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Proceedings Article•DOI•

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

Rajlaxmi Chouhan1, Rajib Kumar Jha2, Prabir Kumar Biswas1•
Indian Institute of Technology Kharagpur1, Indian Institutes of Information Technology2
16 Dec 2012-pp 73
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 Article•DOI•
Iris tissue recognition based on GLDM feature extraction and hybrid MLPNN-ICA classifier

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Neda Ahmadi1, Gholamreza Akbarizadeh1•
Shahid Chamran University of Ahvaz1
01 Apr 2020-Neural Computing and Applications
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 Article•DOI•
Enhancement of dark images using dynamic stochastic resonance with anisotropic diffusion

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Nidhi Gupta1, Rajib Kumar Jha2•
Indian Institutes of Information Technology1, Indian Institute of Technology Patna2
01 Mar 2016-Journal of Electronic Imaging
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 Article•DOI•
Localized image enhancement

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Saumik Bhattacharya1, Sumana Gupta1, Venkatesh K. Subramanian1•
Indian Institute of Technology Kanpur1
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 Article•DOI•
A fast and efficient computer aided diagnostic system to detect tumor from brain magnetic resonance imaging

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Nidhi Gupta1, Pritee Khanna1•
Indian Institute of Information Technology, Design and Manufacturing, Jabalpur1
01 Jun 2015-International Journal of Imaging Systems and Technology
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 Article•DOI•
Dynamic stochastic resonance-based grayscale logo extraction in hybrid SVD-DCT domain

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Rajib Kumar Jha1, Rajlaxmi Chouhan2•
Indian Institute of Technology Patna1, Indian Institute of Technology Kharagpur2
01 May 2014-Journal of The Franklin Institute-engineering and Applied Mathematics
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 Article•DOI•
Enhancement of ultrasound images using stochastic resonance-based wavelet transform.

[...]

V. P. Subramanyam Rallabandi1•
National Brain Research Centre1
01 Jun 2008-Computerized Medical Imaging and Graphics
TL;DR: A technique using stochastic resonance (SR)-based wavelet transform for the enhancement of unclear diagnostic ultrasound images that enhances the edges more clearly and can also optimally enhance an image even if the image noise level is considerable.
Abstract: Ultrasound diagnostic imaging technique is used to visualize muscles and internal organs, their size, structures and possible pathologies or lesions. The limited soft tissue contrast of ultrasound may lead to problems in characterizing perivascular soft tissues. We develop a technique using stochastic resonance (SR)-based wavelet transform for the enhancement of unclear diagnostic ultrasound images. The proposed method enhances the edges more clearly. The advantages of this method are that it can simultaneously operate both as an enhancement process as well as a noise-reduction operation, and that the method can also optimally enhance an image even if the image noise level is considerable.

58 citations

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

  • ...Earlier application of stochastic resonance in contrast enhancement [13], [14] tested its applicability using externally added noise and selected parameters experimentally....

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  • ...Recently, other stochastic resonance-based techniques in wavelet and fourier domains for the enhancement of unclear diagnostic ultrasound and MRI images respectively have been reported [13], [14]....

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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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Journal Article•DOI•
Contrast enhancement of dark images using stochastic resonance

[...]

Rajib Kumar Jha1, Prabir Kumar Biswas2, B. N. Chatterji2•
Indian Institutes of Information Technology1, Indian Institute of Technology Kharagpur2
03 Apr 2012-Iet Image Processing
TL;DR: In this paper, two stochastic resonance (SR)-based techniques are introduced for enhancement of very low-contrast images, and an expression for optimum noise standard deviation σoptimum that maximises signal-to-noise ratio (SNR) is derived.
Abstract: Two stochastic resonance (SR)-based techniques are introduced for enhancement of very low-contrast images. In the proposed SR-based image enhancement technique-1, an expression for optimum threshold has been derived. Gaussian noise of increasing standard deviation has been added iteratively to the low-contrast image until the quality of enhanced image reaches maximum. A quantitative parameter ‘distribution separation measure (DSM)’ is used to measure the enhancement quality. In order to reduce the required number of iterations in the second enhancement technique the author's have derived an expression for optimum noise standard deviation σoptimum that maximises signal-to-noise ratio (SNR). Image enhancement is obtained by iterating only with few noise standard deviations around σoptimum. This reduces number of iterations drastically. Comparison with the existing methods shows the superiority of the proposed method.

47 citations

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

  • ...Techniques on suprathreshold stochastic resonance [5], [7] deal with noiseinduced contrast enhancement of dark images....

