Expeditious Contrast Enhancement for Grayscale Images Using a New Swift Algorithm

Zohair Al-Ameen

doi:10.19139/soic.v6i4.436

Abstract

Contrast enhancement plays a significant role in many existing image-related applications. In various situations, conventional contrast enhancement techniques failed to produce acceptable results for a wide variety of low-contrast images. As a result, various innovative techniques have been proposed for the purpose of contrast enhancement. Despite that, this field is still open for research due to its indispensability in many scientific disciplines and to various unavoidable real-world limitations. Hence, this article introduces a novel swift algorithm for contrast enhancement in images of low-contrast. The processing concept of this algorithm is straightforward. Initially, a non-complex logarithmic function is applied as a preprocessing step to attenuate the immoderate pixel values. Then, a new non-linear enhancement function which is designed experimentally based on mathematical, statistical and spatial information is applied to modify the brightness and contrast. Finally, a regularization function is applied as a post-processing step to rearrange the image pixels into their natural dynamic range. Experimental results revealed the favorability of the proposed algorithm, as it provided better results than those produced by several contemporary techniques in terms of recorded accuracy and perceived quality.

Highlights

In recent decades, the value of digital images has significantly increased since they become irreplaceable in many scientific disciplines
The proposed algorithm is compared to various prominent contrast enhancement techniques, such as brightness preserving dynamic fuzzy histogram equalization (BPDFHE) [32], non-parametric modified histogram equalization (NMHE) [33], median-mean based sub-image-clipped histogram equalization (MMSICHE) [34], recursive exposure based sub image histogram equalization (RESIHE) [35], and dominant orientation-based texture histogram equalization (DOTHE) [36]
The local contrast (LC) specifies the average of local image contrast, while the spatial frequency measure (SFM)

Summary

Introduction

The value of digital images has significantly increased since they become irreplaceable in many scientific disciplines. The authors of [15] provided another histogram equalization technique that utilizes specific penalty terms which helps in adjusting the contrast of a given image, while in [16], the authors developed a genetic-based technique that employs a new chromosome representation with the genetic operators, in which such operations can assist in remapping the gray-levels of the input image in a way that the output image has a better contrast representation. The authors of [19] provided a hybrid technique that combines the concepts of genetic algorithm, ant colony optimization and simulated annealing to achieve the desired enhancement, while in [20], the authors introduced a new technique that utilizes the concept of artificial bee colony to replace the input image set of gray-levels by a new set to produce results with better contrast.

Swift Algorithm

Results and Discussion

Eraser

Proposed algorithm

Conclusion