Dynamic Clip Limit Window Size Histogram Equalization for Poor Information Images

Abstract

During the image enhancement process, hidden information, poor capturing device quality lead to poor image contrast, insufficient user experience, and an inappropriate data collecting environment setting have all been noted as serious concerns. Histogram equalization techniques have been used to solve the challenges described above. Nonetheless, the images obtained using these methods are frequently impacted by unwanted artifacts, and unnatural appearances effects. Due to that, this research presented a novel strategy for enhancing contrast called Dynamic Clip Limit Window Size Histogram Equalization. The proposed technique uses a new fitness function of combining the discrete entropy and root mean square error parameters. The proposed strategy's qualitative and quantitative result is validated and assessed against 5 state-of-the-art strategies (HSQHE, ACLTSHE, CLAHE, AEIHE, and IAECHE). The proposed technique proved its high capability to produce the best average DE, CII, and SSI values for the Faces-199 dataset (7.869, 1.000, and 0.999, respectively).

Optimization Contrast Enhancement and Noise Reduction for Semantic Segmentation of Oil Palm Aerial Imagery

“…Quality data supply supports the process of semantic segmentation in producing map images. Enhancement aims to improve image quality during pre-processing, such as segmentation tasks, feature extraction, and image analysis [1,2], and make it more informative [3]. The purpose of optimization during pre-processing is to maintain the image's edges, structure, and detail [4,5].…”

“…International Journal of Intelligent Engineering and Systems, Vol. 16 The histogram equalization (HE) technique has so far been used to overcome previous challenges in aerial imagery problems, but with this technique, it is still necessary to increase image artifacts [1]. HE is the simplest method to adjust or enhance color contrast globally [8] and update the histogram pixel intensity distribution.…”

“…The results of the correction help improve the intensity of pixels in aerial images; thus helping pixels predict their class in the segmentation process. The proposed enhancement method will be compared with the results of segmentation accuracy with other state-of-the-art methods, such as the contrast enhancement primer method with HE and CLAHE [17], noise reduction with MB [18], and contrast enhancement with dynamic clip-limit histogram equation boundary window size (DCWHE) [1], and adaptive gamma correction with weighted histogram distribution (AGCWHD) [19]. The segmentation process used in this experiment is with VGG-16 and VGG-19 on Keras.…”