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Author

Ming Dai

Bio: Ming Dai is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Filter (signal processing) & Color constancy. The author has an hindex of 1, co-authored 1 publications receiving 52 citations.

Papers
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Journal ArticleDOI
TL;DR: A learning strategy to select the optimal parameters of the nonlinear stretching by optimizing a novel image quality measurement, named as the Modified Contrast-Naturalness-Colorfulness (MCNC) function, which employs a more effective objective criterion and can better agree with human visual perception.

67 citations


Cited by
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Journal ArticleDOI
TL;DR: This paper presents a novel method for underwater image enhancement inspired by the Retinex framework, which simulates the human visual system and utilizes the combination of the bilateral filter and trilateral filter on the three channels of the image in CIELAB color space according to the characteristics of each channel.

244 citations

Journal ArticleDOI
TL;DR: An image fusion-based algorithm to enhance the performance and robustness of image dehazing is proposed, based on a set of gamma-corrected underexposed images, and pixelwise weight maps are constructed by analyzing both global and local exposedness to guide the fusion process.
Abstract: Poor weather conditions, such as fog, haze, and mist, cause visibility degradation in captured images. Existing imaging devices lack the ability to effectively and efficiently mitigate the visibility degradation caused by poor weather conditions in real time. Image depth information is used to eliminate hazy effects by using existing physical model-based approaches. However, the imprecise depth information always affects dehazing performance. This article proposes an image fusion-based algorithm to enhance the performance and robustness of image dehazing. Based on a set of gamma-corrected underexposed images, pixelwise weight maps are constructed by analyzing both global and local exposedness to guide the fusion process. The spatial-dependence of luminance of the fused image is reduced, and its color saturation is balanced in the dehazing process. The performance of the proposed solution is confirmed in both theoretical analysis and comparative experiments.

150 citations

Journal ArticleDOI
TL;DR: 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.
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-saturation (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 nearby regions that are less affected by fog, and it is successfully compared with other current methods in the task of removing haze degradation from faraway regions.

94 citations

Journal ArticleDOI
TL;DR: To improve contrast and restore color for underwater images without suffering from insufficient details and color cast, this paper proposes a fusion algorithm for different color spaces based on co-ordination spaces.
Abstract: To improve contrast and restore color for underwater images without suffering from insufficient details and color cast, this paper proposes a fusion algorithm for different color spaces based on co...

81 citations

Journal ArticleDOI
TL;DR: Results on real low-contrast optical remote sensing images demonstrate that the proposed image enhancement scheme outperforms the state-of-the-arts in terms of brightness improvement, contrast enhancement, and detail preservation.

66 citations