Rajib Kumar Jha

Bio: Rajib Kumar Jha is an academic researcher from Indian Institute of Technology Patna. The author has contributed to research in topics: Stochastic resonance & Watermark. The author has an hindex of 15, co-authored 85 publications receiving 854 citations. Previous affiliations of Rajib Kumar Jha include Indian Institute of Information Technology, Design and Manufacturing, Jabalpur & Indian Institutes of Technology.

Significant region based robust watermarking scheme in lifting wavelet transform domain

Abstract: It discusses the experimental flaw in Lin et al. (2009) and Run et al. (2011).Different secret keys and region based strategy helps the system to be more secure.Remarkably efficient especially in case of JPEG compression attack.Also provides more robustness against various signal processing operations.Shows noteworthy comparisons with currently existing techniques. With the aim of designing a more robust digital watermarking scheme against various unintentional and intentional attacks, a significant region (SR) based image watermarking technique is proposed in the present paper using lifting wavelet transform (LWT). While the energy compaction property of LWT provides higher tolerance against image distortion as opposed to conventional wavelet transform, the proposed block selection procedure provides greater security over the existing watermarking approaches. Non-overlapping coefficient blocks from the lowpass subband are selected after applying three levels of LWT and using certain criterion based on minimum coefficient difference and a threshold value. To disguise the intruder completely, secret key based randomization of coefficients, blocks, and watermark bits is incorporated. Maximum coefficients difference of each selected block and the same threshold value are then used for deciding which block to choose for embedding the bit 0 or 1. Performance of the proposed method is analyzed and compared with some of the existing schemes that demonstrates that the proposed scheme not only outperforms other methods with respect to various attacks for most of the cases, but also maintains a satisfactory image quality.

Enhancement of dark and low-contrast images using dynamic stochastic resonance

Abstract: In this study, a dynamic stochastic resonance (DSR)-based technique in spatial domain has been proposed for the enhancement of dark- and low-contrast images. Stochastic resonance (SR) is a phenomenon in which the performance of a system (low-contrast image) can be improved by addition of noise. However, in the proposed work, the internal noise of an image has been utilised to produce a noise-induced transition of a dark image from a state of low contrast to that of high contrast. DSR is applied in an iterative fashion by correlating the bistable system parameters of a double-well potential with the intensity values of a low-contrast image. Optimum output is ensured by adaptive computation of performance metrics - relative contrast enhancement factor ( F ), perceptual quality measures and colour enhancement factor. When compared with the existing enhancement techniques such as adaptive histogram equalisation, gamma correction, single-scale retinex, multi-scale retinex, modified high-pass filtering, edge-preserving multi-scale decomposition and automatic controls of popular imaging tools, the proposed technique gives significant performance in terms of contrast and colour enhancement as well as perceptual quality. Comparison with a spatial domain SR-based technique has also been illustrated.

Medical image encryption using fractional discrete cosine transform with chaotic function.

Abstract: In this advanced era, where we have high-speed connectivity, it is very imperative to insulate medical data from forgery and fraud. With the regular increment in the number of internet users, it is challenging to transmit the beefy medical data. This (medical data) is always reused for different diagnosis purposes, so the information of the medical images need to be protected. This paper introduces a new scheme to ensure the safety of the medical data, which includes the use of a chaotic map on the fractional discrete cosine transform (FrDCT) coefficients of the medical data/images. The imperative FrDCT provides a high degree of freedom for the encryption of the medical images. The algorithm consists of two significant steps, i.e., application of FrDCT on an image and after that chaotic map on FrDCT coefficients. The proposed algorithm discusses the benefits of FrDCT over fractional Fourier transform (FRFT) concerning fractional order α. The key sensitivity and space of the proposed algorithm for different medical images inspire us to make a platform for other researchers to work in this area. Experiments are conducted to study different parameters and challenges. The proposed method has been compared with state-of-the-art techniques. The results suggest that our technique outperforms many other state-of-the-art techniques. Graphical Abstract Overview of the proposed algorithm.

Noise-induced contrast enhancement using stochastic resonance on singular values

Abstract: In this paper, a dynamic stochastic resonance (DSR)-based technique in singular value domain for contrast enhancement of dark images has been presented. The internal noise due to the lack of illumination is utilized using a DSR iterative process to obtain enhancement in contrast, colorfulness as well as perceptual quality. DSR is a phenomenon that has been strategically induced and exploited and has been found to give remarkable response when applied on the singular values of a dark low-contrast image. When an image is represented as a summation of image layers comprising of eigen vectors and values, the singular values denote luminance information of each such image layer. By application of DSR on the singular values using the analogy of a bistable double-well potential model, each of the singular values is scaled to produce an image with enhanced contrast as well as visual quality. When compared with performance of some existing spatial domain enhancement techniques, the proposed DSR-SVD technique is found to give noteworthy better performance in terms of contrast enhancement factor, color enhancement factor and perceptual quality measure.

