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The MMSE criterion requires statistical characterization of the given image.

We traverse all the SIFT features in the given image.

Then, noise in a given image is not considerably masked.

The optimal value of k depends on the given image.

Neural networks have shown good results for detecting a certain pattern in a given image.

Specifically, we first apply a conventional feature extraction method to a given image.

The chapter begins with the concrete problem of simultaneously denoising, decomposing, and deblurring a given image.

From image forensics point of view, revealing the processing history is essential to the content authentication of a given image.

However, both methods in[11, 12] can only reveal the compression history of a given image, and cannot detect the local tampered region in a given image.

Since the GLCM can describe the textural characteristics clearly in a given image, we use the GLCM to extract the edge areas from the given image.

The method used allowed a fast and automatic selection of most sperm heads for a given image with high precision.

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