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Screen photos often suffer from moiré patterns, which significantly affect their visual quality. Although many deep learning-based methods for removing moiré patterns have been proposed, they fail to recover images with complex textures and heavy moiré patterns. Here, we focus on text images with heavy moiré patterns and propose a new demoiré approach, incorporating frequency-domain peak filtering and spatial-domain visual quality enhancement. We find that the content of the text image mainly lies in the central region, whereas the moiré pattern lies in the peak region, in the frequency domain. Based on this observation, a peak-filtering algorithm and a central region recovery strategy are proposed to accurately locate and remove moiré patterns while preserving the text parts. In addition, to further remove the noisy background and paint the missing text parts, an image enhancement algorithm utilising the Otsu method is developed. Extensive experimental results show that the proposed method significantly removes severe moiré patterns from images with better visual quality and lower time cost compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In current research on reversible visible watermarking algorithm, the original visible watermark image plays an important auxiliary role, and some algorithms also entirely depend on it to restore host image without any distortion. Therefore, in order to realize semi-blind reversible visible watermarking algorithm, the conventional reversible watermarking algorithm is used to embed compressed visible watermark image data into non-visible-watermarked region of host image. However, the amount of compressed image data obtained by conventional image compression algorithm is relatively large. Therefore, a method based on vectorization compression for the visible watermark image is proposed in this paper. Firstly, it performs edge detection on visible watermark image to obtain a discrete points set |$\Gamma $| of vector contour curve. Then, the discrete points in |$\Gamma $| are simplified by improved Douglas–Peucker algorithm, after that it obtains compressed vector contour data of visible watermark image. In addition, a reversible visible watermarking algorithm based on convolutional relief and image alpha fusion is proposed, which realizes reversible embedding of visible watermark image and lossless restoration of host image. The experimental results show that the proposed vectorization compression method has more advantages than traditional image compression algorithms, which greatly reduces the storage space of visible watermark image with high fidelity. Additionally, the embedded watermarking image has translucent 3D relief effect, and the fusion of host image and visible watermark image becomes more natural and harmonious. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the robustness, visibility, and transparency of the existing visible watermarking technologies are difficult to achieve a balance, this paper proposes an adaptive embedding method for visible watermarking. Firstly, the salient region of the host image is detected based on superpixel detection. Secondly, the flat region with relatively low complexity is selected as the embedding region in the nonsalient region of the host image. Then, the watermarking strength is adaptively calculated by considering the gray distribution and image texture complexity of the embedding region. Finally, the visible watermark image is adaptively embedded into the host image with slight adjustment by just noticeable difference (JND) coefficient. The experimental results show that our proposed method improves the robustness of visible watermarking technology and greatly reduces the risk of malicious removal of visible watermark image. Meanwhile, a good balance between the visibility and transparency of the visible watermark image is achieved, which has the advantages of high security and ideal visual effect. [ABSTRACT FROM AUTHOR]

Visible watermarking is one of the most direct and effective copyright protection methods for digital images and videos, but it faces the problems of easy detection of watermarking location and watermarking removal attacks. It requires that the visible watermarking not only can not affect visual quality of original digital products but also can resist accidental editing and malicious attacks. In this paper, an adaptive region selection method for visible watermark embedding is proposed. First, an improved region selection method based on the model of saliency-based visual attention (MSVA) is used to more accurately distinguish salient and non-salient regions of host image; then, the non-salient regions of image are divided into sub-blocks to calculate texture complexity respectively; finally, a moderate image block is selected as the embedding region of visible watermark image according to the distribution of image texture complexity. The experimental results show that the proposed method can identify saliency regions more accurately, thus ensuring that the important contents of host image will not be occluded. The adaptive selection of embedding region based on image complexity not only ensures the recognizability and aesthetics of visible watermark embedding effect but also takes into account the security of watermark embedding, and especially reduces the risk of batch removal attacks. [ABSTRACT FROM AUTHOR]

Recently, a method based on multiple histograms modification (MHM) is proposed for reversible data hiding (RDH), in which a sequence of prediction-error histograms are generated and two expansion bins are selected in each histogram for expansion embedding. However, although efficient, it only chooses a single pair of expansion bins which limits the embedding capacity. On the other hand, the exhaustive expansion-bin-selection procedure in MHM takes huge computation time, so that it cannot be extended for high capacity RDH. In order to overcome the aforementioned drawbacks, an optimal RDH scheme based on MHM for high capacity embedding is proposed in this paper. First, to improve the embedding capacity, instead of a single pair of expansion bins, multiple pairs of expansion bins are utilized for each histogram, and the multiple-expansion-bin-selection for optimal embedding is formulated as an optimization problem. Then, unlike the exhaustive searching way used in MHM, a computationally efficient algorithm is proposed to solve the optimization problem, so that the optimal expansion bins can be adaptively determined to optimize the embedding performance. By the proposed approach, high embedding capacity can be achieved with good marked image quality, and the experimental results show that it is better than the original MHM and some other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Pixel-value-ordering (PVO) is an efficient technique of reversible data hiding (RDH). By PVO, the maximum and minimum in each cover image block are first predicted and then modified to embed data. Actually, many PVO-based methods are essentially based on high-dimensional histogram modification. For these methods, a two-dimensional (2D) prediction-error histogram (PEH) is first generated and then modified based on a 2D mapping. However, these methods have two drawbacks. On one hand, the generated 2D PEH is irregular so that it is difficult to design suitable histogram modification strategy. On the other hand, the employed 2D mapping is empirically designed, and thus the embedding performance is far from optimal. Based on these considerations, a new PVO-based RDH scheme is proposed in this paper. By considering both pixel value orders and pixel locations, a new predictor is proposed so that the generated 2D PEH is regular in shape and suitable for reversible embedding. Moreover, instead of manually designing 2D mappings, to optimize the embedding performance, a self-learning mechanism is proposed to adaptively select the 2D mapping according to the image content. With the new predictor and the self-learning mechanism for 2D mapping selection, the proposed method works well with a good marked image quality, e.g., the PSNR of the image Lena is as high as 61.53 dB for an embedding capacity of 10 000 bits. Besides, compared with some state-of-the-art RDH methods, the superiority of the proposed method is experimentally verified. [ABSTRACT FROM AUTHOR]

Reversible visible watermarking (RVW) is an active copyright protection mechanism. It not only transparently superimposes copyright patterns on specific positions of digital images or video frames to declare the copyright ownership information, but also completely erases the visible watermark image and thus enables restoring the original host image without any distortion. However, existing RVW algorithms mostly construct the reversible mapping mechanism for a specific visible watermarking scheme, which is not versatile. Hence, we propose a generic RVW framework to accommodate various visible watermarking schemes. In particular, we obtain a reconstruction data packet-the compressed difference image between the watermarked image and the original host image, which is embedded into the watermarked image via any conventional reversible data hiding method to facilitate the blind recovery of the host image. The key is to achieve compact compression of the difference image for efficient embedding of the reconstruction data packet. To this end, we propose regularized Graph Fourier Transform (GFT) coding, where the difference image is smoothed via the graph Laplacian regularizer for more efficient compression and then encoded by multi-resolution GFTs in an approximately optimal manner. Experimental results show that the proposed framework has much better versatility than state-of-the-art methods. Due to the small amount of auxiliary information to be embedded, the visual quality of the watermarked image is also higher.