A fast color image enhancement algorithm based on Max Intensity Channel
TL;DR: This extension of image enhancement techniques based on the retinex theory achieves simultaneous dynamic range modification, color consistency, and lightness rendition without multi-scale Gaussian filtering which has a certain halo effect.
Abstract: In this paper, we extend image enhancement techniques based on the retinex theory imitating human visual perception of scenes containing high illumination variations. This extension achieves simultaneous dynamic range modification, color consistency, and lightness rendition without multi-scale Gaussian filtering which has a certain halo effect. The reflection component is analyzed based on the illumination and reflection imaging model. A new prior named Max Intensity Channel (MIC) is implemented assuming that the reflections of some points in the scene are very high in at least one color channel. Using this prior, the illumination of the scene is obtained directly by performing a gray-scale closing operation and a fast cross-bilateral filtering on the MIC of the input color image. Consequently, the reflection component of each RGB color channel can be determined from the illumination and reflection imaging model. The proposed algorithm estimates the illumination component which is relatively smooth and maintains the edge details in different regions. A satisfactory color rendition is achieved for a class of images that do not satisfy the gray-world assumption implicit to the theoretical foundation of the retinex. Experiments are carried out to compare the new method with several spatial and transform domain methods. Our results indicate that the new method is superior in enhancement applications, improves computation speed, and performs well for images with high illumination variations than other methods. Further comparisons of images from National Aeronautics and Space Administration and a wearable camera eButton have shown a high performance of the new method with better color restoration and preservation of image details.
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TL;DR: The cultivation of crops, conservation of plants, restoration of landscape, and management of soil are the phases incorporated in agriculture and horticulture.
Abstract: The cultivation of crops, conservation of plants, restoration of landscape, and management of soil are the phases incorporated in agriculture and horticulture During the cultivation and conservati
41 citations
TL;DR: A novel single-image based dehazing framework is proposed to remove haze artifacts from images through local atmospheric light estimation using a novel strategy based on a physical model where the extreme intensity of each RGB pixel is used to define an initial atmospheric veil.
39 citations
Cites background from "A fast color image enhancement algo..."
...Illumination of one patch can be estimated as the maximum values among RGB channels (max-RGB), and then the maximal reflectance of RGB channel in this patch will be normalized to 1, which is a very important hypothesis inspired by [28]....
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TL;DR: A Moore neighborhood-based gradient profile prior is designed and developed to efficiently estimate the transmission map and atmospheric veil and shows the supremacy of the proposed technique in removing haze from still images when compared with several existing techniques.
Abstract: Removing the haze from still images is a challenging issue. Dark Channel Prior (DCP) based dehazing techniques have been used to remove haze from still images. However, it produces poor results when image objects are inherently similar to the airlight and no shadow is cast on them. To eliminate this problem, a Moore neighborhood-based gradient profile prior is designed and developed to efficiently estimate the transmission map and atmospheric veil. The transmission map is also refined by developing a local activity-tuned anisotropic diffusion based filter. Afterward, image restoration is performed using the estimated transmission function. Thus, the proposed technique has an ability to remove haze from still images in an effective manner. The performance of the proposed technique is compared with recently developed seven dehazing techniques over synthetic and real-life hazy images. The experimental results depict the supremacy of the proposed technique in removing haze from still images when compared with several existing techniques. It also reveals that the restored image has little or no artifacts.
37 citations
TL;DR: Computed tomography approach is extensively utilized in clinical diagnoses, however, X-ray residue in human body may introduce somatic damage such as cancer.
Abstract: Computed tomography (CT) approach is extensively utilized in clinical diagnoses. However, X-ray residue in human body may introduce somatic damage such as cancer. Owing to radiation risk, research ...
32 citations
TL;DR: The experimental results demonstrate that the proposed approach is able to remove the different types of fog from roadside images as well as significantly improve the image’s visibility and reveals that the restored image has little or no artifacts.
