Efficient Color Image Segmentation of Low Density Range Image Using RCAB-RDMCNN Enhancement Technique and RBSHM Segmentation Algorithm

Color image segmentation is the primary step to elicit detailed information about the image. However, the segmentation of images under low dynamic ranges is extremely difficult. So the work has proposed an efficient color image segmentation of low-density range image using RCAB-RDMCNN enhancement technique and RBSHM segmentation algorithm. The proposed framework efficiently segments the LDR images by undergoing the following steps. Initially, the RGB formats of the LDR images are converted into HSV format. Then, the brighter as well as the darker regions from the images are segmented by using the AMOT algorithm. After that, the contrasts of the image are enhanced by using RCAB-RDMCNN. Finally, the image segmentation is performed by means of the RBSHM. The publically available LDR images are used in this research. And the results obtained by the proposed method are compared with the previous state-of-art algorithms. The experimental outcomes indicated that the proposed method segments the LDR images with the highest accuracy rate and outperforms the other state-of-the-art methods.

Automated summary

This study proposes an efficient color image segmentation method for low-density range images using the RCAB-RDMCNN enhancement technique and RBSHM segmentation algorithm. The proposed method converts LDR images into HSV format, segments the brighter and darker regions using the AMOT algorithm, enhances the image contrast using RCAB-RDMCNN, and performs image segmentation using RBSHM. Experimental results show that the proposed method achieves the highest accuracy rate and outperforms other state-of-the-art methods.