Ehsan Nezhadarya

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Gradient estimators are mostly designed to yield accurate and robust estimates of the gradient magnitude, not the gradient direction. This paper proposes a method for the accurate and robust estimation of both the gradient magnitude and direction. It robustly estimates the gradient in the x- and y-directions. The robustness against noise is achieved by(More)
—A successful approach to image quality assessment involves comparing the structural information between a distorted and its reference image. However, extracting structural information that is perceptually important to our visual system is a challenging task. This paper addresses this issue by employing a sparse representation-based approach and proposes a(More)
A new vector-wise scheme for the gradient estimation and edge detection in noisy color images is proposed. In color images, different types of noise may corrupt the image. To reduce the effects of noise in the gradient estimation, we introduce the RCMG-Median-Mean estimator. RCMG-Median-Mean is a combination of the robust color morphological gradient(More)
Many successful image quality metrics rely on the structural information in an image to assess its perceptual quality. Extracting the structural information that is perceptually meaningful to our visual system, however, is a challenging task. This paper proposes a new quality assessment metric that relies on a sparse modeling approach to learn the inherent(More)
This paper presents a fuzzy approach for contrast enhancement, based on two multi-scale transforms, namely wavelet and contourlet transforms. Separability and nondirectionality of conventional 2D wavelet transform, makes it unsuitable for sparsely representation of curve or line shaped image objects. On the other hand, the contourlet transform isa good(More)
a r t i c l e i n f o a b s t r a c t A transform that estimates the first and higher-order derivatives of images at multiple scales is proposed. The proposed transform, called Multi-Scale Derivative Transform (MSDT), is specially designed for image watermarking applications. To calculate the first and higher-order image derivatives, MSDT uses the detail(More)