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Proceedings ArticleDOI

Wavelet-based contrast enhancement of dark images using dynamic stochastic resonance

TL;DR: The proposed dynamic stochastic resonance (DSR) technique has been proposed for contrast enhancement of dark and low contrast images in discrete wavelet transform (DWT) domain and is found to give noteworthy performance in terms of contrast enhancement, perceptual quality, as well as colorfulness.
Abstract: In this paper, a dynamic stochastic resonance (DSR)-based technique has been proposed for contrast enhancement of dark and low contrast images in discrete wavelet transform (DWT) domain. Traditionally, the performance of a stochastic resonance (SR)-based system is improved by addition of external noise. However, in the proposed DSR-based approach, the internal noise of an image has been utilized for the purpose of contrast enhancement. The degradation due to inadequate illumination is treated as noise, and is used to produce a noise-induced transition of the image from a low-contrast state to a high-contrast state. Stochastic resonance is induced in the approximation and detail coefficients in an iterative fashion, producing an increase in variance and mean of the coefficient distribution. Optimal output response is ensured by selection of optimal of bistable system parameters. An iterative algorithm is followed to achieve target value of performance metrics, such as relative contrast enhancement factor (F), perceptual quality measures (PQM), and color enhancement factor (CEF), at minimum iteration count. When compared with the existing SR-based and non SR-based enhancement techniques in spatial and frequency domains, the proposed technique is found to give noteworthy performance in terms of contrast enhancement, perceptual quality, as well as colorfulness.
Citations
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Journal ArticleDOI
TL;DR: A new method of feature extraction and classification based on gray-level difference method and hybrid MLPNN-ICA classifier is proposed, which is implemented on CASIA-Iris V3 dataset and UCI machine learning repository datasets.
Abstract: The use of iris tissue for identification is an accurate and reliable system for identifying people. This method consists of four main processing stages, namely segmentation, normalization, feature extraction, and matching. In this study, a new method of feature extraction and classification based on gray-level difference method and hybrid MLPNN-ICA classifier is proposed. For experimental results, our study is implemented on CASIA-Iris V3 dataset and UCI machine learning repository datasets.

40 citations

Journal ArticleDOI
TL;DR: The method increases mean and variance of the image by the optimum iterations on low coefficients of images, which improves contrast and brightness, respectively, and simultaneously, edges also become sharper.
Abstract: Image enhancement techniques are intended to improve the quality of an image without any kind of distortion or degradation. The literature is rich enough in this area, but there also exist some limitations. A technique is proposed for image enhancement by combining anisotropic diffusion with dynamic stochastic resonance in discrete wavelet transform domain. The method increases mean and variance of the image by the optimum iterations on low coefficients of images, which improves contrast and brightness, respectively, and simultaneously, edges also become sharper. It is well demonstrated by performing on various test images. Specifically, the adaptation and efficiency of the proposed technique for medical images are shown, because generally medical images appear contaminated with noise in terms of low illumination.

23 citations

Proceedings ArticleDOI
08 May 2014
TL;DR: This work proposes a fast algorithm to increase the contrast of an image locally using singular value decomposition (SVD) approach and attempts to define some parameters which can give clues related to the progress of the enhancement process.
Abstract: Image enhancement is a well established field in image processing. The main objective of image enhancement is to increase the perceptual information contained in an image for better representation using some intermediate steps, like, contrast enhancement, debluring, denoising etc. Among them, contrast enhancement is especially important as human eyes are more sensitive to luminance than the chrominance components of an image. Most of the contrast enhancement algorithms proposed till now are global methods. The major drawback of this global approach is that in practical scenarios, the contrast of an image does not deteriorate uniformly and the outputs of the enhancement techniques reach saturation at proper contrast points. That leads to information loss. In fact, to the best of our knowledge, no non-reference perceptual measure of image quality has yet been proposed to measure localized enhancement. We propose a fast algorithm to increase the contrast of an image locally using singular value decomposition (SVD) approach and attempt to define some parameters which can give clues related to the progress of the enhancement process.

21 citations


Cites background or methods from "Wavelet-based contrast enhancement ..."

  • ..., fail to preserve the color information of the image and thus an image processed with these techniques looks very synthetic [9], [10]....

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  • ...[13] R. K. Jha, R.Chouhan and P. K. Biswas, Noise-induced contrast enhancement of dark images using non-dynamic stochastic resonance, NCC’12, 2012, p. 1-5....

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  • ...have been defined such that the improvement can be measured using the processed image and the input image [9], [10], [18], [19]....

