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A digital image is not an exact snapshot of reality; it is only a discrete approximation. Thus, the captured images are always bit different from the images actually perceived by human eyes. These variations occur due to varying l...
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A digital image is not an exact snapshot of reality; it is only a discrete approximation. Thus, the captured images are always bit different from the images actually perceived by human eyes. These variations occur due to varying lighting conditions, weathers conditions like rain and fog, distance of scene from camera, image capturing angle, etc. The problem becomes more severe if these images are captured using low resolution image capturing devices like: Mobile phones, CCTV Cameras, Webcam, VGA cameras etc. Image enhancement addresses a solution of generating a high quality image from its low contrast version. Color enhancement is a process that differentiates objects in an image; as well as provides the detailed information of that image. This paper proposes color enhancement of low resolution digital images using clock algorithm. It is claimed that the proposed clock algorithm employed here produces good quality images in comparison with the existing color enhancement techniques. The simulation results proved that the proposed clock algorithm efficiently enhances the quality of digital low resolution images and analytically their quality improvement is observed in terms of peak signal to noise ratio (PSNR), mean square error (MSE) and bit error rate (BER) over the existing color enhancement techniques.
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The quality of satellite images is frequently degraded by the low-contrast effect. Therefore, different research works have been developed to deal with this undesirable effect. Still, no conclusive verdicts have been reached and t...
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The quality of satellite images is frequently degraded by the low-contrast effect. Therefore, different research works have been developed to deal with this undesirable effect. Still, no conclusive verdicts have been reached and the problem remains open for research. Hence, an ameliorated balance contrast enhancement technique using a parabolic function (ABCETP) is proposed in this article to improve the quality in terms of brightness, contrast, and colors. The original BCETP works by determining a parabolic function with three coefficients. However, the proposed ABCETP works by computing a modified parabolic function with one modified and two unmodified coefficients, as well as, it utilizes two additional methods to produce adequate-quality results. The proposed ABCETP is examined with numerous real contrast-distorted images, compared against different contrast enhancement methods and the results are assessed with two specialized quality appraisal metrics. Empirical results obtained from conducting various comparisons and experiments revealed the favorability of ABCETP, wherein it outputted images with better-perceived quality and outperformed the comparative methods in several aspects.
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Retinal images show an essential role in Ophthalmology to diagnosis wide set of diseases. In this direction, using retinal images in computerized techniques increases the ability of diagnosis in fast time effectively. However, som...
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Retinal images show an essential role in Ophthalmology to diagnosis wide set of diseases. In this direction, using retinal images in computerized techniques increases the ability of diagnosis in fast time effectively. However, some eye diseases and capturing conditions produce low-quality retinal images, which reduces the diagnosis ability for machines and humans. To solve that, several works have been proposed to enhance retinal images. But they show a lot of negative observations, especially with color images of retina. In this paper, a novel enhancement algorithm for color retinal images is proposed. It consists of three stages; firstly, the appearance of visual details is increased by enhancing the contrast of structural details of retinal image using details enhanced and Bilateral filters. Then, a novel uneven illumination correction method is proposed to solve the uneven illumination problem adaptively. Finally, the advantages of both previous stages are combined using HSV color model to produce the final enhanced retinal images. DRIVE and STARE benchmark datasets are used to conduct experiments. The results were compared with histogram Equalized (HE), Contrast Stretching (CS), the adaptive histogram equalization (CLAHE) and Zhou’s method retinal enhancement methods. In conclusion, the results show that the proposed method shows high performance compared with the corresponding enhancement methods.
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A survey of local image contrast measures is presented and a new contrast measure for measuring the local contrast of regions of interest is proposed. The measures validation is based on the gradual objective contrast decreasing o...
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A survey of local image contrast measures is presented and a new contrast measure for measuring the local contrast of regions of interest is proposed. The measures validation is based on the gradual objective contrast decreasing on medical test images in both grayscale and color. The performance of the eleven most frequented contrast measures is mutually compared and their robustness to different types of image degradation is analyzed. Since the contrast measures can be both global, regional and local pixelwise, a simple way of adapting the contrast measures for regions of interest is proposed.
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Reproduction of more pleasing colors for natural objects is one of the available methods to improve image quality. This paper deals with the saturation enhancement of blue sky to increase the preference of scenery images by introd...
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Reproduction of more pleasing colors for natural objects is one of the available methods to improve image quality. This paper deals with the saturation enhancement of blue sky to increase the preference of scenery images by introducing the saturation enhancement factor determined by an average saturation of the whole sky region and the weight using the relative pixel position as well as original saturation.
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The quality of visual media is critically impacted by low illumination and the presence of airborne particulates, leading to challenges in brightness balance, color saturation, and texture clarity which are detrimental to various ...
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The quality of visual media is critically impacted by low illumination and the presence of airborne particulates, leading to challenges in brightness balance, color saturation, and texture clarity which are detrimental to various applications in image processing and computer vision. Addressing these challenges, this study introduces a novel image enhancement algorithm that significantly improves the quality of degraded images. Our proposed method, the multi-channel phase activation and multi-constraint dark channel prior (MMDCP), leverages an innovative approach by integrating the phase-adjusted Gaussian kernel function for brightness channel optimization in the Fourier transform frequency domain. This optimization is enhanced through the application of a saturated dark channel prior, achieving simultaneous brightness enhancement and color fidelity. Furthermore, we refine the dark channel prior deblurring algorithm by incorporating intensity, brightness, and color constraints to correct overexposure issues and color offsets in the reconstructed images. The efficacy of the MMDCP algorithm is demonstrated through extensive experimentation, comparing it against six contemporary image enhancement algorithms using two types of objective indicators and subjective assessments across four public datasets. The MMDCP algorithm consistently outperforms the existing methods, with a notable average improvement of 20% in PSNR and 19.6% in SSIM metrics, substantiating its superiority in enhancing brightness, detail, and color accuracy. This study’s results underline the MMDCP algorithm’s robustness and versatility in improving image quality across various conditions, including daytime, nighttime, indoor, and outdoor settings.
