Noise-aided dynamic range compression using selective processing in a statistics-dependent stochastic resonance model
01 Dec 2015-pp 1-4
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.
References
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16 Dec 2012
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.
20 citations
"Noise-aided dynamic range compressi..." refers background in this paper
...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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01 Oct 2014
TL;DR: A noise-aided image enhancement algorithm focussed on addressing images that have a large dynamic range, i.e., images with both dark and bright regions, and the application of a new mathematical model, in a shifted double-well system exhibiting stochastic resonance, is investigated.
Abstract: This paper presents a noise-aided image enhancement algorithm focussed on addressing images that have a large dynamic range, i.e., images with both dark and bright regions. The application of a new mathematical model, in a shifted double-well system exhibiting stochastic resonance, is investigated for such images. The new mathematical model addresses the shortcomings of earlier SR-based enhancement model by deriving parameters purely from input values (instead of input statistics). This model is specific to spatial domain pixel representation and operates on a revised iterative equation. This iterative processing is here applied selectively to the under-illuminated regions of the image, characterized as the De Vries-Rose (DVR) region of a human psychovisual model. The idea of suitably modifying the existing universal image quality index is also proposed for its participation in iteration termination, and to gauge the property of dynamic range compression. While the iterative algorithm is terminated using the revised image quality index, entropy maximization, and contrast quality of DVR region with constraints on perceptual quality, the performance of the proposed algorithm is also characterized by observing color enhancement and subjective scores on visual quality.
7 citations
"Noise-aided dynamic range compressi..." refers background or methods in this paper
...Among those listed, Selective-IVSR [19] is the only iterative dynamic SR-based algorithm that produces DRC, and gives best TMQI but at the cost of larger t0....
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...More recently, a noise-aided dynamic range compression algorithm [19] was reported using intensity-specific value-dependent SR (IVSR model) parameter selection and selective-processing of psychovisually underexposed regions....
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...Input Selective-ISSR (Proposed) Selective-IVSR [19] AHE MSR [1]...
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...5, and the quantitative performance values in terms of TMQI, PQM, and iteration count, t0, are displayed in Table I. Visually, the proposed method gives noteworthy and comparable performance with Selective-IVSR, MSR, HMF, and better outputs than PS-AC and MCEDRC....
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...It is found that while Selective-IVSR takes nine iterations for all test images (owing to the mapping characteristics of the IVSR model), the optimal iteration count for the same test images using proposed method was lesser, despite the use of semi-adaptively decreasing value of Δt....
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TL;DR: In this paper, the authors proposed a level-to-level framework by generating the reference intensity level and the given target intensity level, which is an integration algorithm, the illumination compensation could be considered as the transformation from reference intensity to the chosen target intensity, and the contrast is enhanced by the given algebraic definition of the characteristic values of each pixel.
Abstract: In this study, the authors present a new strategy to implement an illumination compensation-based contrast enhancement. Different from the traditional pixel-to-pixel transformation, the proposed method offers a level-to-level framework by generating the reference intensity level and the given target intensity level. Fundamentally, the traditional illumination compensation algorithms such as Histogram equalisation, log and gamma transformation are trade-off strategies and face the same dilemma. For example, the log function compresses the dynamic range of image with large variations in pixel values. The proposed method is an integration algorithm, the illumination compensation could be considered as the transformation from reference intensity to the chosen target intensity, and the contrast is enhanced by the given algebraic definition of the characteristic values of each pixel. The advantages are the adjusted intensity would be accomplished in the native phase according to the variant characteristic intensity values, clarifying the details of the darker areas as well as preserving the highlight areas, reducing HALO, colour constancy, removing colour cast and colour restoration. Shown as the experimental results, the proposed method can obtain better performance compared with other methods in subjective evaluation with contour plot and the objective evaluations by universal quality index, structural similarity index and average of standard deviations.
4 citations
"Noise-aided dynamic range compressi..." refers background in this paper
...Several remarkable enhancement and dynamic range compression algorithms, in spatial and frequency domains, have been found in the literature in the past several years [1], [2], [3], [4], [5], [6], [7], [8], [9], [10]....
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TL;DR: An adaptive normalisation-based method is proposed in this study that preserves the low-frequency facial features, maximising the intra-individual correlation and improves the visual quality of face images in different lighting conditions.
Abstract: Illumination variation is a challenging issue in face recognition. In many conventional approaches the low-frequency coefficients are usually discarded in order to compensate the illumination variations, and hence degrade the visual quality. To deal with these problems, an adaptive normalisation-based method is proposed in this study. Each image is normalised according to its lighting attribute by mapping the low-frequency components to the normal condition instead of discarding them by applying a novel statistical concept called light mapping matrix. The method preserves the low-frequency facial features, maximising the intra-individual correlation and improves the visual quality of face images in different lighting conditions.
2 citations
"Noise-aided dynamic range compressi..." refers background in this paper
...Several remarkable enhancement and dynamic range compression algorithms, in spatial and frequency domains, have been found in the literature in the past several years [1], [2], [3], [4], [5], [6], [7], [8], [9], [10]....
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