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Journal ArticleDOI

Enhancement of low-contrast images by internal noise-induced Fourier coefficient rooting

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.
Citations
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Journal ArticleDOI
TL;DR: An advanced adaptive and simple algorithm for dark medical image enhancement based on adaptive gamma correction using discrete wavelet transform with singular-value decomposition (DWT-SVD-AGC) is proposed and shows that it performs better than other state-of-the-art techniques.
Abstract: The performances of medical image processing techniques, in particular CT scans, are usually affected by poor contrast quality introduced by some medical imaging devices. This suggests the use of contrast enhancement methods as a solution to adjust the intensity distribution of the dark image. In this paper, an advanced adaptive and simple algorithm for dark medical image enhancement is proposed. This approach is principally based on adaptive gamma correction using discrete wavelet transform with singular-value decomposition (DWT-SVD). In a first step, the technique decomposes the input medical image into four frequency sub-bands by using DWT and then estimates the singular-value matrix of the low–low (LL) sub-band image. In a second step, an enhanced LL component is generated using an adequate correction factor and inverse singular value decomposition (SVD). In a third step, for an additional improvement of LL component, obtained LL sub-band image from SVD enhancement stage is classified into two main classes (low contrast and moderate contrast classes) based on their statistical information and therefore processed using an adaptive dynamic gamma correction function. In fact, an adaptive gamma correction factor is calculated for each image according to its class. Finally, the obtained LL sub-band image undergoes inverse DWT together with the unprocessed low–high (LH), high–low (HL), and high–high (HH) sub-bands for enhanced image generation. Different types of non-contrast CT medical images are considered for performance evaluation of the proposed contrast enhancement algorithm based on adaptive gamma correction using DWT-SVD (DWT-SVD-AGC). Results show that our proposed algorithm performs better than other state-of-the-art techniques.

60 citations


Cites background from "Enhancement of low-contrast images ..."

  • ...Different techniques are proposed in literature to repair the damaged images and improve their quality, improve their contrast and brightness [12], [19]....

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Journal ArticleDOI
TL;DR: Simulation results show that the proposed algorithm consistently produces good contrast enhancement, with best brightness and edges details conservation and with minimum added distortions to the enhanced CT images.
Abstract: We propose in this paper a new enhancement algorithm dedicated to dark computed tomography (CT) scan based on discrete wavelet transform with singular value decomposition (DWT–SVD) followed by adaptive gamma correction (AGC). Discrete wavelet transform (DWT) is considered to decompose the input dark CT image in four sub-bands. Singular value decomposition (SVD) is used in order to compute the corresponding singular value matrix of low–low (LL) sub-band image. The enhanced LL sub-band is determined by scaling the singular value matrix of original LL sub-band by an adequate correction factor, followed by inverse SVD. For a further contrast improvement, the new enhanced LL sub-band image is processed using an AGC algorithm. Finally, the obtained LL sub-band image undergoes inverse DWT together with the unprocessed sub-bands to generate the final enhanced image. This proposed method has the advantage of being fully automatic and could be applied for dark input images with either low or moderate contrast. Different dark CT images are considered to compare the performance of our proposed method to three other enhancement techniques using both objective and subjective assessments. Simulation results show that our proposed algorithm consistently produces good contrast enhancement, with best brightness and edges details conservation and with minimum added distortions to the enhanced CT images.

20 citations

Journal ArticleDOI
TL;DR: This work implements an improved retinex image enhancement algorithm to enhance the structure layer and uses mask-weighted least squares method to suppress noise and artifact in the texture layer.
Abstract: Nighttime image captured in low- or non-uniform illumination scene always suffers from the loss of visibility and contains various noise and objectionable artifact. When we enlarge the amplitude of the brightness, the noise and artifact will be amplified as well. Hence, we propose a nighttime image enhancement approach based on image decomposition. We decompose the input image into two components: Structure layer contains main information of the image, and texture layer contains details, noise, and artifacts. We implement an improved retinex image enhancement algorithm to enhance the structure layer. To remain details and suppress noise and artifact in the texture layer, we use mask-weighted least squares method. In the final, we fuse these two components to obtain the result. The experimental results demonstrate that the proposed approach can improve the perceptual quality of nighttime images and suppress noise and artifact without excessive reinforcement.

17 citations

Journal ArticleDOI
TL;DR: In this article, a brief overview of nonlinear resonance applications in the context of image processing is presented, and a threshold detector based on these resonance properties is introduced. But this detector is not suitable for image classification.
Abstract: In this paper, we first propose a brief overview of nonlinear resonance applications in the context of image processing. Next, we introduce a threshold detector based on these resonance properties ...

8 citations

Journal ArticleDOI
TL;DR: An extensive literature review was conducted and the nighttime visual refinement approaches into nighttime restoration and enhancement were classified and identified the research gap fields to explore future research directions in nighttime visual enhancement techniques.
Abstract: Video surveillance systems substitute manual efforts in various safety critic domains such as border area, assisted living, banking, service stations, and transportation. The multimedia-based surveillance system has a significant role in security and forensic systems because people tend to be easily convinced after observing voice, image, and video. Hence, these videos are strong evidence in the forensic investigation. However, most of the criminal activities such as ATM robbery and assassination are occur at nighttime because of the crime supporting dark environment. Many of the night surveillance systems in military, as well as commercial applications, are equipped with infrared and thermal based night vision systems. Its poor capability of texture and color interpretations are the major issues to ensure secure nighttime video monitoring. Specifically, visual refinements of nighttime surroundings and foreground objects provide a valuable assistance in the nighttime security system. In this scenario, it is highly recommended a review of the state-of-the-art nighttime visual refinement approaches. We conducted an extensive literature review and classified the nighttime visual refinement approaches into nighttime restoration and enhancement. This comparative literary analysis identified the research gap fields to explore future research directions in nighttime visual enhancement techniques. Finally, we discussed various open issues and future directions in the context enhancement based nighttime enhancement research.

8 citations

References
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Journal ArticleDOI
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

Journal ArticleDOI
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

Proceedings ArticleDOI
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

Journal ArticleDOI
TL;DR: The results of a psychophysics experiment show that the brain can consistently and quantitatively interpret detail in a stationary image obscured with time varying noise and that both the noise intensity and its temporal characteristics strongly determine the perceived image quality.
Abstract: Stochastic resonance can be used as a measuring tool to quantify the ability of the human brain to interpret noise contaminated visual patterns. Here we report the results of a psychophysics experiment which show that the brain can consistently and quantitatively interpret detail in a stationary image obscured with time varying noise and that both the noise intensity and its temporal characteristics strongly determine the perceived image quality.

470 citations

Journal ArticleDOI
TL;DR: An algorithm that enhances the contrast of an input image using interpixel contextual information and produces better or comparable enhanced images than four state-of-the-art algorithms is proposed.
Abstract: This paper proposes an algorithm that enhances the contrast of an input image using interpixel contextual information. The algorithm uses a 2-D histogram of the input image constructed using a mutual relationship between each pixel and its neighboring pixels. A smooth 2-D target histogram is obtained by minimizing the sum of Frobenius norms of the differences from the input histogram and the uniformly distributed histogram. The enhancement is achieved by mapping the diagonal elements of the input histogram to the diagonal elements of the target histogram. Experimental results show that the algorithm produces better or comparable enhanced images than four state-of-the-art algorithms.

383 citations