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  • ...2) Several perceptual measures have been proposed by different authors to calculate the quality of the enhanced images [9], [10], [12], [17]....

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  • ...For convenience, a brief description of the parameter selections of different techniques have been stated as follows: CLAHE has been implemented using the in-built function provided by MATLAB; gamma correction has been implemented by selecting the optimal value of gamma between 1 to 2, whichever gives the maximum F for globally degraded image and maximum ce for partially degraded images; multiscale retinex has been implemented using the MATLAB algorithm as given by Funt et al. [21]; NDSR has been developed using gaussian noise and standard deviation ranging 1− 20; DSR has been implemented using the optimal values for resonance as described by Jha and Chouhan [10]....

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Journal ArticleDOI
TL;DR: A simple and efficient CAD (computer‐aided diagnostic) system is proposed for tumor detection from brain magnetic resonance imaging (MRI) that is well adaptive and fast, and it is compared with well‐known existing techniques, like k‐mean, fuzzy c‐means, etc.
Abstract: In this work, a simple and efficient CAD computer-aided diagnostic system is proposed for tumor detection from brain magnetic resonance imaging MRI. Poor contrast MR images are preprocessed by using morphological operations and DSR dynamic stochastic resonance technique. The appropriate segmentation of MR images plays an important role in yielding the correct detection of tumor. On examination of three views of brain MRI, it was visible that the region of interest ROI lies in the middle and its size ranges from 240 × 240 mm2 to 280 × 280 mm2. The proposed system makes effective use of this information and identifies four blocks from the desired ROI through block-based segmentation. Texture and shape features are extracted for each block of all MRIs in the training set. The range of these feature values defines the threshold to distinguish tumorous and nontumorous MRIs. Features of each block of an MRI view are checked against the threshold. For a particular feature, if a block is found tumorous in a view, then the other views are also checked for the presence of tumor. If corresponding blocks in all the views are found to be tumorous, then the MRI is classified as tumorous. This selective block processing technique improves computational efficiency of the system. The proposed technique is well adaptive and fast, and it is compared with well-known existing techniques, like k-means, fuzzy c-means, etc. The performance analysis based on accuracy and precision parameters emphasizes the effectiveness and efficiency of the proposed work.

20 citations


Cites methods from "Wavelet-based contrast enhancement ..."

  • ...Enhancement: Image enhancement is done by using DSR (explained in the DSR section) in Discrete Wavelet Transform (DWT) domain (Chouhan et al., 2012)....

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Journal ArticleDOI
TL;DR: A novel dynamic stochastic resonance (DSR)-based technique for robust extraction of a grayscale logo from a tampered watermarked image and suggests that remarkable improvement of robustness is achieved by using DSR on singular values of DCT.
Abstract: This paper presents a novel dynamic stochastic resonance (DSR)-based technique for robust extraction of a grayscale logo from a tampered watermarked image. The watermark embedding is done on the singular values (SV) of the discrete cosine transform (DCT) coefficients of the cover image. DSR is then strategically applied during the logo extraction process where the SV of DCT coefficients are tuned following a double-well potential model by utilizing the noise introduced during attacks. The resilience of this technique has been tested in the presence of various noises, geometrical distortions, enhancement, compression, filtering and watermarking attacks. The proposed DSR-based technique for logo extraction gives noteworthy robustness without any significant trade-off in perceptual transparency of the watermarked image. A maximization approach has been adopted for the selection of bistable double-well parameters to establish noise-enhanced resonance. When compared with existing watermark extraction techniques based in SVD, DCT, SVD-DCT domains, as well as with their combination with DSR, the results suggest that remarkable improvement of robustness is achieved by using DSR on singular values of DCT.

15 citations

References
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Journal ArticleDOI
TL;DR: In this paper, it has been shown that a seeming resonance is actually caused by a noise-induced change in the effective stiffness and damping factor with respect to a signal, which leads to a non-monotonic variation of the output-signal amplitude as a function of noise intensity.
Abstract: Stochastic resonance in an overdamped oscillator is considered theoretically. It has been shown that a seeming resonance is actually caused by a noise-induced change in the effective stiffness and damping factor with respect to a signal. For a certain noise intensity, the effective stiffness is minimal, which leads to a nonmonotonic variation of the output-signal amplitude as a function of noise intensity. It is substantial that the position of the minimum of the effective stiffness and its value depend strongly on the signal frequency. The results are compared with similar processes for vibrational resonance. Considerable differences between these phenomena are indicated.

