Hybrid Domain Analysis of Noise-Aided Contrast Enhancement Using Stochastic Resonance
TL;DR: It can be inferred from comparative analysis with respect to other conventional methods that while the algorithm is observed to work well in all three hybrid domains, the SV-DCT domain performs better in terms of iteration count, while DCT-DWT is found to outperform others in Terms of perceptual quality.
Abstract: This paper aims to present an analysis of a noise-aided contrast enhancement algorithm in hybrid transform domains. The performance of our earlier noise-enhanced iterative algorithm, formulated from the motion dynamics of a double-well system exhibiting dynamic stochastic resonance, has been investigated here on hybrid coefficients, viz. singular values (SVs) of wavelet coefficients, SVs of discrete cosine transform (DCT) coefficients, and DCT of wavelet coefficients, of a dark image. The performance of the algorithm is gauged using metrics indicating relative contrast enhancement and perceptual quality. Colorfulness, subjective visual scores and logarithmic contrast metrics for outputs are also observed. Experimental results display noteworthy enhancement of contrast on both natural and synthetically-darkened images. It can be inferred from comparative analysis with respect to other conventional methods that while the algorithm is observed to work well in all three hybrid domains, the SV-DCT domain performs better in terms of iteration count, while DCT-DWT is found to outperform others in terms of perceptual quality.
Citations
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TL;DR: In this article , an image enhancement method based on Grunwald-Letnikov, Riemann-Liouville fractional-order derivatives and genetic algorithm was proposed to boost the homomorphic filtering performance.
Abstract: The main aim of image enhancement is to improve the visual quality or appearance of an image. This article presents an image enhancement method based on Grunwald-Letnikov, Riemann-Liouville fractional-order derivatives and genetic algorithm to boost the homomorphic filtering performance. Homomorphic filtering is used to attenuate the contribution made by the illumination and amplify the reflectance components of an image. This work uses a fractional-order derivative to enhance the mid- and high-frequencies and preserve the low-frequencies. The enhancement of the image depends on the parameters required for the homomorphic filter function and fractional-order value, which are not the same for all types of images. Hence, the genetic algorithm is applied, which automatically determines these parameters by optimizing the fitness function. The capability of the proposed approach is evaluated using performance metrics such as information entropy, average gradient, and contrast improvement index on different sizes of images. An average improvement in information entropy of 6.5%, average gradient of 52%, and contrast improvement index of 75%, respectively, are achieved for standard, medical images and images with low contrast and non-uniform illumination conditions. Also, the proposed method outperforms the existing methods by producing a better visual appearance of the image.
4 citations
TL;DR: In this paper , a turbid underwater image enhancement method based on parameter-tuned stochastic resonance (SR) is proposed to address the attenuation and scattering of light caused by scatterers.
Abstract: In turbid water, the attenuation and scattering of light caused by scatterers make underwater optical images degraded, blurred, and contrast reduced, limiting the extraction and analysis of information from images. To address such problems, a turbid underwater image enhancement method based on parameter-tuned stochastic resonance (SR) is proposed in this article. First, an SR algorithm framework for underwater image enhancement is constructed, including the dimensionality reduction and normalization of input images, the solution and parameter optimization of the SR system, the dimensionality upgrading of output images, etc. This framework can apply the SR's ability to enhance weak signals to the enhancement of turbid underwater images. Second, to measure the performance of the system, a synthetic turbid underwater image data set (UWCHIC) is constructed using the underwater imaging model and an image set with simulated scatterers. Based on this data set, the relationship between various image quality evaluation metrics and system parameters is analyzed, and then the suitable no-reference (NR) metrics for system performance evaluation are selected and an adaptive parameter tuning strategy of the SR system is proposed to guide the image enhancement. Lastly, the proposed method is evaluated on the UWCHIC, a dataset to evaluate underwater image restoration methods (TURBID), marine underwater environment database (MUED), and underwater image enhancement benchmark (UIEB) data sets and the turbid underwater images captured from natural waters. Different experimental evaluations demonstrated that the proposed method not only effectively enhances the visual quality of turbid underwater images but also improves the performance of downstream vision tasks.
2 citations
DOI•
TL;DR: In this article , a turbid underwater image enhancement method based on parameter-tuned stochastic resonance (SR) is proposed to address the attenuation and scattering of light caused by scatterers, which makes underwater optical images degraded, blurred, and contrast reduced, limiting the extraction and analysis of information from images.
Abstract: In turbid water, the attenuation and scattering of light caused by scatterers make underwater optical images degraded, blurred, and contrast reduced, limiting the extraction and analysis of information from images. To address such problems, a turbid underwater image enhancement method based on parameter-tuned stochastic resonance (SR) is proposed in this article. First, an SR algorithm framework for underwater image enhancement is constructed, including the dimensionality reduction and normalization of input images, the solution and parameter optimization of the SR system, the dimensionality upgrading of output images, etc. This framework can apply the SR's ability to enhance weak signals to the enhancement of turbid underwater images. Second, to measure the performance of the system, a synthetic turbid underwater image data set (UWCHIC) is constructed using the underwater imaging model and an image set with simulated scatterers. Based on this data set, the relationship between various image quality evaluation metrics and system parameters is analyzed, and then the suitable no-reference (NR) metrics for system performance evaluation are selected and an adaptive parameter tuning strategy of the SR system is proposed to guide the image enhancement. Lastly, the proposed method is evaluated on the UWCHIC, a dataset to evaluate underwater image restoration methods (TURBID), marine underwater environment database (MUED), and underwater image enhancement benchmark (UIEB) data sets and the turbid underwater images captured from natural waters. Different experimental evaluations demonstrated that the proposed method not only effectively enhances the visual quality of turbid underwater images but also improves the performance of downstream vision tasks.
2 citations
14 Aug 2019
TL;DR: An algorithm of image de-noising for Gaussian-Gaussian mixed noise based on stochastic resonance is innovatively proposed and can achieve higher peak-signal-to-noise (PSNR) and structural similarity (SSIM), and meanwhile the visual effect is also better.
Abstract: It is of great significance to image de-noising since the image has been the main medium of acquiring and transmitting information in human life. The image is not only destroyed by signal-independent noise, but also destroyed by signaldependent noise largely. In order to fill the gap between stochastic resonance for image processing with signalindependent noise and signal-dependent noise and eliminate the shortcoming of unsatisfactory processing results of image in the traditional image de-noising methods, an algorithm of image de-noising for Gaussian-Gaussian mixed noise based on stochastic resonance is innovatively proposed in this paper. And three main steps involved are image segmentation, add mixed noise to the image and stochastic resonance processing. First, the original image is clustered and segmented to obtain multiple regions. Then, signal-independent Gaussian noise and signal-dependent Gaussian noise are added to the image in sequence. Finally the multiple noisy regions are respectively processing by stochastic resonance. The proposed method is experimented with different noise variance combinations. The experimental results show that the proposed method can achieve higher peak-signal-to-noise (PSNR) and structural similarity (SSIM), and meanwhile the visual effect is also better.
References
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2,671 citations
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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
2,403 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.
2,395 citations
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.
1,674 citations