Gradient domain color restoration of clipped highlights

Rouf, Mushfiqur; Lau, Cindy; Heidrich, Wolfgang

doi:10.1109/cvprw.2012.6239193

Cited by 7 publications

(3 citation statements)

References 38 publications

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“…In some of the proposed enhancement techniques, for example, luminance or chrominance gradients as well as RGB channel spatial variation patters were propagated from the well-exposed parts of the degraded images in a gradient domain and sRGB spaces. The methods mostly rely on the assumption of strong spatial and color channel correlations of natural image contents [5,6,7]. Other related methods utilized perceptual color spaces to adjust degraded perceptual attributes (such as chroma and hue) for color clipped pixels in ill-exposed image regions [8,9].…”

Section: Introductionmentioning

confidence: 99%

Content Fidelity of Deep Learning Methods for Clipping and Over-exposure Correction

Abebe¹

2021

lim

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Exposure problems, due to standard camera sensor limitations, often lead to image quality degradations such as loss of details and change in color appearance. The quality degradations further hiders the performances of imaging and computer vision applications. Therefore, the reconstruction and enhancement of uderand over-exposed images is essential for various applications. Accordingly, an increasing number of conventional and deep learning reconstruction approaches have been introduced in recent years. Most conventional methods follow color imaging pipeline, which strongly emphasize on the reconstructed color and content accuracy. The deep learning (DL) approaches have conversely shown stronger capability on recovering lost details. However, the design of most DL architectures and objective functions don’t take color fidelity into consideration and, hence, the analysis of existing DL methods with respect to color and content fidelity will be pertinent. Accordingly, this work presents performance evaluation and results of recent DL based overexposure reconstruction solutions. For the evaluation, various datasets from related research domains were merged and two generative adversarial networks (GAN) based models were additionally adopted for tone mapping application scenario. Overall results show various limitations, mainly for severely over-exposed contents, and a promising potential for DL approaches, GAN, to reconstruct details and appearance.

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Section: Introductionmentioning

confidence: 99%

Content Fidelity of Deep Learning Methods for Clipping and Over-exposure Correction

Abebe¹

2021

lim

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“…To correct over-exposure, they recovered lightness and color by optimizing respective energy functions. Rouf et al [12] estimated hue values in the saturated regions, and then restored the color values using estimated hue value and gradient information in the nonsaturated channel(s) of a saturated region. Mansour et al's method [13] used a hierarchical windowing algorithm, which divides an image into nonoverlapping (2 l+1 )×(2 l+1 ) blocks according to the hierarchical level l, to detect and correct a saturated region.…”

Section: Introductionmentioning

confidence: 99%

Correction of Saturated Regions in RGB Color Space

Ju¹,

Park²

2016

IJCGA

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In a digital image, color representation of a digital image sensor is limited to a narrow dynamic range. Especially, when extremely bright light is captured, the original color of a scene is saturated to the maximum value, up to which a digital image sensor can represent the color. This paper proposes an algorithm that corrects the color in a saturated region, where the original color is distorted and lost. For natural correction, i.e., to minimize the artifacts near the boundary of a saturated region, the proposed method uses the weighted sum of color value(s) in the saturated color channel(s) of neighborhood of saturated regions. In determining the weight of each pixel, saturation, hue, and color values are used with the certainty map. Using the certainty map, the proposed method can reliably distinguish the unsaturated and already desaturated neighboring pixels from the remaining pixels. Then, the proposed correction method computes the weight function using saturation, hue, and color values. Therefore, the proposed algorithm can get reliable corrected colors. Comparison of experimental results of the proposed and existing correction methods shows the effectiveness of the proposed saturated region correction method in the view of natural color restoration.

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“…The OER is then detected by thresholding the over-exposed map. In addition, some methods [ 11 – 13 ] estimate the OER by thresholding each RGB color channel separately. Thus, in these methods, the detected OER is much larger than that obtained by the previously described detection methods.…”

Section: Introductionmentioning

confidence: 99%

A New Human Perception-Based Over-Exposure Detection Method for Color Images

Yoon

Byun

Lee

et al. 2014

Sensors

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To correct an over-exposure within an image, the over-exposed region (OER) must first be detected. Detecting the OER accurately has a significant effect on the performance of the over-exposure correction. However, the results of conventional OER detection methods, which generally use the brightness and color information of each pixel, often deviate from the actual OER perceived by the human eye. To overcome this problem, in this paper, we propose a novel method for detecting the perceived OER more accurately. Based on the observation that recognizing the OER in an image is dependent on the saturation sensitivity of the human visual system (HVS), we detect the OER by thresholding the saturation value of each pixel. Here, a function of the proposed method, which is designed based on the results of a subjective evaluation on the saturation sensitivity of the HVS, adaptively determines the saturation threshold value using the color and the perceived brightness of each pixel. Experimental results demonstrate that the proposed method accurately detects the perceived OER, and furthermore, the over-exposure correction can be improved by adopting the proposed OER detection method.

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Gradient domain color restoration of clipped highlights

Cited by 7 publications

References 38 publications

Content Fidelity of Deep Learning Methods for Clipping and Over-exposure Correction

Content Fidelity of Deep Learning Methods for Clipping and Over-exposure Correction

Correction of Saturated Regions in RGB Color Space

A New Human Perception-Based Over-Exposure Detection Method for Color Images

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