PANKAJ KANDHWAY
@nitp.ac.in
NIT Patna
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Ashish Kumar Bhandari, Pankaj Kandhway, and Shubham Maurya
Institute of Electrical and Electronics Engineers (IEEE)
This article introduces a novel optimally selected plateau limit (PL)-based histogram modification framework. This approach preserves the brightness and improves the contrast of an image effectively without introducing absurd visual deterioration, unnatural contrast effects, and structural artifacts. It also enhances the weak illumination situations, such as backlighting effect and the nonuniform illumination of images without introducing any undesirable artifacts. The proposed method based on the subhistogram and clipping operations utilizes the PLs to modify the histogram of the image before applying the histogram equalization approach. The salp swarm algorithm (SSA)-based optimization technique is incorporated to compute the optimal PLs or adaptive weighted limits. To prove the efficiency of the proposed algorithm, a comparative study is done with the well-known histogram-based processing techniques and state-of-art methods in the literature. Furthermore, well-recognized different evaluation parameters are considered to compare the proposed framework with other existing methods.
Pankaj Kandhway and Ashish Kumar Bhandari
Springer Science and Business Media LLC
Image segmentation using multilevel thresholding (MT) is one of the leading methods. Although, as most techniques are based on the image histogram to be segmented, MT approaches only include the occurrence frequency of particular intensity range disregarding each spatial information. Energy curve-based contextual information can help to improve the quality of the thresholded image as it computes not only the value of the pixel but also its vicinity. Therefore, the energy curve is intended to carry spatial information into a curve with the same properties as the histogram. In this paper, classical Otsu’s method (between-class variance) is combined with energy curve for multilevel thresholding to perform segmentation of colored images. The energy curve-based Otsu (Energy-Otsu) uses an exhaustive search process to determine the optimal threshold values. However, due to the multidimensionality and multimodal nature of the color images, it becomes challenging and highly complex to obtain optimal thresholds. Therefore, the cuckoo search (CS) algorithm is coupled with Otsu thresholding criteria to perform MT over the energy curve. The proposed Energy-Otsu-CS method produces better-segmented results as compared to other well-known optimization algorithms such as differential evolution, particle swarm optimization, firefly algorithm, bacterial foraging optimization, and artificial bee colony algorithm. The proposed approach is examined intensively regarding quality, and the numerical parameter analysis is presented to compare the segmented results of the algorithms against closely related current approaches such as gray-level co-occurrence matrix and Renyi’ entropy-based thresholding approaches.
Pankaj Kandhway, Ashish Kumar Bhandari, and Anurag Singh
Elsevier BV
Abstract In this paper, a novel krill herd (KH) based optimized contrast and sharp edge enhancement framework is introduced for medical images. Plateau limit and fitness function are proposed in this paper to achieve the best-enhanced image. A new plateau limit is applied to clip the histogram using minimum, maximum, mean, and median of the histogram with a tunable parameter. The residue pixels are reallocated to the relative vacancy available on histogram bins. This method explores KH meta-heuristic algorithm to automatically adjust the tunable parameter based on a novel fitness function. Fitness function contains two different objective functions, which use edge, entropy, gray level co-occurrence matrix (GLCM) contrast, and GLCM energy of image for best visual, contrast enhancement and improved different characteristic information of the anatomical images. This method is compared with a different state of the art methods to check the viability and vigorous of the scheme and salp swarm algorithm (SSA) optimization is also used for the fair comparison of the proposed approach. The results show that the proposed framework is having superior performance compared to all the existing methods, both qualitatively and quantitatively, in terms of contrast, information content, edge details, and structure similarity.
Pankaj Kandhway, Ashish Kumar Bhandari, and Anurag Singh
Elsevier BV
Abstract In this paper, a novel krill herd (KH) based optimized contrast and sharp edge enhancement framework is introduced for medical images. Plateau limit and fitness function are proposed in this paper to achieve the best-enhanced image. A new plateau limit is applied to clip the histogram using minimum, maximum, mean, and median of the histogram with a tunable parameter. The residue pixels are reallocated to the relative vacancy available on histogram bins. This method explores KH meta-heuristic algorithm to automatically adjust the tunable parameter based on a novel fitness function. Fitness function contains two different objective functions, which use edge, entropy, gray level co-occurrence matrix (GLCM) contrast, and GLCM energy of image for best visual, contrast enhancement and improved different characteristic information of the anatomical images. This method is compared with a different state of the art methods to check the viability and vigorous of the scheme and salp swarm algorithm (SSA) optimization is also used for the fair comparison of the proposed approach. The results show that the proposed framework is having superior performance compared to all the existing methods, both qualitatively and quantitatively, in terms of contrast, information content, edge details, and structure similarity.
