Wavelet-based contrast enhancement of dark images using dynamic stochastic resonance
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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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.
25 citations
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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.
20 citations
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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.
18 citations
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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.
18 citations
Cites methods from "Wavelet-based contrast enhancement ..."
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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.
12 citations
References
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TL;DR: In this paper, it was shown that a dynamical system subject to both periodic forcing and random perturbation may show a resonance (peak in the power spectrum) which is absent when either the forcing or the perturbations is absent.
Abstract: It is shown that a dynamical system subject to both periodic forcing and random perturbation may show a resonance (peak in the power spectrum) which is absent when either the forcing or the perturbation is absent.
2,561 citations
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TL;DR: This paper extends a previously designed single-scale center/surround retinex to a multiscale version that achieves simultaneous dynamic range compression/color consistency/lightness rendition and defines a method of color restoration that corrects for this deficiency at the cost of a modest dilution in color consistency.
Abstract: Direct observation and recorded color images of the same scenes are often strikingly different because human visual perception computes the conscious representation with vivid color and detail in shadows, and with resistance to spectral shifts in the scene illuminant. A computation for color images that approaches fidelity to scene observation must combine dynamic range compression, color consistency-a computational analog for human vision color constancy-and color and lightness tonal rendition. In this paper, we extend a previously designed single-scale center/surround retinex to a multiscale version that achieves simultaneous dynamic range compression/color consistency/lightness rendition. This extension fails to produce good color rendition for a class of images that contain violations of the gray-world assumption implicit to the theoretical foundation of the retinex. Therefore, we define a method of color restoration that corrects for this deficiency at the cost of a modest dilution in color consistency. Extensive testing of the multiscale retinex with color restoration on several test scenes and over a hundred images did not reveal any pathological behaviour.
1,921 citations
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TL;DR: A practical implementation of the retinex is defined without particular concern for its validity as a model for human lightness and color perception, and the trade-off between rendition and dynamic range compression that is governed by the surround space constant is described.
Abstract: The last version of Land's (1986) retinex model for human vision's lightness and color constancy has been implemented and tested in image processing experiments. Previous research has established the mathematical foundations of Land's retinex but has not subjected his lightness theory to extensive image processing experiments. We have sought to define a practical implementation of the retinex without particular concern for its validity as a model for human lightness and color perception. We describe the trade-off between rendition and dynamic range compression that is governed by the surround space constant. Further, unlike previous results, we find that the placement of the logarithmic function is important and produces best results when placed after the surround formation. Also unlike previous results, we find the best rendition for a "canonical" gain/offset applied after the retinex operation. Various functional forms for the retinex surround are evaluated, and a Gaussian form is found to perform better than the inverse square suggested by Land. Images that violate the gray world assumptions (implicit to this retinex) are investigated to provide insight into cases where this retinex fails to produce a good rendition.
1,439 citations
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10 Dec 2002
TL;DR: It is shown that Peak Signal-to-Noise Ratio (PSNR), which requires the reference images, is a poor indicator of subjective quality and tuning an NR measurement model towards PSNR is not an appropriate approach in designing NR quality metrics.
Abstract: Human observers can easily assess the quality of a distorted image without examining the original image as a reference. By contrast, designing objective No-Reference (NR) quality measurement algorithms is a very difficult task. Currently, NR quality assessment is feasible only when prior knowledge about the types of image distortion is available. This research aims to develop NR quality measurement algorithms for JPEG compressed images. First, we established a JPEG image database and subjective experiments were conducted on the database. We show that Peak Signal-to-Noise Ratio (PSNR), which requires the reference images, is a poor indicator of subjective quality. Therefore, tuning an NR measurement model towards PSNR is not an appropriate approach in designing NR quality metrics. Furthermore, we propose a computational and memory efficient NR quality assessment model for JPEG images. Subjective test results are used to train the model, which achieves good quality prediction performance.
884 citations
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