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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01 Dec 2014
TL;DR: In the proposed algorithm, the area to be enhanced can be detected exactly with a minimum user intervention and enhanced using a singular value decomposition (SVD) based technique which preserves the color information of the scene and can enhance the scene in real time.
Abstract: The main objective of this paper is to propose a method for localized image enhancement. This method can be highly beneficial in medical purposes, like Laminography, or in enhancing images captured under non-uniform illumination. Along with a depth map, a spatial queue-based data structure is used to properly locate the area to be enhanced in the captured scene. There are automated techniques to detect areas where enhancement is required, but these methods suffer heavily with false detections, which, in turn, deteriorate the perceptual quality of the image. In the proposed algorithm, the area to be enhanced can be detected exactly with a minimum user intervention. After detecting, the area is enhanced using a singular value decomposition (SVD) based technique which preserves the color information of the scene and can enhance the scene in real time.
1 citations
Cites methods from "Wavelet-based contrast enhancement ..."
...and our algorithm clearly outperform them [6], [8], [15], [16]....
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TL;DR: Some techniques used for contrast enhancement of image are presented, which uses scaling to remove internal noise of dark images using DCT technique.
Abstract: Contrast enhancement technique is used to enhance the perception of the image or scene. This enhancement controls the brightness difference between objects and their backgrounds. It also used as brightness preservation of image. Numerous contrast enhancement techniques are offered by various researchers for the betterment of the quality of images like contrast enhancement of HDR, DCT, DWT, Filtering etc. the common problem with image enhancement is difficult to achieve such images. Noise is an unwanted element of system that creates problem or interference. Contrast enhancement uses scaling to remove internal noise of dark images using DCT technique. Here in this paper we are presenting some techniques used for contrast enhancement of image.
1 citations
29 Jul 2018
TL;DR: This paper presented the idea of auto-tuning of the iteration with random noise and threshold value 0 by using the process related to the histogram calculation, mean and median, and confirmed the effectiveness of the auto- Tuning SR based image enhancement algorithm.
Abstract: This paper is associated with evaluation of an auto-tuning stochastic resonance (SR) for image enhancement on illumination variant images. The new process has been developed being based on our previous works related to the image enhancement by using the manual tuning stochastic resonance. The process was performed by adding the random noise and threshold 0 in an image. The process works properly in the dark and very low contrast images as well as bright images based and mixed illumination variant images. This image enhancement system works on the images that includes both dark and bright environment. The system was tested with the face detection algorithm on the dark and illumination variant images. In this paper, we present the idea of auto-tuning of the iteration with random noise and threshold value 0 by using the process related to the histogram calculation, mean and median. In this paper, we performed various experiments on object and human detection as well under different conditions and confirmed the effectiveness of our auto-tuning SR based image enhancement algorithm.
1 citations
01 Dec 2015
TL;DR: It is observed that by semi-adaptively changing the processing parameters with iteration, the processed dark regions and the unprocessed bright regions of an image smoothly merge producing a quality of dynamic range compression in the image.
Abstract: This paper presents a noise-aided dynamic range compression algorithm using a stochastic resonance model in spatial domain. An input statistics-dependent stochastic resonance (ISSR) model, that is designed for contrast enhancement of dark images, is used here to enhance an image with both bright and dark areas. The underilluminated regions of such an image are selected as the De Vries Rose region from a human visual system-based segmentation algorithm, and then processed using the ISSR model. It is observed that by semi-adaptively changing the processing parameters with iteration, the processed dark regions and the unprocessed bright regions of an image smoothly merge producing a quality of dynamic range compression in the image. The performance of the proposed algorithm is characterized using image quality index for tone-mapped images and a no-reference perceptual quality measure. Results and comparative analysis suggest notable performance of the proposed algorithm with fewer iteration.
Cites background from "Wavelet-based contrast enhancement ..."
...In the context of image enhancement, noise-aided stochastic resonance-based algorithms have been found to display noteworthy performance, especially in contrast enhancement of dark images in spatial [11], [12], [13], frequency [14], [15], multiresolutional [16], [17] domains....
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Journal Article•
TL;DR: The proposed methodology implemented provides high image quality index and image color enhancement as well as perceptual quality of dark images and hybrid combination of transformation and filtering techniques.
Abstract: Contrast Enhancement involves enhancement of images such that the visibility of images increases Contrast enhancement of images is used for a variety of applications such as in medical field, image enhances software‘s Although there are various techniques implemented for the enhancement of images such as using non-dynamic stochastic resonance [1] But the technique implemented improves the enhancement of images up to some extent Here in this paper an efficient technique is implemented for the contrast enhancement using hybrid combination of transformation and filtering techniques The idea is to use DWT transformation and then applying guided filtering and bilateral filtering The proposed methodology implemented provides high image quality index and image color enhancement as well as perceptual quality of dark images General Terms Gray Scale, GIF, JPEG, TIFF, RGB model, CMYK model, guided filtering, bilateral filtering, DWT
Cites background or methods from "Wavelet-based contrast enhancement ..."
...The table shown below is the analysis of Existing technique [2] for different dark images....
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...They were able to achieve iterative scaling of internal noise which is caused by insufficient illumination by tuning of approximation and detailed coefficient with the help of parameter dependent equation and selecting the parameters by increasing signal to noise ratio of traditional SR system [2]....
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...Image processing techniques deployment resolves such type of enhancement problem in dark or over bright images [2]....
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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.
2,774 citations
2,671 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
"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....
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...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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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
"Wavelet-based contrast enhancement ..." refers background or 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 modified highpass filtering (MHPF) [21] has been performed....
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...Many techniques for contrast enhancement that operate in spatial domain exist in literature [10], [3], [9], [20]....
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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.
913 citations
"Wavelet-based contrast enhancement ..." refers methods in this paper
...Here, perceptual quality is calculated from a model [19] taking into account the activity, blurriness and blockiness of an image....
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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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