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Rajlaxmi Chouhan

Bio: Rajlaxmi Chouhan is an academic researcher from Indian Institute of Technology Kharagpur. The author has contributed to research in topics: Stochastic resonance & Watermark. The author has an hindex of 10, co-authored 26 publications receiving 330 citations. Previous affiliations of Rajlaxmi Chouhan include Indian Institute of Information Technology, Design and Manufacturing, Jabalpur & Indian Institute of Technology Bombay.

Papers
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Journal ArticleDOI
TL;DR: 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.
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.

88 citations

Journal ArticleDOI
TL;DR: 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.
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.

59 citations

Proceedings ArticleDOI
01 Sep 2012
TL;DR: The proposed contrast enhancement technique using scaling of internal noise of a dark image in discrete cosine transform (DCT) domain adopts a local adaptive processing and significantly enhances the image contrast and color information while ascertaining good perceptual quality.
Abstract: A contrast enhancement technique using scaling of internal noise of a dark image in discrete cosine transform (DCT) domain has been proposed in this paper. The mechanism of enhancement is attributed to noise-induced transition of DCT coefficients from a poor state to an enhanced state. This transition is effected by the internal noise present due to lack of sufficient illumination and can be modeled by a general bistable system exhibiting dynamic stochastic resonance. The proposed technique adopts a local adaptive processing and significantly enhances the image contrast and color information while ascertaining good perceptual quality. When compared with the existing enhancement techniques such as adaptive histogram equalization, gamma correction, single-scale retinex, multi-scale retinex, modified high-pass filtering, multi-contrast enhancement, multi-contrast enhancement with dynamic range compression, color enhancement by scaling, edge-preserving multi-scale decomposition and automatic controls of popular imaging tool, the proposed technique gives remarkable performance in terms of relative contrast enhancement, colorfulness and visual quality of enhanced image.

34 citations

Journal ArticleDOI
TL;DR: The DWT-based DSR technique gives better performance in terms of visual information, color preservation and computational complexity of the enhancement process.
Abstract: In this paper, a dynamic stochastic resonance (DSR)-based technique in discrete wavelet transform (DWT) domain is presented for the enhancement of very dark grayscale and colored images. Generally in DSR, the performance of an input signal can be improved by addition of external noise. However in this paper, the intrinsic noise of an image has been utilized for the purpose of contrast enhancement. The DSR procedure iteratively tunes the DWT coefficients using bistable system parameters. The DSR-based technique significantly enhances the image without introducing any blocking, ringing or spot artifacts. The algorithm has been optimized and made adaptive. Performance of the given technique has been measured in terms of distribution separation measure (DSM), target-to- background enhancement measure based on standard deviation (TBEs) and target-to-background enhancement measure based on entropy (TBEe). When compared with the existing enhancement techniques such as histogram equalization, gamma correction, single-scale retinex, multi- scale retinex, modified high-pass filtering and Fourier-based DSR, the DWT-based DSR technique gives better performance in terms of visual information, color preservation and computational complexity of the enhancement process.

29 citations

Proceedings ArticleDOI
03 Apr 2012
TL;DR: In this paper, a nonlinear non-dynamic stochastic resonance-based technique has been proposed for enhancement of dark and low contrast images, where a low contrast image is treated as a sub-threshold signal and noise-enhanced signal processing is applied to improve its contrast.
Abstract: In this paper, a nonlinear non-dynamic stochastic resonance-based technique has been proposed for enhancement of dark and low contrast images. A low contrast image is treated as a subthreshold signal and noise-enhanced signal processing is applied to improve its contrast. The proposed technique uniquely utilizes addition of external noise to neutralize the effect of internal noise (due to insufficient illumination) of a low contrast image. Random noise is added repeatedly to an image and is successively hard-thresholded followed by overall averaging. By varying the noise intensities, noise induced resonance is obtained at a particular optimum noise intensity. Performance of the proposed technique has been investigated for four types of noise distributions - gaussian, uniform, poisson and gamma. Quantitative evaluation of their performances have been done in terms of contrast enhancement factor, color enhancement and perceptual quality measure. Comparison with other existing spatial domain techniques shows that the proposed technique gives remarkable enhancement while ascertaining good perceptual quality.

28 citations


Cited by
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Journal ArticleDOI
Yaguo Lei1, Zijian Qiao1, Xuefang Xu1, Jing Lin1, Shantao Niu1 
TL;DR: Wang et al. as discussed by the authors proposed an underdamped multistable stochastic resonance (SR) method with stable-state matching for bearing fault diagnosis, which is able to suppress the multiscale noise.