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Proceedings Article•DOI•
A SR-based radon transform to extract weak lines from noise images

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Qinghua Ye1, Raining Huang, Xinyi He, Chimhua Zhang•
Chinese Academy of Sciences1
24 Nov 2003
TL;DR: A SR-based Radon transform is presented, in which a bistable stochastic resonance structure is introduced into theRadon transform, which can easily extract weak lines from noise images and give applications in the bearing-time record and the LOFAR display.
Abstract: The Radon transform is able to transform two dimensional images with lines into a space of line parameters, where each line in the image will give a peak positioned at the corresponding line parameters. This has led to many line detection applications in image processing, computer vision and array processing. But when the lines are embedded in very strong noise background, using the Radon transform directly is not so effective. In this paper we present a SR-based Radon transform, in which a bistable stochastic resonance structure is introduced into the Radon transform. Using this kind of transform, we can easily extract weak lines from noise images. We also give applications in the bearing-time record and the LOFAR display.

46 citations

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

  • ...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]....

    [...]

Proceedings Article•DOI•
Image enhancement using stochastic resonance [sonar image processing applications]

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Qinghua Ye1, Haining Huang1, Chunhua Zhang1•
Chinese Academy of Sciences1
24 Oct 2004
TL;DR: The results show that the proposed method is suitable for noisy images with very low signal-to-noise ratio (SNR), when the texture of the object is not subtle, and the region where the object lies in is not too small compared to the minimal region coverage over which SR works.
Abstract: This paper presents an image enhancement method using stochastic resonance (SR) and provides two applications to sonar image processing., i.e. side-scan sonar image and bearing-time record. Simulated and real data are tested and the results show that the proposed method is suitable for noisy images with very low signal-to-noise ratio (SNR), when the texture of the object is not subtle, and the region where the object lies in is not too small compared to the minimal region coverage over which SR works. We also show that an additional amount of noise besides the noise of the image itself may be helpful in enhancing the image.

43 citations

Journal Article•DOI•
Image enhancement by the modified high-pass filtering approach

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Ching-Chung Yang1•
Tatung Institute of Commerce and Technology1
01 Nov 2009-Optik
TL;DR: This work demonstrates a brand-new method for image enhancement by using a modified high-pass filtering approach that selectively magnified some specific spatial frequencies by exaggerating the local visibility of an image.
Abstract: We demonstrate a brand-new method for image enhancement by using a modified high-pass filtering approach. Some specific spatial frequencies are selectively magnified by exaggerating the local visibility of an image. Then a high-pass filter is employed to adjust those critical frequencies. The enhanced final image has well-sharpened fine characteristics.

36 citations

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

  • ...Comparative analysis with non-SR-based techniques, like contrast-limited adaptive histogram equalization (CLAHE) [24], gamma correction (Gamma), single-scale retinex (Retinex) [9], multi-scale retinex (MSR) [8], and modi.ed high­pass .ltering (MHPF) [21] has been performed....

    [...]

  • ...Comparative analysis with non-SR-based techniques, like contrast-limited adaptive histogram equalization (CLAHE) [24], gamma correction (Gamma), single-scale retinex (Retinex) [9], multi-scale retinex (MSR) [8], and modified highpass filtering (MHPF) [21] has been performed....

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  • ...4(a) Method F PQM CEF F PQM CEF F PQM CEF DWT-DSR 3.0 10.0 3.1 2.5 10.0 2.7 5.3 8.95 5.75 SVD-DSR 5.8 9.4 1.3 3.1 9.7 3.1 5.23 8.40 4.62 SSR 2.2 9.5 6.5 6.1 8.9 5.1 2.51 7.95 6.00 CLAHE 1.9 10.8 0.5 2.2 10.5 1.3 1.98 7.85 2.73 Photoshop 5.4 8.5 1.3 2.1 11.0 1.3 4.69 8.69 4.75 Gamma 9.5 8.5 11.5 1.2 10.9 1.5 5.92 6.92 5.01 Retinex 7.8 8.2 7.1 0.1 12.4 0.2 4.78 6.96 8.37 MSR 1.8 9.5 7.1 0.4 11.7 0.7 1.68 7.18 2.77 MHPF 8.4 8.2 16.8 0.6 11.7 0.8 5.02 9.01 7.21 MCE 1.0 12.2 0.2 1.1 8.8 1.0 1.18 8.77 0.96 MCE-DRC 0.7 11.9 0.2 0.9 11.1 1.0 0.97 9.01 7.21 CES 1.2 11.3 0.3 0.9 10.3 1.5 1.13 8.32 1.58 contrast enhancement of dark images using non-dynamic stochastic resonance....

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