Search

This paper presents a novel image contrast enhancement technique that uses exposure-based energy curve equalization (ECE) with a plateau limit. In a primary deviation from the current histogram equalization process for contrast enhancement, the proposed approach uses an energy curve for the same. The energy curve is computed based on the modified Hopfield neural network architecture, which contains spatial context information. The calculated energy curve is clipped with a plateau limit computed as the average of the energy curve. The exposure threshold is computed and used to divide the clipped energy curve. The two resulting energy curves are equalized independently, and the final enhanced image is generated by integrating the images achieved by transforming the equalized energy curves. The performance of the proposed method is evaluated on a variety of low contrast images. The subjective and objective evaluations of the proposed method are compared with the various histogram equalization (HE) based methods and other state-of-the-art methods to exemplify the effectiveness.

To overcome the shortcomings of 1-D and 2-D Otsu’s thresholding methods, a 3-D Otsu method has been introduced. While yielding satisfactory segmentation results for images with a low signal-to-noise ratio (SNR) and poor contrast, it has the downside of high computational complexity. In this paper, the cuttlefish algorithm (CFA)-based 3-D Otsu thresholding method is proposed to pace up the conventional 3-D Otsu thresholding for color image segmentation. In order to decrease the effects of noises and weak edges, an optimally selected multilevel 3-D Otsu image thresholding technique is brought into the proposed segmentation scheme. The CFA is a newly developed stochastic meta-heuristic optimization algorithm based on observing, mimicking, and modeling the camouflaging feature of cuttlefish. It is used to simplify the problem of exhaustive search for the optimal threshold vector in 3-D space. Experimental results, when compared to 1-D Otsu, 1-D Otsu-Cuckoo search (CS) algorithm, 1-D Otsu-lightning search algorithm (LSA), 1-D Otsu-CFA, conventional 3-D Otsu, 3-D Otsu-CS, and 3-D Otsu-LSA, indicate that the proposed algorithm CFA-based 3-D Otsu thresholding is superior to all the other multilevel thresholding algorithms. The proposed 3-D-CFA method produces promising segmentation results from the objective and subjective aspects.