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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]

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]

• A novel high-fidelity reversible embedding scheme using prediction-error value ordering and multiple embedding is proposed. • A prediction-error is computed by considering the difference between the top two maximum (minimum) prediction-errorswithin each image block. • A multiple-embedding mechanism is proposed to adaptive determine the number of modified prediction-errors. Prediction-error expansion (PEE) is the most widely investigated reversible data hiding (RDH) framework. However, the performance of PEE can be further improved since the image redundancy has not fully exploited yet. To enlarge the embedding capacity and optimize the embedding performance of RDH, in this paper, a novel high-fidelity reversible embedding scheme using prediction-error value ordering and multiple embedding is proposed. Incorporating with PEE, the inter-correlation of prediction-errors is exploited by ordering the prediction-errors values, and multiple to-be-modified prediction-errors within each image block are adaptive determined. Specifically, firstly, a newly defined prediction-error is computed by considering the difference between the top two maximum (minimum) prediction-errors within each image block. Then, these prediction-errors are modified for expansion embedding. Moreover, a multiple-embedding mechanism is introduced to adaptive determine the number of modified prediction-errors. Finally, extensive experiments are conducted, and it is experimentally verified that the proposed method is superior to than some state-of-the-art RDH works. A benchmark result is that, for the standard Lena image, a PSNR as high as 62.01 dB can be derived for an embedding capacity of 10,000 bits. [ABSTRACT FROM AUTHOR]