Dark and low-contrast image enhancement using dynamic stochastic resonance in discrete cosine transform domain
11 Nov 2013-Vol. 2, Iss: 1
TL;DR: A novel technique based on dynamic stochastic resonance in discrete cosine transform (DCT) domain has been proposed in this paper for the enhancement of dark as well as low-contrast images and gives remarkable performance in terms of contrast and color enhancement while ascertaining good perceptual quality.
Abstract: A novel technique based on dynamic stochastic resonance (DSR) in discrete cosine transform (DCT) domain has been proposed in this paper for the enhancement of dark as well as low-contrast images. In conventional DSR-based techniques, the performance of a system can be improved by addition of external noise. However, in the proposed DSR-based work, the intrinsic noise of an image has been utilized to create a noise-induced transition of a dark image to a state of good contrast. The proposed technique significantly enhances the image contrast and color information without losing any image or color data by optimization of bistable system parameters. The performance of the proposed methodology has been measured in terms of relative contrast enhancement factor, perceptual quality measure, and color enhancement factor. When compared with the existing enhancement techniques, such as adaptive histogram equalization, gamma correction, single-scale retinex, multi-scale retinex, modified high-pass filtering, multicontrast enhancement with dynamic range compression, color enhancement by scaling, edge-preserving multi-scale decomposition, automatic control of imaging tool, and various spatial/frequency-domain SR-based techniques, the proposed technique gives remarkable performance in terms of contrast and color enhancement while ascertaining good perceptual quality.
Citations
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TL;DR: The noise always plays a key role in different science and engineering applications, and here, the effect of the addition of external noise (i.e., stochastic resonance (SR) noise) in weak signal detection application is studied and the proposed detection technique is compared with the state-of-the-art techniques.
Abstract: The noise always plays a key role in different science and engineering applications. Here, we study the effect of the addition of external noise (i.e., stochastic resonance (SR) noise) in weak signal detection application. We also explore the conditions of improvability and non-improvability for a particular SR noise. We analyze both symmetric and asymmetric SR noises in our example. With certain equality and inequality constraints, we discuss the penalty function method which is used to design a single objective function. Furthermore, the particle swarm optimization technique has been used to maximize the probability of detection (
$$P_\mathrm{D}$$
) at a constant value of the probability of false alarm (
$$P_\mathrm{FA}$$
). With a numerical example, we have exhibited the performance of the proposed detector. We compare our proposed detection technique with the state-of-the-art techniques, and it is observed that the optimum $$P_\mathrm{D}$$
is comparable at a constant value of $$P_\mathrm{FA}$$
. The proposed detection technique is also used for watermark detection application to show the practicality of the proposed technique.
14Â citations
TL;DR: The proposed noise-enhanced iterative processing on Fourier coefficients for enhancement of low-contrast images has been found to give noteworthy performance for both low- Contrast and dark images among the SR-based techniques and is found to be better than most of the non-SR- based techniques, in terms of contrast enhancement, perceptual quality and colorfulness.
Abstract: This paper presents a study of noise-enhanced iterative processing on Fourier coefficients for enhancement of low-contrast images. The processing equation is derived from the concept of dynamic stochastic resonance (SR), where the presence of optimum amount of noise produces an improved performance in the system. Similar to our earlier works on SR-based contrast enhancement, noise in the current context is the internal noise inherent in an image due to insufficient illumination. Here, however, the parameter selection is done so as to achieve large noise suppression. Iteration is terminated when target performance has been achieved. It is observed that the increase in the variance of the Fourier magnitude distribution leads to an increase in the contrast of the image. The increase in the variance is analytically proven to be equivalent to the process of coefficient rooting. Comparison has been made with various state-of-the-art SR and non-SR-based techniques in spatial/frequency domains. The proposed technique has been found to give noteworthy performance for both low-contrast and dark images among the SR-based techniques. The performance is also found to be better than most of the non-SR-based techniques, in terms of contrast enhancement, perceptual quality and colorfulness.
