Nonlinear Exposure Intensity Based Modification Histogram Equalization for Non-Uniform Illumination Image Enhancement

Nor Hidayah Saad,Hariyanti Mohd Saleh,Nor Ashidi Mat Isa

doi:10.1109/access.2021.3092643

Nor Hidayah Saad, Hariyanti Mohd Saleh + Show 1 more

Open Access

https://doi.org/10.1109/access.2021.3092643

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Abstract

Non-uniform illumination image is often generated owing to various factors, such as an improper setting in the image acquisition device and absorption or reflectance of the objects that results in the existence of different exposure regions in the image. Although Histogram Equalization (HE) is well known and widely used in image enhancement, existing HE-based methods often generate washed-out effects and show unnatural appearance due to the over-enhancement phenomenon, which limits the capabilities of achieving illumination uniformity of an image. Therefore, this study proposes a modified HE method for non-uniform illumination image, namely Nonlinear Exposure Intensity-Based Modification Histogram Equalization (NEIMHE). The proposed NEIMHE method divides the non-uniform illumination image into five sub-regions and modifies the histogram of each sub-region by setting a nonlinear weight into their cumulative density function (CDF) of histogram in each sub-region. Each modified histogram is then equalized using modified HE equations that provide the intensity expansion and different intensity mapping directions for under-exposed and over-exposed sub-regions. A total of 354 non-uniform illuminated sample images were used to evaluate the performance of the proposed NEIMHE method, qualitatively and quantitatively. The proposed NEIMHE method was compared qualitatively with five state-of-the-art methods: Backlit, Adaptive Fuzzy Exposure Local Contrast Enhancement (AFELCE), Visual Contrast Enhancement Algorithm Based on Histogram Equalization (VCEA), Exposure Region-based Multi Histogram Equalization (ERMHE); and Exposure based Sub-Image Histogram Equalization (ESIHE). The proposed NEIMHE method produced an enhanced image with more uniform illumination, better preservation of image details, and high capability of maintaining image naturalness. Quantitatively, the proposed NEIMHE method achieved the highest scores in Discrete Entropy (DE), Measure of Enhancement (EME), Measure of Enhancement by Entropy (EMEE), and Peak Signal to Noise Ratio (PSNR); it attained second-best in Absolute Mean Brightness Error (AMBE) and Lightness Order Error (LOE). From both analyses, the proposed NEIMHE method has shown its capability of enhancing different exposure regions that exist in non-uniform illumination images.

Highlights

During image acquisition, light sources such as the sun, the moon and fluorescent light will radiate light to the object, which is captured by the acquisition device sensorThe associate editor coordinating the review of this manuscript and approving it for publication was Yongjie Li.that produces an image
The exposure-based intensity mapping functions extended the range of OE and UE regions based on the Zone system
The performance of the proposed Nonlinear Exposure Intensity-Based Modification Histogram Equalization (NEIMHE) method in improving the enhancement of original non-uniform illumination images was studied qualitatively and quantitatively. It tested 266 close-up and 88 non-close-up non-uniform illumination images, while evaluation was performed against four existing non-uniform illumination image enhancement methods, i.e., Backlit, Exposure Region-based Multi Histogram Equalization (ERMHE), Adaptive Fuzzy Exposure Local Contrast Enhancement (AFELCE), and VCEA

Summary

Introduction

Light sources such as the sun, the moon and fluorescent light will radiate light to the object, which is captured by the acquisition device sensorThe associate editor coordinating the review of this manuscript and approving it for publication was Yongjie Li.that produces an image. Saad et al.: NEIMHE for Non-Uniform Illumination Image Enhancement (i.e. shadow region) and sometimes extreme bright regions (i.e. bright sky) could be observed in an image. These conditions affect visual evaluation, may lead to misinterpretation of information from the image and inaccuracies in subsequent processes in image analysis [2], [3]. The WE regions normally provide clear information, are preferable to be captured during image acquisition

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