Tieyong Zeng

Efficient Reversible Watermarking Based on Adaptive Prediction-Error Expansion and Pixel Selection

Prediction-error expansion (PEE) is an important technique of reversible watermarking which can embed large payloads into digital images with low distortion. In this paper, the PEE technique is further investigated and an efficient reversible watermarking scheme is proposed, by incorporating in PEE two new strategies, namely, adaptive embedding and pixel selection. Unlike conventional PEE which embeds data uniformly, we propose to adaptively embed 1 or 2 bits into expandable pixel according to the local complexity. This avoids expanding pixels with large prediction-errors, and thus, it reduces embedding impact by decreasing the maximum modification to pixel values. Meanwhile, adaptive PEE allows very large payload in a single embedding pass, and it improves the capacity limit of conventional PEE. We also propose to select pixels of smooth area for data embedding and leave rough pixels unchanged. In this way, compared with conventional PEE, a more sharply distributed prediction-error histogram is obtained and a better visual quality of watermarked image is observed. With these improvements, our method outperforms conventional PEE. Its superiority over other state-of-the-art methods is also demonstrated experimentally.

General Framework to Histogram-Shifting-Based Reversible Data Hiding

Histogram shifting (HS) is a useful technique of reversible data hiding (RDH). With HS-based RDH, high capacity and low distortion can be achieved efficiently. In this paper, we revisit the HS technique and present a general framework to construct HS-based RDH. By the proposed framework, one can get a RDH algorithm by simply designing the so-called shifting and embedding functions. Moreover, by taking specific shifting and embedding functions, we show that several RDH algorithms reported in the literature are special cases of this general construction. In addition, two novel and efficient RDH algorithms are also introduced to further demonstrate the universality and applicability of our framework. It is expected that more efficient RDH algorithms can be devised according to the proposed framework by carefully designing the shifting and embedding functions.

A Generalization of LSB Matching

Recently, a significant improvement of the well-known least significant bit (LSB) matching steganography has been proposed, reducing the changes to the cover image for the same amount of embedded secret data. When the embedding rate is 1, this method decreases the expected number of modification per pixel (ENMPP) from 0.5 to 0.375. In this letter, we propose the so-called generalized LSB matching (G-LSB-M) scheme, which generalizes this method and LSB matching. The lower bound of ENMPP for G-LSB-M is investigated, and a construction of G-LSB-M is presented by using the sum and difference covering set of finite cyclic group. Compared with the previous works, we show that the suitable G-LSB-M can further reduce the ENMPP and lead to more secure steganographic schemes. Experimental results illustrate clearly the better resistance to steganalysis of G-LSB-M.

A Multiphase Image Segmentation Method Based on Fuzzy Region Competition

The goal of this paper is to develop a multiphase image segmentation method based on fuzzy region competition. A new variational functional with constraints is proposed by introducing fuzzy membership functions which represent several different regions in an image. The existence of a minimizer of this functional is established. We propose three methods for handling the constraints of membership functions in the minimization. We also add auxiliary variables to approximate the membership functions in the functional such that Chambolles fast dual projection method can be used. An alternate minimization method can be employed to find the solution, in which the region parameters and the membership functions have closed form solutions. Numerical examples using grayscale and color images are given to demonstrate the effectiveness of the proposed methods.

A Two-Stage Image Segmentation Method Using a Convex Variant of the Mumford-Shah Model and Thresholding ∗

The Mumford--Shah model is one of the most important image segmentation models and has been studied extensively in the last twenty years. In this paper, we propose a two-stage segmentation method based on the Mumford--Shah model. The first stage of our method is to find a smooth solution

A Convex Variational Model for Restoring Blurred Images with Multiplicative Noise

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Multiplicative Noise Removal via a Learned Dictionary

to a convex variant of the Mumford--Shah model. Once

Reducing Artifacts in JPEG Decompression Via a Learned Dictionary

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A Dictionary Learning Approach for Poisson Image Deblurring

is obtained, then in the second stage the segmentation is done by thresholding

Detecting LSB matching by applying calibration technique for difference image

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