In the consumer electronics field, the main challenge in image processing is to preserve the original brightness. Histogram Equalization (HE) is one of the simplest and widely used methods for contrast enhancement. However, HE does not suit into the consumer electronics field as this procedure flattens the histogram by distributing the entire gray levels uniformly. Therefore, several HE variants have been proposed based on proper histogram segmentation, histogram weighting, and range optimization techniques to overcome this flattening effect. However, sometimes these modifications become complex and computationally expensive. Recently, researchers have formulated the HE variants for image enhancement as optimization problems and solved, using Nature-Inspired Optimization Algorithms (NIOA), which starts a new era in the image enhancement field. This study presents an up-to-date review over the application of NIOAs for HE variants in image enhancement domain. The main issues which are involved in the application of NIOAs with HE are also discussed here.

Histogram Equalization Variants as Optimization Problems: A Review

In the field of image processing, there are several problems where the efficient search has to be performed in complex search domain to find an optimal solution Image enhancement which improves the quality of an image for visual analysis and/or machine understanding is one of these problems There is no unique image enhancement technique and it’s measurement criterion which satisfies all the necessity and quantitatively judge the quality of a given image respectively Thus sometimes proper image enhancement problem becomes hard and takes large computational time In order to overcome that problem, researchers formulated the image enhancement as optimization problems and solved using Nature-Inspired Optimization Algorithms (NIOAs) which starts a new era in image enhancement field This study presents an up-to-date review over the application of NIOAs in image enhancement domain The key issues which are involved in the formulation of NIOAs based image enhancement models are also discussed here

A Survey on Nature-Inspired Optimization Algorithms and Their Application in Image Enhancement Domain

Aiming at the characteristics of remote sensing images with low-contrast, weak edge preservation, and poor resolution textual information, an image enhancement method that combines nonsubsampled shearlet transform (NSST) and guided filtering is presented. First, histogram equalization is applied to the remote sensing image. Second, the image is decomposed into a low frequency component and several high frequency components by NSST. Then, a linear stretch is adopted for the coefficients of the low-frequency component to improve the contrast of the original image; the threshold method is used to restrain the noise in the high-frequency components, then guided filtering is used for dealing with the high-frequency components, improving the detail information and edge-gradient retention ability. Finally, the final enhanced image is reconstructed by applying the inverse NSST to the processed low- and high-frequency components. The results show that the algorithm can significantly improve the visual impression of the image. Compared with the proposed algorithms in recent years, the average gradient and information entropy are significantly improved and the running time is shortened.

Remote sensing image enhancement based on the combination of nonsubsampled shearlet transform and guided filtering

This paper presents a new approach for improving the contrast of digital colour images in extreme lighting environment. The concept of information sets that are derived from fuzzy sets is applied for this purpose. The S and V components of the HSV (hue, saturation, value) colour model are fuzzified using Gaussian and triangular membership functions but H is preserved. Objective measures like contrast information, quality and visual factor (Vf) are defined to represent the image characteristics. A new entropy function and Vf are used to form the objective function which is optimised using particle swarm optimisation to learn the image-specific parameters required for the enhancement. These new measures defined here serve to evaluate the performance of the proposed algorithm. In addition to preserving the colour and specific image features, the subjective and objective evaluations clearly depict the improvement in the quality of both the underexposed and overexposed images. On comparison, it is found that t...

Underexposed and overexposed colour image enhancement using information set theory

We suggested a Fuzzy image enhancement technique using Type-II to improvise visibly weather-degraded fruit images. The acquired fruit picture is divided into dark and light regions with Fuzzy partitioned. Fuzzy-sure entropy was used to choose the optimum visibility enhancement threshold values. Compared to the ATCE, BPDHE, GC and CLAHE image enhancement frameworks, the proposed scheme offers improved image quality. Experimental findings show that the approach in terms of visual and quantitative analysis have a lower percentage of saturated pixels, better output of visible edges and color gradients.

Fruit images Visibility enhancement using Type-II Fuzzy

This paper presents a new approach to enhance the contrast of color images. The intensity component of Hue, Saturation and Intensity (HSV) color model is fuzzified using Global intensification operator (GINT). A new objective measure called contrast information factor is introduced which is optimized using particle swarm optimization technique to learn the parameters. The enhanced image is evaluated by its entropy, index of fuzziness, contrast information and visual quality factor. Subjective and objective evaluation results clearly show the improvement in the quality of the underexposed images in addition of preserving color and specific image features. Also the shape of the histogram is preserved. The results are also compared with histogram equalization technique.

A novel optimal fuzzy color image enhancement using particle swarm optimization

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A novel optimal fuzzy color image enhancement using particle swarm optimization