image enhancement, Pixel intensity center regionalization, Histogram equalization, Multi-scale fusion.

Underwater Image Enhancement Based on Multi-Scale Fusion and Global Stretching of Dual-Model

“…Because of the poor performance of UCM [21] in qualitative evaluation, we do not carry out a quantitative analysis of UCM. As illustrated in Table 1, RD [22] and GLHDF [14] perform better in UCIQE index. The color of these two algorithms is more saturated.…”

“…Figure 8 shows the qualitative results of various algorithms for the image with blue/green deviation (Data A). The results of RD [22] and GLHDF [14] are more colorful and produce unexpected oversaturation with a red hue. The results of UCM [21], RGHS [13], and IBLA [7] still have obvious color deviation.…”

“…In this section, we compare the proposed approach with the existing classical and state-of-the-art underwater restoration/enhancement techniques, namely, unsupervised color correction (UCM) [21], Rayleigh distribution (RD) [22], relative global histogram stretching (RGHS) [13], image blurriness and light absorption (IBLA) [7], global and local equalization of histogram and fusion (GLHDF) [14], and color balance and fusion (Fusion) [12]. The results of each method are evaluated qualitatively and quantitatively.…”

“…In 2018, Huang et al [13] put forward relative global histogram stretching (RGHS) in the red, green, blue (RGB) and Commission International Eclairage-Lab (CIE-Lab) color models, which effectively improves the visual effect of the blurred image, except for the color deviation image. In 2020, Bai et al [14] proposed an enhancement method based on global and local equalization of the histogram and dual-image multi-scale fusion (GLHDF), which obtained good performance, but there existed some limitations in processing turbid water images.…”

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

“…With the development of research, investigators paid more attention to proposing single image dehazing methods that need no additional hardware devices or complex operations to assist. The underwater single image dehazing techniques can be roughly categorized into three branches: underwater image enhancement methods, underwater image restoration methods, and data-driven methods [17]. The underwater image restoration methods always require additional prior knowledge to reconstruct the degraded image.…”