507 citations


"Wavelet-based contrast enhancement ..." refers methods in this paper

  • ...In the proposed technique, an analogy to Benzi’s double well model for recurrence of ice ages [1] has been presented in the wavelet domain....

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Journal ArticleDOI
TL;DR: The results of a psychophysics experiment show that the brain can consistently and quantitatively interpret detail in a stationary image obscured with time varying noise and that both the noise intensity and its temporal characteristics strongly determine the perceived image quality.
Abstract: Stochastic resonance can be used as a measuring tool to quantify the ability of the human brain to interpret noise contaminated visual patterns. Here we report the results of a psychophysics experiment which show that the brain can consistently and quantitatively interpret detail in a stationary image obscured with time varying noise and that both the noise intensity and its temporal characteristics strongly determine the perceived image quality.

470 citations


"Wavelet-based contrast enhancement ..." refers background in this paper

  • ...The first experimental work on visualization of stochastic resonance was reported by [16]....

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Journal ArticleDOI
TL;DR: An image enhancement algorithm for images compressed using the JPEG standard is presented, based on a contrast measure defined within the discrete cosine transform (DCT) domain that does not affect the compressibility of the original image.
Abstract: An image enhancement algorithm for images compressed using the JPEG standard is presented. The algorithm is based on a contrast measure defined within the discrete cosine transform (DCT) domain. The advantages of the psychophysically motivated algorithm are 1) the algorithm does not affect the compressibility of the original image because it enhances the images in the decompression stage and 2) the approach is characterized by low computational complexity. The proposed algorithm is applicable to any DCT-based image compression standard, such as JPEG, MPEG 2, and H. 261.

317 citations


"Wavelet-based contrast enhancement ..." refers background in this paper

  • ...Many algorithms available in literature have been designed for both colored and grayscale images in block DCT domain [2], [17], [11], [18]....

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Journal ArticleDOI
TL;DR: The proposed technique, computationally more efficient than the spatial domain based method, is found to provide better enhancement compared to other compressed domain based approaches.
Abstract: This paper presents a new technique for color enhancement in the compressed domain. The proposed technique is simple but more effective than some of the existing techniques reported earlier. The novelty lies in this case in its treatment of the chromatic components, while previous techniques treated only the luminance component. The results of all previous techniques along with that of the proposed one are compared with respect to those obtained by applying a spatial domain color enhancement technique that appears to provide very good enhancement. The proposed technique, computationally more efficient than the spatial domain based method, is found to provide better enhancement compared to other compressed domain based approaches.

238 citations


"Wavelet-based contrast enhancement ..." refers background or methods in this paper

  • ...If the image is colored, one would also interested to observe the quality in terms of colorfulness, and therefore, a metric for color enhancement factor (CEF ) [11] has been used....

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  • ...For evaluation of perceptual quality, we have used a no-reference metric for judging the image quality which we shall refer to as perceptual quality metric (PQM ) [19, 11], where for good perceptual quality, PQM should be close to 10....

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  • ...Many algorithms available in literature have been designed for both colored and grayscale images in block DCT domain [2], [17], [11], [18]....

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Journal ArticleDOI
TL;DR: It is shown that, far from being a drawback, the ubiquitous presence of random vibrations in vision systems operating from mobile devices can advantageously be used as a fundamental tool for edge detection.
Abstract: We show that, far from being a drawback, the ubiquitous presence of random vibrations in vision systems operating from mobile devices can advantageously be used as a fundamental tool for edge detection. Directly inspired by biology, the concept of dynamic retina uses the random spatiotemporal path, traced by a moving receptor that samples the image over time, as the basis for the edge detection operation. We propose a simple mathematical formalization of the dynamic retina concept that shows that the relevant information needed for edge detection is contained in the modulation of the variance of the output signal delivered by the retina. Based on a sequence of observations, we then use a variance estimator to determine the presence of the image edges. Following again a biological inspiration, more specifically focusing on neuron dynamics, we introduce a threshold type estimator and use its local asymptotic normality to optimize, via the Cramer-Rao relation, the value of the threshold. The optimal threshold value coincides with a maximum of the associated Fisher information and the overall process can therefore be directly interpreted as a stochastic resonance. We end our contribution by reporting some simple experimental illustrations.

141 citations


"Wavelet-based contrast enhancement ..." refers background or methods in this paper

  • ...The technique reported in [4] deals with edge detection using vibrating noise....

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  • ...Recently some of the works on application of stochastic resonance for grayscale image or edge enhancement that have been reported in literature are [22, 4, 23, 12, 13, 14, 15, 6]....

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