Pankaj Kandhway and Ashish Kumar Bhandari
Springer Science and Business Media LLC
In this paper, a new adaptive thresholding based sub-histogram equalization (ATSHE) scheme is proposed for contrast enhancement and brightness preservation with retention of basic image features. The histogram of an input image is divided into different sub-histogram using adaptive thresholding intensity values. The number of threshold values or sub-histograms of the image are not fixed, but depends on the peak signal-to-noise ratio (PSNR) of the thresholded image. Histogram clipping is also used here to control the undesired enhancement of resultant image thus avoiding over-enhancement. Median value of the original histogram gives the threshold value of clipping process. The main objective of proposed method is to improve contrast enhancement with preservation of mean brightness value, structural similarity index (SSIM) and information content of the images. Image contrast enhancement is examined by well-known enhancement assessment parameters such as contrast per pixel and modified measure of enhancement. The mean brightness preservation of the image is evaluated by using absolute mean brightness error value and feature preservation qualities are checked through SSIM and PSNR values. Through the proposed routine, the enhanced images achieve a good trade-off between features enhancement, low contrast boosting and brightness preservation in addition with the natural feel of the original image. In particular, the proposed ATSHE scheme due to its adaptive nature of threshold selection can successfully enhance images under oodles of weak illumination situations such as backlighting effects, non-uniform illumination low contrast and dark images.
Pankaj Kandhway and Ashish Kumar Bhandari
Springer Science and Business Media LLC
In this paper, a context-sensitive energy curve based cross-entropy method for multilevel color image segmentation is proposed. In thresholding approaches, pixels are arranged in various regions based on their intensity level. The main challenge generally faced in multilevel thresholding is the selection of best threshold values for the pixel division. However, the combination of the energy curve and the minimum cross entropy (Energy-MCE) scheme provides appropriate thresholds for a multilevel approach, but the computational cost for selecting optimal thresholds is high. Therefore, the selection of meta-heuristic optimization algorithms reduces this cost and generates optimal thresholds. A multi-verse optimizer (MVO) algorithm based on Energy-MCE thresholding approach is proposed to search the accurate and near-optimal thresholds for segmentation. Tests on natural images showed that the proposed method achieves better performance than the well-known optimization techniques in many challenging cases or images, such as identifying weak objects and revealing fine structures of complex objects while the added computational cost is minimal.
Pankaj Kandhway and Ashish Kumar Bhandari
Institution of Engineering and Technology (IET)
In this study, an image enhancement algorithm based on the modified histogram clipping scheme using a difference of histogram bins (MCDHB) has been proposed. The core idea of the proposed method is to ascertain the difference between the number of pixels’ in histogram bins of an input image and that of the traditional histogram equalised (HE) image. The calculated difference of each bin is partitioned into different blocks based on range criteria. The proposed algorithm can be attested as a global HE approach and mainly focuses on maintaining peaks in the histogram. The proposed MCDHB framework provides a good trade-off among contrast enhancement, shape of histogram, detailed information, and natural colour. Furthermore, the MCDHB framework is also incorporated with gamma correction for further improvement. The subjective and objective assessment confirms that both the proposed techniques can efficiently enhance the images, in a better way than those produced by classical techniques.
Pankaj Kandhway and Ashish Kumar Bhandari
Institution of Engineering and Technology (IET)
In this study, an image enhancement algorithm based on the modified histogram clipping scheme using a difference of histogram bins (MCDHB) has been proposed. The core idea of the proposed method is to ascertain the difference between the number of pixels’ in histogram bins of an input image and that of the traditional histogram equalised (HE) image. The calculated difference of each bin is partitioned into different blocks based on range criteria. The proposed algorithm can be attested as a global HE approach and mainly focuses on maintaining peaks in the histogram. The proposed MCDHB framework provides a good trade-off among contrast enhancement, shape of histogram, detailed information, and natural colour. Furthermore, the MCDHB framework is also incorporated with gamma correction for further improvement. The subjective and objective assessment confirms that both the proposed techniques can efficiently enhance the images, in a better way than those produced by classical techniques.
Pankaj Kandhway and Ashish Kumar Bhandari
Springer Science and Business Media LLC
AbstractIn this paper, a recently developed metaheuristic water cycle algorithm (WCA) is coupled with Masi entropy (Masi-WCA) to perform color image segmentation over the optimal threshold value selection process. Masi entropy gives the non-extensive/additive information that exists in an image by a tunable entropic parameter. The water cycle algorithm is a newly established population-based method which has been employed to exploit an optimal value of weighing factors for enforcement of constraints on individual components. The idea behind WCA is grounded on thought of water cycle and how streams and rivers flow downward toward the sea in the real world. The key feature of this paper is to exploit the modern optimization techniques such as water cycle algorithm, monarch butterfly optimization, grasshopper optimization algorithm, bat algorithm, particle swarm optimization, and wind-driven optimization for the color image segmentation purpose. In this paper, two objective (fitness) functions are exploited which are Tsallis and Masi entropy for a fair comparison of the proposed method. The proposed scheme is examined intensively regarding quality, and a statistical graph is included to compare the outcomes of the proposed Masi-WCA method against similar algorithms. Different to other recently developed optimization algorithms used for color image multilevel thresholding operations, WCA presents a better performance in terms of superior quality and fast convergence rate. Experimental evidence encourages the use of WCA for multilevel thresholding with Masi entropy, while it concludes that Tsallis entropy does not outperform over the proposed scheme.