Non-square Image Research Articles

Nonequal-length image encryption based on bitplane chaotic mapping

In recent years, extensive research has focused on encryption algorithms for square images, with relatively little attention given to nonsquare images. This paper introduces a novel encryption algorithm tailored for nonequal length images, integrating bit-plane chaotic mapping and Arnold transformation. To effectively implement the algorithm, the plain image is initially transformed into two equal-sized binary sequences. A new diffusion strategy is then introduced to mutually diffuse these sequences, followed by the use of a chaotic map to control the swapping of binary elements between them, enabling permutation of bits across different bitplanes. Finally, the positional information of the image is scrambled using the Arnold transform, resulting in the generation of the encrypted image. By utilizing nonequal Arnold transformation parameters and the initial value of the Lorenz chaotic map as keys, the transmission of keys is simplified, and the cryptosystem gains infinite key space to resist brute force attacks. Experimental results and security analysis confirm the effectiveness of the proposed quantum image encryption algorithm in encrypting nonsquare images, demonstrating good performance in terms of nonstatistical properties, key sensitivity, and robustness. Furthermore, simulation experiments based on Qiskit successfully validate the correctness and feasibility of the quantum image encryption algorithm.

Iris Data Compression Based on Hexa-Data Coding

Iris research is focused on developing techniques for identifying and locating relevant biometric features, accurate segmentation and efficient computation while lending themselves to compression methods. Most iris segmentation methods are based on complex modelling of traits and characteristics which, in turn, reduce the effectiveness of the system being used as a real time system. This paper introduces a novel parameterized technique for iris segmentation. The method is based on a number of steps starting from converting grayscale eye image to a bit plane representation, selection of the most significant bit planes followed by a parameterization of the iris location resulting in an accurate segmentation of the iris from the original image. A lossless Hexadata encoding method is then applied to the data, which is based on reducing each set of six data items to a single encoded value. The tested results achieved acceptable saving bytes performance for the 21 iris square images of sizes 256x256 pixels which is about 22.4 KB on average with 0.79 sec decompression average time, with high saving bytes performance for 2 iris non-square images of sizes 640x480/2048x1536 that reached 76KB/2.2 sec, 1630 KB/4.71 sec respectively, Finally, the proposed promising techniques standard lossless JPEG2000 compression techniques with reduction about 1.2 and more in KB saving that implicitly demonstrating the power and efficiency of the suggested lossless biometric techniques.

HRInversion: High-Resolution GAN Inversion for Cross-Domain Image Synthesis

We investigate GAN inversion problems of using pre-trained GANs to reconstruct real images. Recent methods for such problems typically employ a VGG perceptual loss to measure the difference between images. While the perceptual loss has achieved remarkable success in various computer vision tasks, it may cause unpleasant artifacts and is sensitive to changes in input scale. This paper delivers an important message that algorithm details are crucial for achieving satisfying performance. In particular, we propose two important but undervalued design principles: (i) not down-sampling the input of the perceptual loss to avoid high-frequency artifacts; and (ii) calculating the perceptual loss using convolutional features which are robust to scale. Integrating these designs derives the proposed framework, HRInversion, that achieves superior performance in reconstructing image details. We validate the effectiveness of HRInversion on a cross-domain image synthesis task and propose a post-processing approach named local style optimization (LSO) to synthesize clean and controllable stylized images. For the evaluation of the cross-domain images, we introduce a metric named ID retrieval which captures the similarity of face identities of stylized images to content images. We also test HRInversion on non-square images. Equipped with implicit neural representation, HRInversion applies to ultra-high resolution images with more than 10 million pixels. Furthermore, we show applications of style transfer and 3D-aware GAN inversion, paving the way for extending the application range of HRInversion.

A novel 4D chaotic system with nonhyperbolic hyperbolic shape equilibrium points: Analysis, circuit implementation and color image encryption

This paper presents a new 4D chaotic system by extending a 3D system to investigate chaotic systems2 with hidden attractors further and explore their engineering applications in color image encryption. The system has hyperbolic shape equilibriums and the equilibriums are nonhyperbolic which is interesting and rarely mentioned before. The system has been investigated deeply by phase plot, time series, the largest Lyapunov exponent, bifurcation diagram, Poincaré map and 0–1 test. A coexisting hidden attractor has been found in this new system and the circuit simulation model of the system is built by Power Simulation (PSIM). Circuit simulation results are consistent with results of numerical simulations which verify its dynamic behavior further. Then, by combining the generalized Arnold transform with the new chaotic system, a new color image encryption algorithm has been proposed which has large key space and can encrypt either square images or nonsquare images with different size. Finally, the results of encryption experiment and security analysis prove the effectiveness of the algorithm.

