Naturalness Preserved Enhancement Algorithm for Non-Uniform Illumination Images

Abstract

Image enhancement plays an important role in image processing and analysis. Among various enhancement algorithms, Retinex-based algorithms can efficiently enhance details and have been widely adopted. Since Retinex-based algorithms regard illumination removal as a default preference and fail to limit the range of reflectance, the naturalness of non-uniform illumination images cannot be effectively preserved. However, naturalness is essential for image enhancement to achieve pleasing perceptual quality. In order to preserve naturalness while enhancing details, we propose an enhancement algorithm for non-uniform illumination images. In general, this paper makes the following three major contributions. First, a lightness-order-error measure is proposed to access naturalness preservation objectively. Second, a bright-pass filter is proposed to decompose an image into reflectance and illumination, which, respectively, determine the details and the naturalness of the image. Third, we propose a bi-log transformation, which is utilized to map the illumination to make a balance between details and naturalness. Experimental results demonstrate that the proposed algorithm can not only enhance the details but also preserve the naturalness for non-uniform illumination images.

Non-uniform illumination endoscopic imaging enhancement via anti-degraded model and L 1 L 2-based variational retinex

“…6, the results obtained by the proposed algorithm gave a natural look, not only enhanced details in dark areas, but also prevented color cast. [34], MSR [22], spatially adaptive retinex variational model (SARV) [35], and Naturalness preserved enhancement algorithm (NPEA) [36] methods. Clearly, LHE, MSR, and SARV gave over-enhanced images, simultaneously saturating the resulting images much further and causing color cast.…”

Symmetrical Logarithm Transformation Method for Driving Vision Enhancement

“…In order to show its superiorities, the proposed method will be compared with some other advanced methods, which include histogram equalization (HE) [8], scaling coefficients enhancement (CES) [16], dynamic fuzzy histogram equalization (DFHE) [10], fourier domain multi-scale retinex enhancement (FRE) [17], adaptive gamma correction (AGC) [18], and naturalness-preserved enhancement algorithm (NPEA) [19]. The experimental images are captured by ordinary tachograph.…”

Mission-critical monitoring based on surround suppression variational Retinex enhancement for non-uniform illumination images

“…However, reflectance should be within [0~1], which means that it cannot completely contains the whole information of input image. Moreover, illumination component represents ambience information [35,36]. In order to preserve the naturalness as well as enhance details, we add a contrast gain for reflectance and a gamma correction operation for illumination after the decomposition.…”

“…The parameters were set as α = 4, β = 0.06, μ = 0.04, λ = 0.02, and γ = 0.02. In this paper, the proposed algorithm is compared to the existing MSR [13], LHE [7], AL [6], ALTM [37], GUM [9], SARV [28], and NPE [36] methods. Clearly, MSR, LHE, AL, and GUM gave over enhanced images, simultaneously saturating the resulting images much further and causing color distortion.…”