Improved watermarking technique based on significant difference of lifting wavelet coefficients

Abstract: In this paper, a blind watermarking scheme based on significant difference of lifting wavelet transform coefficients has been proposed. The difference between two maximum coefficients in a block is called as significant difference. Embedding of binary watermark has been done based on the largest coefficient of randomly shuffled blocks of CH3 sub-band. This sub-band is quantized using the predefined threshold value by comparing the significant difference value with the average of significant difference value of all blocks. The watermarked image shows no perceptual degradation as the PSNR value exceeds 42 dB. An adaptive-thresholding-based method is used for watermark extraction. In the proposed technique, the benefit of using lifting wavelet over traditional wavelet is the maximum energy compaction property, which helps in resisting different attacks. The simulation results show higher performance of the proposed technique as compared to the similar existing techniques under different geometric and nongeometric attacks such as amplification, median filtering, sharpening, scaling, rotation, Gaussian noise, salt and paper noise, Gaussian filter and JPEG compression.

Singular Value Decomposition for Genome-Wide Expression Data Processing and Modeling

Abstract: ‡We describe the use of singular value decomposition in transforming genome-wide expression data from genes 3 arrays space to reduced diagonalized ‘‘eigengenes’’ 3 ‘‘eigenarrays’’ space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.

Two Dimensional Signal And Image Processing

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Applications of stochastic resonance to machinery fault detection: A review and tutorial

Abstract: Fault detection is a key tool to ensure the safety and reliability of machinery. In machinery fault detection, signal processing methods are extensively applied to extract fault characteristics. Widely used signal processing methods attempt to eliminate the noise imbedded in signals for discovering fault characteristics. Different from widely used signal processing methods, stochastic resonance (SR) is able to utilize the noise imbedded in signals to extract weak fault characteristics from the signals. Therefore, it has been extensively applied to fault characteristic extraction and machinery fault detection. Up to now, massive literature on the applications of SR to machinery fault detection has been published in academic journals, conference proceedings, etc. This paper attempts to survey and summarize the current progress of SR applied in machinery fault detection, providing comprehensive references for researchers concerning with the subject and further helping them identify future trends for research. First, this paper elaborates SR from its original mechanism to fundamental theory. Then, the literature on machinery fault detection using SR is reviewed in terms of critical rotary components prone to faults, such as rolling element bearings, gears and rotors. Moreover, a tutorial on how to use SR for machinery fault detection is provided. What’s more, the key issues and prospects of SR in machinery fault detection are pointed out and discussed. It is expected that this review would inspire researchers to explore the potential of SR as well as develop advanced research in this field.

An underdamped stochastic resonance method with stable-state matching for incipient fault diagnosis of rolling element bearings

Abstract: Most traditional overdamped monostable, bistable and even tristable stochastic resonance (SR) methods have three shortcomings in weak characteristic extraction: (1) their potential structures characterized by single stable-state type are insufficient to match with the complicated and diverse mechanical vibration signals; (2) they vulnerably suffer the interference from multiscale noise and largely depend on the help of highpass filters whose parameters are selected subjectively, probably resulting in false detection; and (3) their rescaling factors are fixed as constants generally, thereby ignoring the synergistic effect among vibration signals, potential structures and rescaling factors. These three shortcomings have limited the enhancement ability of SR. To explore the SR potential, this paper initially investigates the SR in a multistable system by calculating its output spectral amplification, further analyzes its output frequency response numerically, then examines the effect of both damping and rescaling factors on output responses and finally presents a promising underdamped SR method with stable-state matching for incipient bearing fault diagnosis. This method has three advantages: (1) the diversity of stable-state types in a multistable potential makes it easy to match with various vibration signals; (2) the underdamped multistable SR, equivalent to a moving nonlinear bandpass filter that is dependent on the rescaling factors, is able to suppress the multiscale noise; and (3) the synergistic effect among vibration signals, potential structures and rescaling and damping factors is achieved using quantum genetic algorithms whose fitness functions are new weighted signal-to-noise ratio (WSNR) instead of SNR. Therefore, the proposed method is expected to possess good enhancement ability. Simulated and experimental data of rolling element bearings demonstrate its effectiveness. The comparison results show that the proposed method is able to obtain higher amplitude at target frequency and larger output WSNR, and performs better than traditional SR methods.