An adaptive reversible data hiding method through autoregression is presented in this paper. In the proposed algorithm, we focus on the image pixel value prediction, which plays a key role in the data embedding process. Unlike conventional data hiding techniques, a threshold is adjusted for each image to divide all pixels into two regions: the smooth region and the texture region. Then the proposed algorithm optimally estimates the coefficients of autoregression model for pixel value prediction through least-squares minimization. The prediction error is adaptively minimized to achieve high prediction accuracy so that more redundancy in the image is exploited to achieve very high data embedding capacity while keeping the distortion low. Experimental results show that the proposed algorithm outperforms typical state-of-theart methods in general.

Although it has been recognized that different textual contents in an image need to be treated differently during accurate image interpolation, how to classify these contents well has been a difficult problem due to the inherent complexity in natural images. In this paper we propose an efficient image interpolation framework with a novel weighted surface approximation approach. The key is that the weighted mean squared error of the approximation can be converted to a continuously distributed probability of a pixel belonging to a local smooth region or a textural one, thus essentially making a soft pixel classification. In addition, the fitted local surface provides an estimate of the pixel value under the smooth region assumption. This estimate is then fused with the estimate from the texture region assumption using the previously obtained probability to yield the final estimate. Experimental results show that the proposed framework consistently improves over typical state-of-the-art methods in terms of interpolation accuracy while maintaining comparable computational complexity.

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Compared with the two other mainstream medical imaging methods, CT and MRI, the worst weakness of ultrasound imaging is poor resolution—the ability to resolve tiny variations in tissue structure or texture. Due to sound wave diffraction and the way imaging ultrasound signal is retrieved, a point target in object domain does not generate point image in image domain, but an image spot with sophisticate spot pattern and considerable spot size as functions of point target location relative to the transducer—the most direct cause of poor resolution. The mainstream techniques aimed at reducing the image spot, either by shortening the impulse signal or by sharpening the beam focusing, are unavoidably accompanied by the deteriorated sensitivity and depth of penetration. It is a common consent in medical ultrasound community that ultrasound imaging is very close to its theoretical resolution limit. This article presents a different approach we named E-mode imaging that uses a diffraction-theory-based spot pattern recognition technique to account for the effects of image spot pattern in image processing. With the same set of ultrasound data that B imaging is based on, E-mode imaging achieved five to ten times better resolution and diagnostic power in computer simulations.