Naturalness Preserved Image Enhancement Using <italic>a Priori</italic> Multi-Layer Lightness Statistics

Abstract

Enhancement of non-uniformly illuminated images often suffers from over-enhancement and produces unnatural results. This paper presents a naturalness preserved enhancement method for non-uniformly illuminated images, using multi-layer lightness statistics acquired from high-quality images. Our work makes three important contributions: designing a novel multi-layer image enhancement model; deriving the multi-layer lightness statistics of high-quality outdoor images, which are incorporated into the multi-layer enhancement model; and showing that the overall quality rating of enhanced images is consistent with a combination of contrast enhancement and naturalness preservation. Two separate human observer evaluation studies were conducted on naturalness preservation and overall image quality. The results showed the proposed method outperformed four compared state-of-the-art enhancement methods.

Progressive Retinex

“…9. The LDSE Figure 8: The contrast enhancement and denoising results of state-of-the-art methods including SRIE [8], JIEP [3], LIME [11], NPE [33], SRLL [21], HQEC [40], and the proposed method. can improve the overall visibility of scenes but introduces the color cast and blur.…”

“…Compared with using IM-Net only, i.e., the noise level is 0, the quantitative results are significantly improved after denoising. However, the denoising results guided Figure 7: The contrast enhancement results of state-of-the-art methods including SRIE [8], JIEP [3], LIME [11], NPE [33], SRLL [21], HQEC [40], and the proposed method.…”

Detection of Lane-Change Events in Naturalistic Driving Videos

“…5(c) causes the two local areas in the same frame to be different. According to Retinex theory, reflectance is invariant to the illumination, 20,22,41,42 so our method uses reflectance rather than the raw image to evaluate the background variation. Based on Retinex theory, the reflectance of a window can be derived by $$R\left(x,y\right)=SQ\left(cx+x,cy+y\right)/I_e,$$where ( cx ; cy ) indicates the centers of the window, SQ indicates the lightness of the window, I e is the environment illumination, which is simply estimated by the average lightness of the brightest 10% pixels in the window, and R ( x ; y ) is the reflectance of the window.…”

A Variational Model for Sea Image Enhancement

“…We also compared the results using our method with those using the other six state-of-the-art image enhancement methods: HE, SSR [10], MSR [11], Naturalness-Preserved Enhancement (NPE) algorithm [8], Simultaneous Reflectance and Illumination Estimation (SRIE) model [19], and Probabilistic method for Image Enhancement (PIE) [13]. The six comparison methods above basically cover the typical methods involved in the introduction: HE-based enhancement methods, masking-based enhancement methods, Retinex-based enhancement methods and total variation methods.…”

“…This method focuses on the design of reasonable frequency band decomposition methods and the selection of different frequency band enhancement methods according to different image characteristics. For example, Wang et al [8] designed a multi-layer model and processed different layers according to their different luminance statistics, effectively achieving a naturalness-preserved image enhancement. This enhancement method requires that the image should have a higher administrative-level sense; however, the texture response in the sea image is very weak and it is almost impossible to obtain detailed information through delamination.…”