Abstract: Poor weather conditions are responsible for most of the road accidents year in and year out. Poor weather conditions, such as fog, degrade the visibility of objects. Thus, it becomes difficult for drivers to identify the vehicles in a foggy environment. The dark channel prior (DCP)-based defogging techniques have been found to be an efficient way to remove fog from road images. However, it produces poor results when image objects are inherently similar to airlight and no shadow is cast on them. To eliminate this problem, a modified restoration model-based DCP is developed to remove the fog from road images. The transmission map is also refined by developing a gain coefficient-based trilateral filter. Thus, the proposed technique has an ability to remove fog from road images in an effective manner. The proposed technique is compared with seven well-known defogging techniques on two benchmark foggy images datasets and five real-time foggy images. The experimental results demonstrate that the proposed approach is able to remove the different types of fog from roadside images as well as significantly improve the image’s visibility. It also reveals that the restored image has little or no artifacts.
31 citations
Cites background from "A fast color image enhancement algo..."
...Thus, the normalized image is also confined into a range [0, 1] with linear stretch strategy as provided below: EQ-TARGET;temp:intralink-;e008;326;369 FmgðjÞ G∞ ¼ AmgðjÞTmpðjÞ G∞ þ AvlðjÞ: (8) The atmospheric veil [AvlðjÞ] is based upon two conditions....
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...By addressing the transmission map as T̃mpðjÞ and atmospheric veil as ÃvlðjÞ, the minimal function is used on color channels and local window as follows: EQ-TARGET;temp:intralink-;e009;326;203 min y∈WðjÞ min c∈ðR;G;BÞ FcmgðyÞ Gc∞ ¼ T̃mpðjÞ min y∈WðjÞ min c∈ðR;G;BÞ AcmgðyÞ Gc∞ þ ÃvlðjÞ: (9) Based on the conditions of DCP, dark channel of AmgðjÞ is near to 0, which can be provided under EQ-TARGET;temp:intralink-;e010;326;105FDmgðjÞ ¼ min y∈WðjÞ min c∈ðR;G;BÞ ½AcmgðyÞ ¼ 0: (10) Journal of Electronic Imaging 013004-3 Jan∕Feb 2018 • Vol. 27(1) As Gc∞ ∈ R≥0, therefore, Eq....
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...1⁄4 T̃mpðjÞ min y∈WðjÞ min c∈ðR;G;BÞ AmgðyÞ G∞ þ ÃvlðjÞ: (9)...
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...(9) and (11), atmospheric veil is re-evaluated as below:...
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...(1) and (3), the imaging model can be redefined as follows: EQ-TARGET;temp:intralink-;e006;326;551FmgðjÞ ¼ AmgðjÞTmpðjÞ þ G∞AvlðjÞ: (6) The restoration model is normalized by splitting the global atmospheric light (Gc∞) in most color channel independently, which will be evaluated as follows: EQ-TARGET;temp:intralink-;e007;326;486 FcmgðjÞ Gc∞ ¼ A c mgðjÞTmpðjÞ Gc∞ þ AvlðjÞ: (7) The value of G∞ is not optimum in real-foggy image....
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References
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TL;DR: A simple but effective image prior - dark channel prior to remove haze from a single input image is proposed, based on a key observation - most local patches in haze-free outdoor images contain some pixels which have very low intensities in at least one color channel.
Abstract: In this paper, we propose a simple but effective image prior-dark channel prior to remove haze from a single input image. The dark channel prior is a kind of statistics of outdoor haze-free images. It is based on a key observation-most local patches in outdoor haze-free images contain some pixels whose intensity is very low in at least one color channel. Using this prior with the haze imaging model, we can directly estimate the thickness of the haze and recover a high-quality haze-free image. Results on a variety of hazy images demonstrate the power of the proposed prior. Moreover, a high-quality depth map can also be obtained as a byproduct of haze removal.
