Localized image enhancement
08 May 2014-pp 1-6
TL;DR: This work proposes a fast algorithm to increase the contrast of an image locally using singular value decomposition (SVD) approach and attempts to define some parameters which can give clues related to the progress of the enhancement process.
Abstract: Image enhancement is a well established field in image processing. The main objective of image enhancement is to increase the perceptual information contained in an image for better representation using some intermediate steps, like, contrast enhancement, debluring, denoising etc. Among them, contrast enhancement is especially important as human eyes are more sensitive to luminance than the chrominance components of an image. Most of the contrast enhancement algorithms proposed till now are global methods. The major drawback of this global approach is that in practical scenarios, the contrast of an image does not deteriorate uniformly and the outputs of the enhancement techniques reach saturation at proper contrast points. That leads to information loss. In fact, to the best of our knowledge, no non-reference perceptual measure of image quality has yet been proposed to measure localized enhancement. We propose a fast algorithm to increase the contrast of an image locally using singular value decomposition (SVD) approach and attempt to define some parameters which can give clues related to the progress of the enhancement process.
Citations
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04 Mar 2016
TL;DR: In this paper, contrast enhancement techniques are used to achieve contrast enhancement of images using neighborhood operation, average filter, bilateral ratinex, imadjust and sigmoid function to process a medical image.
Abstract: The main goal of this paper is to process a medical image. For a medical diagnosis, the result is more suitable. Contrast enhancement is used to improve the contrast of an image. Contrast enhancement of images is used for a different variety of applications such as in the medical field. Most of the images like medical images, remote sensing, aerial images and real life photographs suffer from poor contrast. The main goal of image enhancement is to improve the quality or clarity of images or to increase the interpretability in images for human viewing. In medical images detection and analysis, contrast enhancement techniques are one of the most significant stages. We are used contrast enhancement techniques to achieve contrast enhancement of images. The type of techniques includes neighborhood operation, average filter, bilateral ratinex, imadjust and sigmoid function. All these techniques are comparing with each other to achieve which enhancement techniques have produced a better contrast of an image. The four separate parameters are used. These parameters are such as peak signal to noise ratio (PSNR), mean square error (MSE), normalization coefficient (NC) and root mean square error (RMSE). In image research, this is one of the most important and difficult technique.
23 citations
Cites methods from "Localized image enhancement"
...Frequency domain enhancement method is used to overcome the defects of spatial domain enhancement [5]....
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TL;DR: Experimental results show that the proposed novel haze-removal method can restore inland waterway images effectively; restored images are more natural and smoother than those obtained by the state-of-the-art haze removal algorithms.
Abstract: Haze significantly degrades the visibility for ship navigation and traffic monitoring in China's inland waterways. In this study, the authors propose a novel haze-removal method based on sky segmentation and dark channel prior to restore images. Sky segmentation is accomplished by using robust image matting and region growth algorithms. Then, the average image intensity of the sky region is chosen as the atmospheric light value to address the defect of dark channel prior. Experimental results show that their method can restore inland waterway images effectively; restored images are more natural and smoother than those obtained by the state-of-the-art haze removal algorithms.
20 citations
01 Aug 2016
TL;DR: B robust image enhancement algorithms are discussed, implemented to noisy images and compared according to their robustness, and Peak Signal to Noise Ratio (PSNR) and Mean Squared Error (MSE) quality measure metrics are used to compare the image enhancement methods systematically.
Abstract: The essential target of image enhancement is to minimize noise from a digital image by keeping the intrinsic information of the image preserved. The main difficulty in image enhancement is determining the criteria for enhancement and, therefore, more than one image enhancement techniques are empirical and require interactive procedures to obtain satisfactory results. In this paper robust image enhancement algorithms are discussed, implemented to noisy images and compared according to their robustness. The algorithms are especially able to improve the contrast of medical images, fingerprint images and selenography images by means of software techniques. When deciding that one image has better quality than another image, quality measure metrics are needed. Otherwise comparing image quality just by visual appearance may not be objective because images could vary from person to person. That is why quantitative metrics are crucial to compare images for their qualities. In this paper Peak Signal to Noise Ratio (PSNR) and Mean Squared Error (MSE) quality measure metrics are used to compare the image enhancement methods systematically. All the methods are validated by the performance measures with PSNR and MSE. It is believed that this paper will provide comprehensive reference source for the researchers involved in image enhancement field.
17 citations
19 Aug 2016
TL;DR: This paper proposes a novel dehazing method to increase visibility from a single view without using any prior knowledge about the outdoor scene and uses stochastic iterative algorithm to remove fog and haze.
Abstract: Images captured in presence of fog, haze or snow usually suffer from poor contrast and visibility. In this paper we propose a novel dehazing method to increase visibility from a single view without using any prior knowledge about the outdoor scene. The proposed method estimates a visibility map of the scene from the input image and uses stochastic iterative algorithm to remove fog and haze. The method can be applied to color and grayscale images. Experimental results show that the proposed algorithm outperforms most of the state-of-the-art algorithms in terms of contrast, colorfulness and visibility.
15 citations
Cites methods from "Localized image enhancement"
...Though several methods exist to quantify the quality of a processed image for global enhancement, it is extremely difficult to quantify the quality of a locally enhanced image [21]....
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15 citations
References
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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
18 Jun 2003
TL;DR: A method for acquiring high-complexity stereo image pairs with pixel-accurate correspondence information using structured light that does not require the calibration of the light sources and yields registered disparity maps between all pairs of cameras and illumination projectors.
Abstract: Progress in stereo algorithm performance is quickly outpacing the ability of existing stereo data sets to discriminate among the best-performing algorithms, motivating the need for more challenging scenes with accurate ground truth information. This paper describes a method for acquiring high-complexity stereo image pairs with pixel-accurate correspondence information using structured light. Unlike traditional range-sensing approaches, our method does not require the calibration of the light sources and yields registered disparity maps between all pairs of cameras and illumination projectors. We present new stereo data sets acquired with our method and demonstrate their suitability for stereo algorithm evaluation. Our results are available at http://www.middlebury.edu/stereo/.
1,840 citations
"Localized image enhancement" refers methods in this paper
...To analyze the performances of various existing measure techniques, we use the data set provided by Scharstein and Szeliski [20]....
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...RESULTS & OBSERVATIONS To analyze the performances of various existing measure techniques, we use the data set provided by Scharstein and Szeliski [20]....
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...[20] D. Scharstein and R. Szeliski, High-accuracy stereo depth maps using structured light, CVPR’03, 2003, volume 1, pages 195-202....
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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
"Localized image enhancement" refers background in this paper
...[19], we observe that PQM is close to 10 for well illuminated images and deviation on both side means poor perceptual quality....
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...have been defined such that the improvement can be measured using the processed image and the input image [9], [10], [18], [19]....
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DOI•
14 Feb 2023
TL;DR: The optical structure and optical properties of the human eye are described, how the retinal image is formed and the factors affecting its quality are described.
626 citations