Histogram Equalization Variants as Optimization Problems: A Review

Adaptive Entropy Index Histogram Equalization for Poor Contrast Images

“…The resulting images from these techniques are superior over those produced by BBHE, DSIHE, and MMBEBHE given their ability to preserve image brightness. However, these techniques suffer from an inefficient improvement in image contrast [20]. According to [17], BHE3PL can produce an excellent resultant image with superior image quality.…”

“…One main source of optimization algorithms is nature itself, which has inspired many researchers to obtain solutions to real-world problems. These algorithms are referred to as nature-inspired-based optimization algorithms (NIOA) [20]. In some cases, the obtained solution may not be the optimal one [47].…”

An Adaptive Detail Equalization for Infrared Image Enhancement Based on Multi-Scale Convolution

“…In recent years, histogram equalization [6], transform domain equalization [7], [8] and enhancement methods based on image stratification [9] have been widely used for image enhancement and have achieved good enhancement effects. Histogram equalization is an image enhancement method with contrast stretch, which can effectively improve the brightness and contrast of low-quality infrared images.…”

“…Histogram equalization is an image enhancement method with contrast stretch, which can effectively improve the brightness and contrast of low-quality infrared images. Chhaya et al [10], Dhal et al [6] and Sim et al [11] used different thresholds to divide the histogram of the original image into two or more subintervals for image equalization operation, so as to improve the image quality. Nevertheless, these methods did not consider the distribution of the original histogram curve when the threshold is selected, especially the enhancement effect was poor when the local peak occurred in the histogram curve of the image.…”

“…Magudeeswaran et al [31] and Tufai et al [5] divided the original image into background area and target area for enhancement. Sim et al [11] and Dhal et al [6] divided the original image into detail layer and structure layer. These methods could solve the blur problem of background and target area, but could not improve the contrast of image.…”