A New Double Color Image Watermarking Algorithm Based on the SVD and Arnold Scrambling

We propose a new image watermarking scheme based on the real SVD and Arnold scrambling to embed a color watermarking image into a color host image. Before embedding watermark, the color watermark image W with size of M×M is scrambled by Arnold transformation to obtain a meaningless image W~. Then, the color host image A with size of N×N is divided into nonoverlapping N/M×N/M pixel blocks. In each (i,j) pixel block Ai,j, we form a real matrix Ci,j with the red, green, and blue components of Ai,j and perform the SVD of Ci,j. We then replace the three smallest singular values of Ci,j by the red, green, and blue values of W~ij with scaling factor, to form a new watermarked host image A~ij. With the reserve procedure, we can extract the watermark from the watermarked host image. In the process of the algorithm, we only need to perform real number algebra operations, which have very low computational complexity and are more effective than the one using the quaternion SVD of color image

Quaternion tensor ring decomposition and application for color image inpainting

In recent years, tensor networks have emerged as powerful tools for solving large-scale optimization problems. One of the most promising tensor networks is the tensor ring (TR) decomposition, which achieves circular dimensional permutation invariance in the model through the utilization of the trace operation and equitable treatment of the latent cores. On the other hand, more recently, quaternions have gained significant attention and have been widely utilized in color image processing tasks due to their effectiveness in encoding color pixels. Therefore, in this paper, we propose the quaternion tensor ring (QTR) decomposition, which inherits the powerful and generalized representation abilities of the TR decomposition while leveraging the advantages of quaternions for color pixel representation. In addition to providing the definition of QTR decomposition and an algorithm for learning the QTR format, this paper also proposes a low-rank quaternion tensor completion (LRQTC) model and its algorithm for color image inpainting based on the QTR decomposition. Finally, extensive experiments on color image inpainting demonstrate that the proposed QTLRC method is highly competitive

MDLatLRR: A novel decomposition method for infrared and visible image fusion

Image decomposition is crucial for many image processing tasks, as it allows to extract salient features from source images. A good image decomposition method could lead to a better performance, especially in image fusion tasks. We propose a multi-level image decomposition method based on latent low-rank representation(LatLRR), which is called MDLatLRR. This decomposition method is applicable to many image processing fields. In this paper, we focus on the image fusion task. We develop a novel image fusion framework based on MDLatLRR, which is used to decompose source images into detail parts(salient features) and base parts. A nuclear-norm based fusion strategy is used to fuse the detail parts, and the base parts are fused by an averaging strategy. Compared with other state-of-the-art fusion methods, the proposed algorithm exhibits better fusion performance in both subjective and objective evaluation.Comment: IEEE Trans. Image Processing 2020, 14 pages, 17 figures, 3 table

Robust color image watermarking using Discrete Wavelet Transform, Discrete Cosine Transform and Cat Face Transform

The primary concern in color image watermarking is to have an effective watermarking method that can be robust against common image processing attacks such as JPEG compression, rotation, sharpening, blurring, and salt and pepper attacks for copyright protection purposes. This research examined the existing color image watermarking methods to identify their strengths and weaknesses, and then proposed a new method and the best embedding place in the host image to enhance and overcome the existing gap in the color image watermarking methods. This research proposed a new robust color image watermarking method using Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Cat Face Transform. In this method, both host and watermark images decomposed into three color channels: red, green, and blue. The second level DWT was applied to each color channel of the host image. DWT decomposed the image into four sub-band coefficients: Low-pass filter in the row, Low-pass filter in the column (LL) signifies approximation coefficient, High-pass filter in the row, Low-pass filter in the column (HL) signifies horizontal coefficient, Low-pass filter in the row, High-pass filter in the column (LH) signifies vertical coefficient, and High-pass filter in the row, High-pass filter in the column (HH) signifies diagonal coefficient. Then, HL2 and LH2 were chosen as the embedding places to improve the robustness and security, and they were divided into 4×4 non-overlapping blocks, then DCT was applied on each block. DCT turned a signal into the frequency domain, which is effective in image processing, specifically in JPEG compression due to good performance. On the other hand, the Cat Face Transform method with a private key was used to enhance the robustness of the proposed method by scrambling the watermark image before embedding. Finally, the second private key was used to embed the watermark in the host image. The results show enhanced robustness against common image processing attacks: JPEG compression (3.37%), applied 2% salt and pepper (0.4%), applied 10% salt and pepper (2%), applied 1.0 radius sharpening (0.01%), applied 1.0 radius blurring (8.1%), and can withstand rotation attack. In sum, the proposed color image watermarking method indicates better robustness against common image processing attacks compared to other reviewed methods