An Improved Multithreshold Segmentation Algorithm Based on Graph Cuts Applicable for Irregular Image

In order to realize the multithreshold segmentation of images, an improved segmentation algorithm based on graph cut theory using artificial bee colony is proposed. A new weight function based on gray level and the location of pixels is constructed in this paper to calculate the probability that each pixel belongs to the same region. On this basis, a new cost function is reconstructed that can use both square and nonsquare images. Then the optimal threshold of the image is obtained through searching for the minimum value of the cost function using artificial bee colony algorithm. In this paper, public dataset for segmentation and widely used images were measured separately. Experimental results show that the algorithm proposed in this paper can achieve larger Information Entropy (IE), higher Peak Signal to Noise Ratio (PSNR), higher Structural Similarity Index (SSIM), smaller Root Mean Squared Error (RMSE), and shorter time than other image segmentation algorithms.

Image encryption scheme based on a novel fractional order compound chaotic attractor

Many image encryption algorithms have too small key space to prevent exhaust attacks. Based on a novel compound chaotic system, an algorithm with expanded key space is proposed to aim at the problem. Firstly, an adaptive method is provided to design switching controllers during creating a novel compound chaos. Secondly, general Arnold transform is used to realise pixel scrambling technology. Finally, the novel fractional compound chaos is applied in pixel diffusion to improve security of the image encryption algorithm. In numerical simulation, classical colour image, gray image, binary image and non-square image are encrypted sufficiently to identify the encryption algorithm. Both theoretical analysis and experimental results show that the algorithm has larger key space and is suitable for different types of digital images, and encryption image can resist some kinds of external attacks.

MDGHM-SURF: A robust local image descriptor based on modified discrete Gaussian–Hermite moment

This paper proposes a novel family of local feature descriptors, a variant of the speed up robust features (SURF) descriptor, which is capable of demonstrably better performance. The conventional SURF descriptor is an efficient implementation of the SIFT descriptor. Although the SURF descriptor can represent the nature of the underlying image pattern, it is still sensitive to more complicated deformations such as large viewpoint and rotation changes. To solve this problem, our family of descriptors, called MDGHM-SURF, is based on the modified discrete Gaussian–Hermite moment (MDGHM), which devises a movable mask to represent the local feature information of non-square images. Whereas conventional SURF uses first-order derivatives, MDGHM-SURF uses MDGHM, which offers more feature information than first-order derivative-based local descriptors such as SURF and SIFT. Consequently, by redefining the conventional SURF descriptor using MDGHM, MDGHM-SURF can extract more distinctive features than conventional SURF. The results of evaluations conducted with six types of deformations indicate that our proposed method outperforms the matching accuracy of other SURF related algorithms.

A New Chaotic Image Scrambling Scheme Based on Spatial Bit-Level Map

Image scrambling is an important technique in digital image encryption and digital image watermarking. This paper proposes a new image scrambling algorithm based on chaotic sequence and spatial bit-level map. First, I used Logistic chaotic sequences to rearrange digital image pixels into one-dimensional (1-D) sequence, then the value of each pixel in the sequence was decomposed into k-bits which constituted a two-dimensional (2-D) bit matrix. Second, the spatial bit-level map was performed in the 2-D bit matrix to generate pixels with new values. Finally, the scrambling image was generated according to these pixels. This algorithm can scramble image based on the pixel’s position and the value and their corresponding probability. The experimental results show that this scrambling algorithm possesses perfect confusion properties and it can resist various attacks. Also it can apply to non-square image.

Enhanced SIFT Descriptor Based on Modified Discrete Gaussian-Hermite Moment

The discrete Gaussian-Hermite moment (DGHM) is a global feature representation method that can be applied to square images. We propose a modified DGHM (MDGHM) method and an MDGHM-based scale-invariant feature transform (MDGHM-SIFT) descriptor. In the MDGHM, we devise a movable mask to represent the local features of a non-square image. The complete set of non-square image features are then represented by the summation of all MDGHMs. We also propose to apply an accumulated MDGHM using multi-order derivatives to obtain distinguishable feature information in the third stage of the SIFT. Finally, we calculate an MDGHM-based magnitude and an MDGHM-based orientation using the accumulated MDGHM. We carry out experiments using the proposed method with six kinds of deformations. The results show that the proposed method can be applied to non-square images without any image truncation and that it significantly outperforms the matching accuracy of other SIFT algorithms.

Fast computation of geometric moments using a symmetric kernel

This paper presents a novel set of geometric moments with symmetric kernel (SGM) obtained using an appropriate transformation of image coordinates. By using this image transformation, the computational complexity of geometric moments (GM) is reduced significantly through the embedded symmetry and separability properties. In addition, it minimizes the numerical instability problem that occurs in high order GM computation. The novelty of the method proposed in this paper lies in the transformation of GM kernel from interval [ 0 , ∞ ] to interval [ - 1 , 1 ] . The transformed GM monomials are symmetry at the origin of principal Cartesian coordinate axes and hence possess symmetrical property. The computational complexity of SGM is reduced significantly from order O ( N 4 ) using the original form of computation to order O ( N 3 ) for the proposed symmetry-separable approach. Experimental results show that the percentage of reduction in computation time of the proposed SGM over the original GM is very significant at about 75.0% and 50.0% for square and non-square images, respectively. Furthermore, the invariant properties of translation, scaling and rotation in Hu's moment invariants are maintained. The advantages of applying SGM over GM in Zernike moments computation in terms of efficient representation and computation have been shown through experimental results.