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

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 nonuniform 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 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 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. INDEX TERMS nonuniform illumination image; image enhancement; Histogram Equalization; nonlinear histogram modification; exposure regions. I. INTRODUCTION 38 During image acquisition, light sources such as the sun, the 39 moon and fluorescent light will radiate light to the object, 40 which is then captured by the acquisition device sensor that 41 produces an image. However, certain conditions either 42 caused by the image acquisition device (i.e. inappropriate 43 adjustment and limitation of the device properties) or by the 44 condition of object itself (i.e. different absorption and 45 reflection properties of the object on light irradiated) can 46 result in uneven exposure to the object in an image [1] [2]. 47 Hence, different illuminati...

Low-Light Image Enhancement via Gradient Prior-Aided Network

“…In addition, it controls over-enhancement and under-enhancement by cropping the histogram of the image. Nonlinear exposure intensitybased modification histogram equalization (NEIMHE) [46] divides a non-uniformly illuminated image into five subregions and modifies each sub-region histogram by setting nonlinear weights in the cumulative density function of each sub-region histogram. HE-based methods effectively enhance contrast in the whole or part of the image, but most methods are inflexible.…”

Dynamic Clip Limit Window Size Histogram Equalization for Poor Information Images

“…The techniques of this groups attempt to overcome the inhomogeneous brashness problem of the HE technique by dividing the histogram of the image into spans and equalizing them to produce an enhanced resultant image. Exposure based sub image HE (ESIHE) [6], adaptive bi-HE (ABHE) [23], and nonlinear exposure intensity based modification HE (NEIMHE) [24] techniques use exposure to enhance the contrast of the image. ESIHE divides the image's histogram into two subs by the use of exposure threshold.…”

Adaptive Clip Limit Tile Size Histogram Equalization for Non-Homogenized Intensity Images

“…This approach improves image brightness based on formulated regions and produces realistic images (non-under or over-enhanced), such as the global histograms category. Some approaches are purposely designed under this sub-class to preserve image brightness in non-homogenous intensity images, such as exposure-based sub-image HE (ESIHE) [7], Exposure Region-based Multi-HE (ERMHE) [13], Median and Mean Bi-HE plateau limit (Mean-BHEPL & Median-BHEPL) [14], adaptive Bi-HE (ABHE) algorithm [14], and Nonlinear Exposure Intensity-Based Modified HE (NEIMHE) [15]. By applying the exposure threshold, the ESIHE approach splits the image's histogram into two areas, and both sub-histograms will be clipped using the gray level's mean value.…”

“…A more recent study by [15] showed that a Nonlinear Exposure Intensity-Based Modified HE (NEIMHE) enhanced non-uniform illumination images. The NEIMHE approach splits the input image into five sub-regions and transforms the histogram of each sub-region by assigning a nonlinear weight to its cumulative density function (CDF).…”