12Â citations
18 Dec 2016
TL;DR: A classical method for retrieval of minute information from the high dynamic range image has been proposed based on variational calculus and dynamic stochastic resonance and it has been observed that the proposed technique is better or at most comparable to the existing techniques.
Abstract: While capturing pictures by a simple camera in a scene with the presence of harsh or strong lighting like a full sunny day, we often find loss of highlight detail information (overexposure) in the bright regions and loss of shadow detail information (underexposure) in dark regions. In this manuscript, a classical method for retrieval of minute information from the high dynamic range image has been proposed. Our technique is based on variational calculus and dynamic stochastic resonance (DSR). We use a regularizer function, which has been added in order to optimise the correct estimation of the lost details from the overexposed or underexposed region of the image. We suppress the dynamic range of the luminance image by attenuating large gradient with the large magnitude and low gradient with low magnitude. At the same time, dynamic stochastic resonance (DSR) has been used to improve the underexposed region of the image. The experimental results of our proposed technique are capable of enhancing the quality of images in both overexposed and underexposed regions. The proposed technique is compared with most of the state-of-the-art techniques and it has been observed that the proposed technique is better or at most comparable to the existing techniques.
10Â citations
Additional excerpts
...However, high dynamic range (HDR) images are preferred over standard low dynamic range images, and several applications have been demonstrated where such images are extremely useful....
[...]
TL;DR: Testing results show that this new algorithm can efficiently generate images with significantly improved effectiveness of enhancement and is thus potentially useful for real-time applications.
Abstract: Image enhancement is a problem of fundamental importance in the area of low level image processing. The goal of image enhancement is to significantly improve the visual effects of images or to obtain the fine details that are invisible in degraded images. In this paper, a new accurate image enhancement algorithm is developed to efficiently perform image enhancement with a dynamic programming approach. Specifically, an objective function is developed for the mappings between an original image and its enhanced versions to evaluate the effectiveness of enhancement. The objective function is then optimized by a dynamic programming algorithm to achieve the optimal enhancement effect. It is also shown that the computation efficiency of this dynamic programming algorithm can be significantly improved when certain conditions are satisfied. Testing results show that this new algorithm can efficiently generate images with significantly improved effectiveness of enhancement and is thus potentially useful for real-time applications. An implementation of the algorithm in MATLAB is freely available at the link: https://github.com/yinglei2020/YingleiSong
.
9Â citations
TL;DR: Chaos grey wolf optimizer is proposed to attain the optimized parameters of dynamic stochastic resonance in non-sub sampled shearlet transform domain (NSST) to enhance the low contrast satellite images.
6Â 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.
2,774Â citations
2,671Â citations
Book•
[...]
01 Jan 2008
TL;DR: In this article, a theoretical approach based on linear response theory (LRT) is described, and two new forms of stochastic resonance, predicted on the basis of LRT and subsequently observed in analogue electronic experiments, are described.
Abstract: Stochastic resonance (SR) - a counter-intuitive phenomenon in which the signal due to a weak periodic force in a nonlinear system can be {\it enhanced} by the addition of external noise - is reviewed A theoretical approach based on linear response theory (LRT) is described It is pointed out that, although the LRT theory of SR is by definition restricted to the small signal limit, it possesses substantial advantages in terms of simplicity, generality and predictive power The application of LRT to overdamped motion in a bistable potential, the most commonly studied form of SR, is outlined Two new forms of SR, predicted on the basis of LRT and subsequently observed in analogue electronic experiments, are described
2,403Â citations
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.
2,395Â citations
Book•
11 Sep 1989TL;DR: This text covers the principles and applications of "multidimensional" and "image" digital signal processing and is suitable for Sr/grad level courses in image processing in EE departments.
Abstract: New to P-H Signal Processing Series (Alan Oppenheim, Series Ed) this text covers the principles and applications of "multidimensional" and "image" digital signal processing. For Sr/grad level courses in image processing in EE departments.
2,022Â citations