Correction of Simple Contrast Loss in Color Images

Oakley, John P.; Bu, Hong

doi:10.1109/tip.2006.887736

Cited by 117 publications

(42 citation statements)

References 35 publications

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“…They study the local structure of the image to infer the presence of haze, and try to compensate for this effect, e.g. [16] or [17]. A fusion of white balanced and contrast enhanced versions of the input hazy image has also been proposed [18].…”

Section: Introductionmentioning

confidence: 99%

Fusion-based Variational Image Dehazing

Galdrán

Vázquez-Corral

Pardo

et al. 2016

IEEE Signal Process. Lett.

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Abstract-We propose a novel image dehazing technique based on the minimization of two energy functionals and a fusion scheme to combine the output of both optimizations. The proposed Fusion-based Variational Image Dehazing (FVID) method is a spatially varying image enhancement process that first minimizes a previously proposed variational formulation that maximizes contrast and saturation on the hazy input. The iterates produced by this minimization are kept, and a second energy that shrinks faster intensity values of well-contrasted regions is minimized, allowing to generate a set of Difference-of-Saturations (DiffSat) maps by observing the shrinking rate. The iterates produced in the first minimization are then fused with these DiffSat maps to produce a haze-free version of the degraded input. The FVID method does not rely on a physical model from which to estimate a depth map, nor it needs a training stage on a database of human-labeled examples. Experimental results on a wide set of hazy images demonstrate that FVID better preserves the image structure on close-by regions that are less affected by fog, and it successfully compares with other current methods in the task of removing haze degradation from far-away regions.

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Section: Introductionmentioning

confidence: 99%

Fusion-based Variational Image Dehazing

Galdrán

Vázquez-Corral

Pardo

et al. 2016

IEEE Signal Process. Lett.

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“…Single image dehazing is highly underconstrained and illposed in the sense that the local transmission that depends on the scene depth for homogeneous atmosphere needs to be estimated. Tan [9] developed a dehazing model using the Markov random field framework, under a weak assumption that airlight in the atmospheric scattering model is constant [10]. In another study Fattal [11] estimated the albedo and transmission light, assuming that shading and transmission are locally uncorrelated.…”

Section: Introductionmentioning

confidence: 99%

An Image Dehazing Model considering Multiplicative Noise and Sensor Blur

Jidesh

Bini

2014

Journal of Computational Engineering

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A restoration model considering the data-dependent multiplicative noise, shift-invariant blur, and haze has been introduced in this paper. The proposed strategy adopts a two-step model to perform a single image dehazing under the blurred and noisy observations. The first step uses the well-known dark channel prior method to estimate the transmission of the medium and atmospheric light that signifies the global color of the haze and dehaze the images. The second step performs denoising and deblurring under a Gamma distributed noise setup and a linear blurring artefact. The restoration under the above mentioned setup has quite a few applications in satellite and long-distant telescopic imaging systems, where the captured images are noisy due to atmospheric pressure turbulence and hazy due to the presence of atmospheric dust formation; further they are blurred due to the common device artefacts. The proposed strategy is tested using a large amount of available image-sets and the performance of the model is analysed in detail in the results section.

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“…It stated that it was essential to enhance and process images according to image features. In 2007, Oakley and Hong 11 published a paper about a method of contrast correction in color images, especially for fog images. Pu et al also studied the detection of weak texture information by using a fractional differential.…”

Section: Introductionmentioning

confidence: 99%

Road identification in aerial images on fractional differential and one-pass ridge edge detection

Yang

Wang

2014

J. Appl. Remote Sens

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Abstract. A method for detecting and identifying thin and vague roads in aerial images is proposed. The method first smooths an image by using a Gaussian filter, then uses a fractional differential operator to enhance the image for sharpening roads, then applies a one-pass ridge edge detection algorithm to roughly detect the roads, and finally utilizes a number of post functions to accurately identify roads. For each detected point in an aerial road image, the new ridge detection algorithm detects if it is a candidate for the ridge edge points by searching through four different directions. After that, the extracted segments of lines/curves are smoothed and their gaps are linked according to preset thresholds of lengths and directions, and the noisy lines are removed based on the rules of the curve length and shape information. If roads are thick, the image can be shrunk to a road with a width less than six pixels, then the detection result for the course resolution image is mapped into the original image to accurately re-identify the road. In experiments, by comparison to traditional methods, the studied method can have better detection results for thin and vague roads, which are difficult to detect with traditional algorithms.

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Correction of Simple Contrast Loss in Color Images

Cited by 117 publications

References 35 publications

Fusion-based Variational Image Dehazing

Fusion-based Variational Image Dehazing

An Image Dehazing Model considering Multiplicative Noise and Sensor Blur

Road identification in aerial images on fractional differential and one-pass ridge edge detection

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