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Data hiding methods involve embedding secret messages into cover objects to enable covert communication in a way that is difficult to detect. In data hiding methods based on image interpolation, the image size is reduced and then enlarged through interpolation, followed by the embedding of secret data into the newly generated pixels. A general improving approach for embedding secret messages is proposed. The approach may be regarded a general model for enhancing the data embedding capacity of various existing image interpolation-based data hiding methods. This enhancement is achieved by expanding the range of pixel values available for embedding secret messages, removing the limitations of many existing methods, where the range is restricted to powers of two to facilitate the direct embedding of bit-based messages. This improvement is accomplished through the application of multiple-based number conversion to the secret message data. The method converts the message bits into a multiple-based number and uses an algorithm to embed each digit of this number into an individual pixel, thereby enhancing the message embedding efficiency, as proved by a theorem derived in this study. The proposed improvement method has been tested through experiments on three well-known image interpolation-based data hiding methods. The results show that the proposed method can enhance the three data embedding rates by approximately 14%, 13%, and 10%, respectively, create stego-images with good quality, and resist RS steganalysis attacks. These experimental results indicate that the use of the multiple-based number conversion technique to improve the three interpolation-based methods for embedding secret messages increases the number of message bits embedded in the images. For many image interpolation-based data hiding methods, which use power-of-two pixel-value ranges for message embedding, other than the three tested ones, the proposed improvement method is also expected to be effective for enhancing their data embedding capabilities.

3D reconstruction technology is gradually applied to underwater scenes, which has become a crucial research direction for human ocean exploration and exploitation. However, due to the complexity of the underwater environment, the number of high-quality underwater images acquired by underwater robots is limited and cannot meet the requirements of 3D reconstruction. Therefore, this paper proposes an adaptive 3D reconstruction algorithm for underwater targets. We apply the frame interpolation technique to underwater 3D reconstruction, an unprecedented technical attempt. In this paper, we design a single-stage large-angle span underwater image interpolation model, which has an excellent enhancement effect on degraded underwater 2D images compared with other methods. Current methods make it challenging to balance the relationship between feature information acquisition and underwater image quality improvement. In this paper, an optimized cascaded feature pyramid scheme and an adaptive bidirectional optical flow estimation algorithm based on underwater NRIQA metrics are proposed and applied to the proposed model to solve the above problems. The intermediate image output from the model improves the image quality and retains the detailed information. Experiments show that the method proposed in this paper outperforms other methods when dealing with several typical degradation types of underwater images. In underwater 3D reconstruction, the intermediate image generated by the model is used as input instead of the degraded image to obtain a denser 3D point cloud and better visualization. Our method is instructive to the problem of acquiring underwater high-quality target images and underwater 3D reconstruction.

Interpolation from a Color Filter Array (CFA) is the most common method for obtaining full color image data. Its success relies on the smart combination of a CFA and a demosaicing algorithm. Demosaicing on the one hand has been extensively studied. Algorithmic development in the past 20 years ranges from simple linear interpolation to modern neural-network-based (NN) approaches that encode the prior knowledge of millions of training images to fill in missing data in an inconspicious way. CFA design, on the other hand, is less well studied, although still recognized to strongly impact demosaicing performance. This is because demosaicing algorithms are typically limited to one particular CFA pattern, impeding straightforward CFA comparison. This is starting to change with newer classes of demosaicing that may be considered generic or CFA-agnostic. In this study, by comparing performance of two state-of-the-art generic algorithms, we evaluate the potential of modern CFA-demosaicing. We test the hypothesis that, with the increasing power of NN-based demosaicing, the influence of optimal CFA design on system performance decreases. This hypothesis is supported with the experimental results. Such a finding would herald the possibility of relaxing CFA requirements, providing more freedom in the CFA design choice and producing high-quality cameras.

In this paper, we propose a novel interpolation and a new reversible data hiding scheme for upscaling the original image and hiding secret data into the upscaled/interpolated image. This data hiding scheme considers the characteristics of the human visual system while embedding the secret data so that the existence of the secret data is not detected even after embedding a large amount of secret data. The proposed hiding scheme first divides pixel intensity ranges into groups and then adaptively embeds the secret data bits into the pixels based on the pixel intensity values. Therefore, the proposed scheme is able to maintain the visual quality of the stego-image. Experimental results show that the achieved PSNR by the proposed interpolation method is more than 30 dB for all the test images. Further, the results prove that the proposed data hiding scheme has superior performance than all the existing interpolation-based data hiding schemes.

