Noise-aided dynamic range compression using selective processing in a statistics-dependent stochastic resonance model

doi:10.1109/VCIP.2015.7457836

Proceedings Article•DOI•

Noise-aided dynamic range compression using selective processing in a statistics-dependent stochastic resonance model

Rajlaxmi Chouhan¹, Prabir Kumar Biswas¹•Institutions (1)

Indian Institute of Technology Kharagpur¹

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.

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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.

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Journal Article•DOI•

Human Visual System-Based Image Enhancement and Logarithmic Contrast Measure

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Karen Panetta¹, Eric J. Wharton¹, Sos S. Agaian²•Institutions (2)

Tufts University¹, University of Texas at San Antonio²

01 Feb 2008

TL;DR: Two novel image enhancement algorithms are introduced: edge-preserving contrast enhancement, which is able to better preserve edge details while enhancing contrast in images with varying illumination, and a novel multihistogram equalization method which utilizes the human visual system to segment the image, allowing a fast and efficient correction of nonuniform illumination.

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Abstract: Varying scene illumination poses many challenging problems for machine vision systems. One such issue is developing global enhancement methods that work effectively across the varying illumination. In this paper, we introduce two novel image enhancement algorithms: edge-preserving contrast enhancement, which is able to better preserve edge details while enhancing contrast in images with varying illumination, and a novel multihistogram equalization method which utilizes the human visual system (HVS) to segment the image, allowing a fast and efficient correction of nonuniform illumination. We then extend this HVS-based multihistogram equalization approach to create a general enhancement method that can utilize any combination of enhancement algorithms for an improved performance. Additionally, we propose new quantitative measures of image enhancement, called the logarithmic Michelson contrast measure (AME) and the logarithmic AME by entropy. Many image enhancement methods require selection of operating parameters, which are typically chosen using subjective methods, but these new measures allow for automated selection. We present experimental results for these methods and make a comparison against other leading algorithms.

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270 citations

"Noise-aided dynamic range compressi..." refers background or methods in this paper

...In spatial domain, such a segmentation may be possible using the human visual system (HVS)-based segmentation algorithm of [20], that was also reported to be used in another SR-based dynamic range compression algorithm [19]....
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...The human visual system (HVS)-based image enhancement model [20] segments an image based upon background intensity and gradient....
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Journal Article•DOI•

Enhancement of Color Images by Scaling the DCT Coefficients

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Jayanta Mukherjee¹, Sanjit K. Mitra²•Institutions (2)

Indian Institute of Technology Kharagpur¹, University of Southern California²

01 Oct 2008-IEEE Transactions on Image Processing

TL;DR: The proposed technique, computationally more efficient than the spatial domain based method, is found to provide better enhancement compared to other compressed domain based approaches.

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Abstract: This paper presents a new technique for color enhancement in the compressed domain. The proposed technique is simple but more effective than some of the existing techniques reported earlier. The novelty lies in this case in its treatment of the chromatic components, while previous techniques treated only the luminance component. The results of all previous techniques along with that of the proposed one are compared with respect to those obtained by applying a spatial domain color enhancement technique that appears to provide very good enhancement. The proposed technique, computationally more efficient than the spatial domain based method, is found to provide better enhancement compared to other compressed domain based approaches.

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238 citations

"Noise-aided dynamic range compressi..." refers background or methods in this paper

...HMF MCEDRC [2] DRC-CES-BLK [3] PS-AC CVC [4]...
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...As dynamic range compression (DRC) is obtained in the proposed selective-ISSR algorithm, we present a comparison with some algorithms that produce a DRC quality, such as, selective-IVSR [19], classical adaptive histogram equalization (AHE), multiscale retinex (MSR) [1], homomorphic filtering (HMF), multicontrast enhancement with DRC (MCEDRC) [2], scaling of DCT coefficients with suppression of blocking artifacts (DRC-CES-BLK) [3], Auto Contrast of Adobe Photoshop (PS-AC), and contextual and variational contrast enhancement (CVC) [4]....
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...According to [3], the values of PQM closest to 10 indicate best visual qualities....
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...DRC-CES-BLK also produces DRC but leaves some minor blocking artifacts....
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...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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Journal Article•DOI•

An Efficient Content-Based Image Enhancement in the Compressed Domain Using Retinex Theory

[...]

Sangkeun Lee¹•Institutions (1)

Samsung¹

01 Feb 2007-IEEE Transactions on Circuits and Systems for Video Technology

TL;DR: The main advantage of the proposed algorithm enhances the details in the dark and the bright areas with low computations without boosting noise information and affecting the compressibility of the original image since it performs on the images in the compressed domain.

