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This paper presents a novel context-based histogram bin stretching (CHBS) method, which enhances the contrast by increasing the range of gray levels and randomness among the gray levels. It comprises image spatial contextual information and discrete cosine transform (DCT). It constitutes the global enhancement with the CHBS and local details with the DCT transform. First, it uses the spatial similarities among surrounding pixels to generate random numbers. Unlike the other methods, the similarity map is generated based on the neighboring pixels' mutual relationship. Intensity values are distributed among the available dynamic range (DR) to generate a global contrast-enhanced image. Second, the DCT is further applied to the previous contrast-enhanced image to adjust its local details automatically. Several experiments are conducted on the different levels of contrast degraded images. Both subjective and objective assessment outcomes validate that the projected approach is better or comparable with several state-of-the-art approaches in terms of brightness preservation, richer details, and natural appearance.

This paper proposes a novel method of image enhancement via energy curve equalization (ECE). Histogram equalization (HE) appears to be the most straightforward method for image contrast enhancement. Many modifications are already recommended to overcome the confines of HE. The computation of the histogram does not consider the spatial correlations among the surrounding pixels. The current method utilizes the energy curve as a substitute for the histogram of an image. In contrast to the histogram, this approach covers the spatial context information of neighboring pixels. The modified Hopfield neural network (HNN) architecture is employed to compute an image's energy curve. It has peaks and valleys and appears smoother than the image histogram. The average value of the mean and median of the energy curve is used as the plateau limit to control the enhancement rate. The clipped energy curve is partitioned into three individual regions based on the standard deviation value. The final transformation function is utilized to remap the pixel intensity values and is formulated by integrating the individual portions of equalized energy curves. The simulation results confirm the projected technique's effectiveness compared to conventional HE-based methods with and without plateau limit.

This paper presents an adaptive enhancement framework for low contrast images to improve the deteriorated details and color information. The proposed algorithm is composed of two adaptive steps: Contrast enhancement and Color restoration. Firstly, according to the luminance map separated from the RGB image, a new spatial contextual similarity histogram (SCSH) is developed. The new SCSH considers the gray-level similarities among the adjacent pixels, following the standard deviation value of the luminance image. The contrast-enhanced image is obtained by applying the intensity transformation function derived from the cumulative distribution function obtained from the normalized values of SCSH. An adaptive weighted fusion is carried out between the luminance enhanced channel and original luminance channel with the weights computed from the enhanced luminance channel. Secondly, an adaptive color restoration technique is designed to reconstruct the more consistent color image using original chromatic information and modified luminance. The results are analyzed using the statistical paired t-test. Both objective and subjective experimentation prove the supremacy of the proposed algorithm with conventional and state-of-the-art algorithms.

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.

Direction of arrival (DOA) estimation from sparse signal representation has gained much attention in recent years. In this, the spatial signal is reconstructed by using a Compressive sensing (CS) f...

Low light image enhancement algorithms intent to produce visually pleasant images and target to extract valuable information for computer vision applications. The task of improving the quality of low light images is a challenging one. The existing methods for quality improvement undeniably annoy the visual aesthetics and suffer the major drawback of high computational complexity and less efficiency. To improve the visual quality and lower the distortions, a simple and computationally efficient low light image enhancement framework is presented in this study. To achieve this, an adaptive sigmoid transfer function (ASTF) is used and is derived from the sigmoid activation function of neural networks. By combining ASTF with a Laplacian operator, colour and contrast-enhanced images are obtained. Experiments show the effectiveness of the proposed method with state-of-the-art methods.

In this paper, a new context-based image contrast enhancement process using energy curve equalization (ECE) with a clipping limit has been proposed. In a fundamental anomaly to the existing contrast enhancement practice using histogram equalization, the projected method uses the energy curve. The computation of the energy curve utilizes a modified Hopfield neural network architecture. This process embraces the image’s spatial adjacency information to the energy curve. For each intensity level, the energy value is calculated and the overall energy curve appears to be smoother than the histogram. A clipping limit applies to evade the over enhancement and is chosen as the average of the mean and median value. The clipped energy curve is subdivided into three regions based on the standard deviation value. Each part of the subdivided energy curve is equalized individually, and the final enhanced image is produced by combining transfer functions computed by the equalization process. The projected scheme’s qualitative and quantitative efficiency is assessed by comparing it with the conventional histogram equalization techniques with and without the clipping limit.