130 citations

Journal ArticleDOI
TL;DR: A low intricacy technique for contrast enhancement is proposed, and its performance is exhibited against various versions of histogram-based enhancement technique using three advanced image quality assessment metrics of Universal Image Quality Index (UIQI), Structural Similarity Index (SSIM), and Feature Similarity index (FSIM).
Abstract: Image contrast is an essential visual feature that determines whether an image is of good quality. In computed tomography (CT), captured images tend to be low contrast, which is a prevalent artifact that reduces the image quality and hampers the process of extracting its useful information. A common tactic to process such artifact is by using histogram-based techniques. However, although these techniques may improve the contrast for different grayscale imaging applications, the results are mostly unacceptable for CT images due to the presentation of various faults, noise amplification, excess brightness, and imperfect contrast. Therefore, an ameliorated version of the contrast-limited adaptive histogram equalization (CLAHE) is introduced in this article to provide a good brightness with decent contrast for CT images. The novel modification to the aforesaid technique is done by adding an initial phase of a normalized gamma correction function that helps in adjusting the gamma of the processed image to avoid the common errors of the basic CLAHE of the excess brightness and imperfect contrast it produces. The newly developed technique is tested with synthetic and real-degraded low-contrast CT images, in which it highly contributed in producing better quality results. Moreover, a low intricacy technique for contrast enhancement is proposed, and its performance is also exhibited against various versions of histogram-based enhancement technique using three advanced image quality assessment metrics of Universal Image Quality Index (UIQI), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM). Finally, the proposed technique provided acceptable results with no visible artifacts and outperformed all the comparable techniques.

86 citations

Journal ArticleDOI
TL;DR: The experimental results demonstrate that the proposed scheme is imperceptible and robust against a variety of intentional or unintentional attacks.

74 citations

Journal ArticleDOI
13 Aug 2014
TL;DR: This paper presents a systematic noise-enhanced information processing framework to analyze and optimize the performance of engineered systems and discusses the constructive effect of noise in associative memory recall.
Abstract: Noise, traditionally defined as an unwanted signal or disturbance, has been shown to play an important constructive role in many information processing systems and algorithms. This noise enhancement has been observed and employed in many physical, biological, and engineered systems. Indeed stochastic facilitation (SF) has been found critical for certain biological information functions such as detection of weak, subthreshold stimuli or suprathreshold signals through both experimental verification and analytical model simulations. In this paper, we present a systematic noise-enhanced information processing framework to analyze and optimize the performance of engineered systems. System performance is evaluated not only in terms of signal-to-noise ratio but also in terms of other more relevant metrics such as probability of error for signal detection or mean square error for parameter estimation. As an important new instance of SF, we also discuss the constructive effect of noise in associative memory recall. Potential enhancement of image processing systems via the addition of noise is discussed with important applications in biomedical image enhancement, image denoising, and classification.

66 citations

Journal ArticleDOI
TL;DR: An advanced adaptive and simple algorithm for dark medical image enhancement based on adaptive gamma correction using discrete wavelet transform with singular-value decomposition (DWT-SVD-AGC) is proposed and shows that it performs better than other state-of-the-art techniques.
Abstract: The performances of medical image processing techniques, in particular CT scans, are usually affected by poor contrast quality introduced by some medical imaging devices. This suggests the use of contrast enhancement methods as a solution to adjust the intensity distribution of the dark image. In this paper, an advanced adaptive and simple algorithm for dark medical image enhancement is proposed. This approach is principally based on adaptive gamma correction using discrete wavelet transform with singular-value decomposition (DWT-SVD). In a first step, the technique decomposes the input medical image into four frequency sub-bands by using DWT and then estimates the singular-value matrix of the low–low (LL) sub-band image. In a second step, an enhanced LL component is generated using an adequate correction factor and inverse singular value decomposition (SVD). In a third step, for an additional improvement of LL component, obtained LL sub-band image from SVD enhancement stage is classified into two main classes (low contrast and moderate contrast classes) based on their statistical information and therefore processed using an adaptive dynamic gamma correction function. In fact, an adaptive gamma correction factor is calculated for each image according to its class. Finally, the obtained LL sub-band image undergoes inverse DWT together with the unprocessed low–high (LH), high–low (HL), and high–high (HH) sub-bands for enhanced image generation. Different types of non-contrast CT medical images are considered for performance evaluation of the proposed contrast enhancement algorithm based on adaptive gamma correction using DWT-SVD (DWT-SVD-AGC). Results show that our proposed algorithm performs better than other state-of-the-art techniques.

60 citations