Digital imaging is used in variety of applications. Together with the improvements in artificial intelligence and its sub-fields, improving computer vision methods to address inter- and multi-disciplinary problems is possible. Especially in medical applications, there are significant improvements related to imaging in the last decades. Digital image interpolation is a key operation in digital image processing where there are no sufficient samples during the acquisition process. Using the available samples in hand, digital interpolation techniques are predicting the missing samples. The paper addresses the problem of digital image interpolation and proposes a novel algorithm using multiplicative calculus. The main contribution of the paper is the application of multiplicative Lagrange interpolation to accomplish image interpolation task. The proposed method is tested on several datasets, and the results are comparable to the state-of-the-art methods. The paper presents encouraging results to the literature, and the proposed method is open for further improvements.

Capturing all-in-focus images with 3D scenes is typically a challenging task due to depth of field limitations, and various multi-focus image fusion methods have been employed to generate all-in-focus images. However, existing methods have difficulty simultaneously achieving real-time and superior fusion performance. In this paper, we propose a region- and pixel-based method that can recognize the focus and defocus regions or pixels by the neighborhood information in the source images. The proposed method can obtain satisfactory fusion results and achieve improved real-time performance. First, a convolutional neural network (CNN)-based classifier generates a coarse region-based trimap quickly, which contains focus, defocus and boundary regions. Then, precise fine-tuning is implemented at the boundary regions to address the boundary pixels that are difficult to discriminate by existing methods. Based on a public database, a high-quality dataset is constructed that provides abundant precise pixel-level labels, so that the proposed method can accurately classify the regions and pixels without artifacts. Furthermore, an image interpolation method called NEAREST_Gaussian is proposed to improve the recognition ability at the boundary. Experimental results show that the proposed method outperforms other state-of-the-art methods in visual perception and object metrics. Additionally, the proposed method has 80% improved to the conventional CNN-based methods.

Image interpolation is an essential process for image processing and computer graphics in wide applications to medical imaging. For image interpolation used in medical diagnosis, the two-dimensional (2D) to three-dimensional (3D) transformation can significantly reduce human error, leading to better decisions. This research proposes the type-2 fuzzy neural networks method which is a hybrid of the fuzzy logic and neural networks as well as recurrent type-2 fuzzy neural networks (RT2FNNs) for advancing a novel 2D to 3D strategy. The ability of the proposed methods in the approximation of the function for image interpolation is investigated. The results report that both proposed methods are reliable for medical diagnosis. However, the RT2FNN model outperforms the type-2 fuzzy neural networks model. The average squares error for the recurrent network and the typical network reported 0.016 and 0.025, respectively. On the other hand, the number of fuzzy rules for the recurrent network and the typical network reported 16 and 22, respectively.

Image super-resolution (SR) is a classic problem of image processing. This paper proposes a self-learning interpolation method (SLIM) based on a single image by combining grid feature mapping with binary decision tree, which is not only transparent as the interpolation-based methods, but also achieves comparable performance as the learning-based methods. Firstly, it downsamples the given image I LR to obtain its low–low-resolution image I LLR , which is used to obtain sample data for the self-learning interpolation algorithm for enlarging I LLR to get I LR . Secondly, it provides a structural feature classification method to divide all of the samples into several groups, such that each class of I LLR is mapped to a matrix of coefficients for calculating the values of the pixels of I LR . The image I LR is approximated by executing the decision tree to refine the corresponding mapping matrix. Finally, the resulting high-resolution image I HR is obtained from the given image I LR by using the mapping matrixes. Experimental results show that SLIM achieves more smooth edges and better details on subjective vision than prevailing SR methods, and it is a transparent one but achieves comparable performances on PSNR and SSIM with the learning-based methods, while it outperforms the interpolation-based methods. It means that SLIM is both transparent and efficient and has much better subjective vision than other SR methods.

Rapidly and accurately constructing high genus geological models with serious intrusion and erosion can effectively help analyse engineering geological conditions. Modelling high genus geological bodies using a stratigraphic interface is complex and subjective. The method based on the voxel model has problems, such as redundancy of the voxel model, which makes it challenging to continue to exploit. Therefore, this study presents an automatic modelling method for high genus geological bodies. After converting the borehole model into a voxel image, accurate interpolation results were obtained using image completion and super-resolution algorithms. First, bilateral filtering and the Otsu method were used to improve the EdgeConnect completion algorithm and obtain preliminary interpolation results. Second, the residual-in-residual dense block (RRDB) and U-Net structure were used to optimise the network structure in the deep plug-and-play super-resolution generative adversarial network (DPSRGAN) to obtain a smoother and more accurate interpolation image. Finally, a voxel model simplification method was proposed to convert the redundant voxel model into a geological mesh model. Engineering practice has shown that the interpolation accuracy of this method is 88.4%. The number of model mesh surfaces is reduced by 91.7%. Compared with the geostatistical kriging interpolation, non-uniform rational B-spline (NURBS), radial basis function (RBF) and deep convolutional generative adversarial network (DCGAN) methods, the accuracy was improved by 19.2%, 3.7%, 11.0% and 20.9%, respectively, and the modelling time was shortened by 93.8% compared with manual modelling.