This paper proposes a novel optimized histogram modification framework using a cuckoo search (CS) algorithm for brightness preserved contrast enhancement. The histogram modification has been modeled as an optimization problem and the CS algorithm is used to find an optimum solution. The proposed method's chief objective is to find a fair tradeoff between contrast improvement and mean-brightness preservation. A new target histogram has been generated using the CS algorithm using a target function composed of tuning parameters. The extent of the image contrast range can be adjusted by selecting appropriately designed tuning parameters. The proposed technique can control the enhancement with the values chosen to fix the enhancement level and retain the enhanced image features. Finally, the optimized histogram generated from the CS algorithm is employed in the histogram equalization (HE) process to generate the final enhanced image. Its performance is validated with qualitative and quantitative assessment against well-known state-of-the-art techniques.

Existent induction cooking application requires high frequency and high efficiency multiple load resonant inverter configuration with independent control that suits for multiple vessels. In this paper, a multiple frequency resonant inverter is proposed for induction cooking application suitable for different material vessels. The proposed inverter is using half bridge configuration and is supplying the loads with power as that of full bridge circuit using a buck boost converter. Pulse Frequency control (PFC) technique is used for output power control. This configuration offers advantages like reduced component count and also improved efficiency. It also provides independent control. This new configuration is simulated with two different vessels and the simulation results prove the benefits of this converter.

During the production of a speech, the instant of significant excitation’s are called epochs. In speech processing, epochs plays a significant role and used in many applications. Accurate detection of epochs from the speech is a challenging task due to time varying nature of the vocal-tract system and excitation source. To detect the epochs from the speech signal several algorithms are already proposed. Zero Frequency Filter (ZFF) approach is one among the different techniques which gives better performance. This method is based on the impulse nature of the excitation source and not affected by the vocal-tract system characteristics. The original filter design of ZFF realized as Infinite Impulse Response (IIR) filter followed by two detrenders. Due to the unstable nature of IIR filter, later the ZFF is realized as the Zero-Band Filter (ZBF). In this paper, we have designed the hardware architectures for IIR and ZBF realization of ZFF. The hardware architectures of ZFF are verified by implementing it on FPGA (ZedBoard Zynq Evaluation and Development Kit xc7z020clg4841) using Xilinx system generator-2016.2.

Compressive sensing is a new method of signal acquisition and reconstruction through which one can greatly reduce the cost of processing, transmission and storage requirements as compared with the conventional sampling rates. This facilitates the accurate reconstruction of the signals even at sub-Nyquist rates. The role of measurement matrix is indispensable in loyal reconstruction. If the measurement matrix is more obtuse then it takes large computational time for signal reconstruction. This paper mainly focuses on different measurement matrices which are used in compressive sensing. The performance of these measurement matrices for compression and reconstruction of 4 GHz Gaussian modulated sinusoidal pulse are compared in this paper.

An expeditious β‐polynitration of 5,10,15,20‐tetrakis(2′,6′‐dichlorophenyl)porphyrin, its Zn and Cu derivatives with montmorillonite K10‐HNO3 and montmorillonite K10‐Cu(NO3)2 (claycop) under mild conditions using microwave irradiation in 72–95% yields has been described.

The reaction of different aryl halides with aryl oxides catalyzed by CuCl in 1-butyl-3-methyl-imidazolium tetrafluoroborate [bmim][BF4] and 1-butyl-3-methyl-imidazolium hexafluorophosphate [bmim][PF6] ionic liquids to give diaryl ethers in 54–90% yields has been studied under mild reaction conditions.

An efficient procedure for catalyst solubility, recycling and easy product isolation in oxidation of thiols to disulfides with molecular oxygen catalyzed by cobalt(II) phthalocyanines dissolved in ionic liquid at room temperature is reported.

The reaction of different aryl halides with aryl oxides catalyzed by CuCl in 1-butyl-3-methyl-imidazolium tetrafluoroborate [bmim][BF4] and 1-butyl-3-methyl-imidazolium hexafluorophosphate [bmim][PF6] ionic liquids to give diaryl ethers in 54–90% yields has been studied under mild reaction conditions.