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Modern techniques for medical diagnostics and therapy in keyhole surgery scenarios as well as technical inspection make use of flexible endoscopes. Their characteristic bendable image conductor consists of a very limited number of...
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Modern techniques for medical diagnostics and therapy in keyhole surgery scenarios as well as technical inspection make use of flexible endoscopes. Their characteristic bendable image conductor consists of a very limited number of coated fibers, which leads to so-called comb structure. This effect has a negative impact on further image processing steps such as feature tracking because these overlaid image structures are wrongly detected as image features. With respect to these tasks, we propose an automatic approach to generate optimal spectral filter masks for enhancement of fiberscopic images. We apply the Nyquist–Shannon sampling theorem to the spectrum of fiberscopically acquired images to obtain parameters for optimal filter mask calculation. This can be done automatically and independently of scale and resolution of the image conductor as well as type and resolution of the image sensor. We designed and verified simple rotation invariant masks as well as star-shaped rotation variant masks that contain information about orientation between the fiberscope and sensor. A subjective survey among experts between different modes of filtering certified the best results to the adapted star-shaped mask for high-quality glass fiberscopes. We furthermore define an objective metric to evaluate the results of different filter approaches, which verifies the results of the subjective survey. The proposed approach enables the automated reduction of fiberscopic comb structure. It is adaptive to arbitrary endoscope and sensor combinations. The results give the prospect of a large field of possible applications to reduce fiberscopic structure both for visual optimization in clinical environments and for further digital imaging tasks.
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Images captured in backlit conditions (i.e., backlit images) often have a vast difference in lightness between bright and dark areas. In such a dark area in an image, the visibility becomes extremely low, making it indistinct to r...
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Images captured in backlit conditions (i.e., backlit images) often have a vast difference in lightness between bright and dark areas. In such a dark area in an image, the visibility becomes extremely low, making it indistinct to recognize the subject. Sufficient image quality cannot be obtained by simply applying a general image enhancement method to such a backlit image. Many methods specializing in improving the image quality of backlit images have been proposed to cope with this problem. Although these methods can effectively improve dark areas' visibility compared to general image enhancement methods, the enhancement process causes artifacts in bright areas. In this paper, we propose a single backlit image enhancement method that effectively improves only the visibility of dark areas while suppressing over-enhancement and artifacts. In the proposed method, the lightness of the output image is calculated by the weighted sum of the input lightness image and the enhanced lightness image based on a weight map. The enhanced lightness image is calculated by alpha-blending two lightness-converted images obtained by gamma conversion and histogram equalization of the input lightness image. The weight map is calculated based on edge-preserving smoothing with a guided filter of a binarized input lightness image obtained using Otsu's method. The experiment shows the proposed method's effectiveness by quantitatively and qualitatively comparing conventional image enhancement methods and the proposed method using various backlit images.
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In this study, an image enhancement algorithm based on the modified histogram clipping scheme using a difference of histogram bins (MCDHB) has been proposed. The core idea of the proposed method is to ascertain the difference betw...
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In this study, an image enhancement algorithm based on the modified histogram clipping scheme using a difference of histogram bins (MCDHB) has been proposed. The core idea of the proposed method is to ascertain the difference between the number of pixels' in histogram bins of an input image and that of the traditional histogram equalised (HE) image. The calculated difference of each bin is partitioned into different blocks based on range criteria. The proposed algorithm can be attested as a global HE approach and mainly focuses on maintaining peaks in the histogram. The proposed MCDHB framework provides a good trade-off among contrast enhancement, shape of histogram, detailed information, and natural colour. Furthermore, the MCDHB framework is also incorporated with gamma correction for further improvement. The subjective and objective assessment confirms that both the proposed techniques can efficiently enhance the images, in a better way than those produced by classical techniques.
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The problem of creating artifact-free upscaled images appearing sharp and natural to the human observer is probably more interesting and less trivial than it may appear. The solution to the problem, often referred to also as “sin...
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The problem of creating artifact-free upscaled images appearing sharp and natural to the human observer is probably more interesting and less trivial than it may appear. The solution to the problem, often referred to also as “single-image super-resolution,” is related both to the statistical relationship between low-resolution and high-resolution image sampling and to the human perception of image quality. In many practical applications, simple linear or cubic interpolation algorithms are applied for this task, but the results obtained are not really satisfactory, being affected by relevant artifacts like blurring and jaggies. Several methods have been proposed to obtain better results, involving simple heuristics, edge modeling, or statistical learning. The most powerful ones, however, present a high computational complexity and are not suitable for real-time applications, while fast methods, even if edge adaptive, are not able to provide artifacts-free images. In this paper, we describe a new upscaling method (iterative curvature-based interpolation) based on a two-step grid filling and an iterative correction of the interpolated pixels obtained by minimizing an objective function depending on the second-order directional derivatives of the image intensity. We show that the constraints used to derive the function are related with those applied in another well-known interpolation method, providing good results but computationally heavy (i.e., new edge-directed interpolation (NEDI) . The high quality of the images enlarged with the new method is demonstrated with objective and subjective tests, while the computation time is reduced of one to two orders of magnitude with respect to NEDI so that we were able, using a graphics processing unit implementation based on the nVidia Compute Unified Device Architecture technology, to obtain real-time performances.
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