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Abstract: The object of this paper is to present a simple and efficient algorithm for dynamic range compression and contrast enhancement of digital images under the noisy environment in the compressed domain. First, an image is separated into illumination and reflectance components. Next, the illumination component is manipulated adaptively for image dynamics by using a new content measure. Then, the reflectance component based on the measure of the spectral contents of the image is manipulated for image contrast. The spectral content measure is computed from the energy distribution across different spectral bands in a discrete cosine transform (DCT) block. The proposed approach also introduces a simple scheme for estimating and reducing noise information directly in the DCT domain. The main advantage of the proposed algorithm enhances the details in the dark and the bright areas with low computations without boosting noise information and affecting the compressibility of the original image since it performs on the images in the compressed domain. In order to evaluate the proposed scheme, several base-line approaches are described and compared using enhancement quality measures

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143 citations

"Noise-aided dynamic range compressi..." refers background or methods in this paper

...As dynamic range compression (DRC) is obtained in the proposed selective-ISSR algorithm, we present a comparison with some algorithms that produce a DRC quality, such as, selective-IVSR [19], classical adaptive histogram equalization (AHE), multiscale retinex (MSR) [1], homomorphic filtering (HMF), multicontrast enhancement with DRC (MCEDRC) [2], scaling of DCT coefficients with suppression of blocking artifacts (DRC-CES-BLK) [3], Auto Contrast of Adobe Photoshop (PS-AC), and contextual and variational contrast enhancement (CVC) [4]....
[...]
...HMF MCEDRC [2] DRC-CES-BLK [3] PS-AC CVC [4]...
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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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...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]....
[...]

Journal Article•DOI•

Magnetic resonance image enhancement using stochastic resonance in Fourier domain

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V. P. Subramanyam Rallabandi¹, Prasun Kumar Roy¹•Institutions (1)

National Brain Research Centre¹

01 Nov 2010-Magnetic Resonance Imaging

TL;DR: The proposed stochastic resonance (SR)-based transform in Fourier space for the enhancement of magnetic resonance images of brain lesions can restore the original image from noisy image and optimally enhance the edges or boundaries of the tissues, and enables improved diagnosis over conventional methods.

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Abstract: Objective In general, low-field MRI scanners such as the 0.5- and 1-T ones produce images that are poor in quality. The motivation of this study was to lessen the noise and enhance the signal such that the image quality is improved. Here, we propose a new approach using stochastic resonance (SR)-based transform in Fourier space for the enhancement of magnetic resonance images of brain lesions, by utilizing an optimized level of Gaussian fluctuation that maximizes signal-to-noise ratio (SNR). Materials and Methods We acquired the T1-weighted MR image of the brain in DICOM format. We processed the original MR image using the proposed SR procedure. We then tested our approach on about 60 patients of different age groups with different lesions, such as arteriovenous malformation, benign lesion and malignant tumor, and illustrated the image enhancement by using just-noticeable difference visually as well as by utilizing the relative enhancement factor quantitatively. Results Our method can restore the original image from noisy image and optimally enhance the edges or boundaries of the tissues, clarify indistinct structural brain lesions without producing ringing artifacts, as well as delineate the edematous area, active tumor zone, lesion heterogeneity or morphology, and vascular abnormality. The proposed technique improves the enhancement factor better than the conventional techniques like the Wiener- and wavelet-based procedures. Conclusions The proposed method can readily enhance the image fusing a unique constructive interaction of noise and signal, and enables improved diagnosis over conventional methods. The approach well illustrates the novel potential of using a small amount of Gaussian noise to improve the image quality.

...read moreread less

108 citations

Journal Article•DOI•

Content-Aware Dark Image Enhancement Through Channel Division

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Adin Ramirez Rivera¹, Byungyong Ryu¹, Oksam Chae¹•Institutions (1)

Kyung Hee University¹

01 Sep 2012-IEEE Transactions on Image Processing

TL;DR: A content-aware algorithm is proposed that enhances dark images, sharpens edges, reveals details in textured regions, and preserves the smoothness of flat regions, which is an improvement over many existing methods.

...read moreread less

Abstract: The current contrast enhancement algorithms occasionally result in artifacts, overenhancement, and unnatural effects in the processed images. These drawbacks increase for images taken under poor illumination conditions. In this paper, we propose a content-aware algorithm that enhances dark images, sharpens edges, reveals details in textured regions, and preserves the smoothness of flat regions. The algorithm produces an ad hoc transformation for each image, adapting the mapping functions to each image's characteristics to produce the maximum enhancement. We analyze the contrast of the image in the boundary and textured regions, and group the information with common characteristics. These groups model the relations within the image, from which we extract the transformation functions. The results are then adaptively mixed, by considering the human vision system characteristics, to boost the details in the image. Results show that the algorithm can automatically process a wide range of images—e.g., mixed shadow and bright areas, outdoor and indoor lighting, and face images—without introducing artifacts, which is an improvement over many existing methods.

...read moreread less

93 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]....
[...]

Noise-aided dynamic range compression using selective processing in a statistics-dependent stochastic resonance model

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"Noise-aided dynamic range compressi..." refers background or methods in this paper

"Noise-aided dynamic range compressi..." refers background or methods in this paper

"Noise-aided dynamic range compressi..." refers background or methods in this paper

"Noise-aided dynamic range compressi..." refers background in this paper

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