3,668 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
TL;DR: In this article, the authors proposed a new signal processing analysis of the bilateral filter, which complements the recent studies that analyzed it as a PDE or as a robust statistical estimator.
Abstract: The bilateral filter is a nonlinear filter that smoothes a signal while preserving strong edges. It has demonstrated great effectiveness for a variety of problems in computer vision and computer graphics, and fast versions have been proposed. Unfortunately, little is known about the accuracy of such accelerations. In this paper, we propose a new signal-processing analysis of the bilateral filter which complements the recent studies that analyzed it as a PDE or as a robust statistical estimator. The key to our analysis is to express the filter in a higher-dimensional space where the signal intensity is added to the original domain dimensions. Importantly, this signal-processing perspective allows us to develop a novel bilateral filtering acceleration using downsampling in space and intensity. This affords a principled expression of accuracy in terms of bandwidth and sampling. The bilateral filter can be expressed as linear convolutions in this augmented space followed by two simple nonlinearities. This allows us to derive criteria for downsampling the key operations and achieving important acceleration of the bilateral filter. We show that, for the same running time, our method is more accurate than previous acceleration techniques. Typically, we are able to process a 2 megapixel image using our acceleration technique in less than a second, and have the result be visually similar to the exact computation that takes several tens of minutes. The acceleration is most effective with large spatial kernels. Furthermore, this approach extends naturally to color images and cross bilateral filtering.
789 citations
TL;DR: The present paper describes a relatively simple alternative technique for the computation of the designator in retinex theory and reports the general operational effectiveness of the new technique, including the competence, not possessed by earlier algorithms, for generating Mach bands.
Abstract: Accepting the first postulate of the retinex theory of color vision that there are three independent lightness-determining mechanisms (one for long waves, one for middle waves, and one for short waves), each operative with less than a millisecond exposure and each served by its own retinal pigment, a basic task of retinex theory becomes the determination of the nature of these mechanisms. Earlier references proposed several workable algorithms. [Land, E. H. (1959) Proc. Natl. Acad. Sci. USA 45, 115-129; Land, E. H. (1959) Proc. Natl. Acad. Sci. USA 45, 636-644; Land, E. H. (1983) Proc. Natl. Acad. Sci. USA 80, 5163-5169; Land, E. H. & McCann, J. J. (1971) J. Opt. Soc. Am. 61, 1-11; Land, E. H. (1986) Vision Res. 26, 7-21.] The present paper describes a relatively simple alternative technique for the computation of the designator in retinex theory and reports the general operational effectiveness of the new technique, including the competence, not possessed by earlier algorithms, for generating Mach bands.
613 citations
TL;DR: This work develops the Retinex computation into a full scale automatic image enhancement algorithm—the multiscale RetineX with color restoration (MSRCR)—which com- bines color constancy with local contrast/lightness enhancement to transform digital images into renditions that approach the realism of direct scene observation.
Abstract: There has been a revivification of interest in the Retinex computation in the last six or seven years, especially in its use for image enhancement. In his last published concept (1986) for a Ret- inex computation, Land introduced a center/surround spatial form, which was inspired by the receptive field structures of neurophysi- ology. With this as our starting point, we develop the Retinex con- cept into a full scale automatic image enhancement algorithm—the multiscale Retinex with color restoration (MSRCR)—which com- bines color constancy with local contrast/lightness enhancement to transform digital images into renditions that approach the realism of direct scene observation. Recently, we have been exploring the fun- damental scientific questions raised by this form of image process- ing. 1. Is the linear representation of digital images adequate in visual terms in capturing the wide scene dynamic range? 2. Can visual quality measures using the MSRCR be developed? 3. Is there a canonical, i.e., statistically ideal, visual image? The answers to these questions can serve as the basis for automating visual as- sessment schemes, which, in turn, are a primitive first step in bring- ing visual intelligence to computers. © 2004 SPIE and IS&T.
598 citations