Lightweight Multi-Scale Asymmetric Attention Network for Image Super-Resolution

Recently, with the development of convolutional neural networks, single-image super-resolution (SISR) has achieved better performance. However, the practical application of image super-resolution is limited by a large number of parameters and calculations. In this work, we present a lightweight multi-scale asymmetric attention network (MAAN), which consists of a coarse-grained feature block (CFB), fine-grained feature blocks (FFBs), and a reconstruction block (RB). MAAN adopts multiple paths to facilitate information flow and accomplish a better balance of performance and parameters. Specifically, the FFB applies a multi-scale attention residual block (MARB) to capture richer features by exploiting the pixel-to-pixel correlation feature. The asymmetric multi-weights attention blocks (AMABs) in MARB are designed to obtain the attention maps for improving SISR efficiency and readiness. Extensive experimental results show that our method has comparable performance with fewer parameters than the current advanced lightweight SISR.

Simplifying the hardware requirements for fast neural EIT of peripheral nerves

Objective: The main objective of this study was to assess the feasibility of lowering the hardware requirements for fast neural EIT in order to support the distribution of this technique. Specifically, the feasibility of replacing the commercial modules present in the existing high-end setup with compact and cheap customized circuitry was assessed. Approach: Nerve EIT imaging was performed on rat sciatic nerves with both our standard ScouseTom setup and a customized version in which commercial benchtop current sources were replaced by custom circuitry. Electrophysiological data and images collected in the same experimental conditions with the two setups were compared. Data from the customized setup was subject to a down-sampling analysis to simulate the use of a recording module with lower specifications. Main results: Compound action potentials (573±287µV and 487±279µV, p=0.28) and impedance changes (36±14µV and 31±16µV, p=0.49) did not differ significantly when measured using commercial high-end current sources or our custom circuitry, respectively. Images reconstructed from both setups showed neglibile (<1voxel, i.e. 40µm) difference in peak location and a high degree of correlation (R2=0.97). When down-sampling from 24 to 16 bits ADC resolution and from 100KHz to 50KHz sampling frequency, signal-to-noise ratio showed acceptable decrease (<-20%), and no meaningful image quality loss was detected (peak location difference <1voxel, pixel-by-pixel correlation R2=0.99). Significance: The technology developed for this study greatly reduces the cost and size of a fast neural EIT setup without impacting quality and thus promotes the adoption of this technique by the neuroscience research community.

Chaotic system constructed by product trigonometric function and polynomial and applied to color image encryption

In the applied research of nonlinear system, the low degree of chaos in the dynamical system leads to the limitation of using the chaos method to solve some practical problems. In this paper, we use the product trigonometric function and ternary polynomial to build a dynamical system, which has strong chaotic characteristics. The dynamical system is constructed by two product trigonometric functions and a ternary linear equation, and its chaotic properties are verified by bifurcation diagrams, Lyapunov exponents, fractal dimensions, etc. The system has many parameters and large parameter intervals and is not prone to cycles. The conditions for the non-divergence of this system are given by mathematical derivation, and it is found that the linear part of the system can be replaced by an arbitrary ternary polynomial system and still not diverge, and the bifurcation diagram is drawn to verify it. Finally, the chaotic sequence is distributed more uniformly in the value domain space by adding the modulo operation. Then, the bit matrix of multiple images is directly permuted by the above system, and the experiment confirms that the histogram, information entropy, and pixel correlation of its encrypted images are satisfactory, as well as a very large key space.

Spectral Graph Wavelet Based Image Steganography Using SVD and Arnold Transform

Steganography is the process of concealing sensitive information within cover medium. This study offers an efficient and safe innovative image steganography approach based on graph signal processing (GSP). To scramble the secret image, Arnold cat map transform is used, then Spectral graph wavelet is used to change the cover and scrambled secret image, followed by singular vector decomposition (SVD) of the modified cover image. To create the stego image, an alpha blending process is used. To produce the stego image, GSP-based synthesis is used. By maintaining the inter-pixel correlation, GSP improves the visual quality of the produced stego image. The effects of image processing attacks on the suggested approach are examined. The investigational results and assessment indicate that the proposed steganography scheme is more efficient and robust in terms of quality measures. The quality of stego image is evaluated in respect of PSNR, NCC, SC and AD performance metrics.

Visual Encryption Using Block based Scrambling Followed by Affine Encryption Technique

This article reports the Block based cipher concept followed by the affine cipher technique. The Image considered was grouped into squared (16, 32 and 64) pixel blocks then each column was shifted by specific values. These values were randomly generated prime numbers and worked as the key for scrambling. These images were investigated for their quality of scrambling using histogram and adjacent pixel correlation. The adjacent pixel correlations for 16, 32 and 64 pixel-based ciphered images were found as 0.7907, 0.7292, and 0.4783 respectively. The analysis gave the information that the level of scrambling was not satisfactory, therefore; the affine cipher technique was applied to each of the images. These images were converted into the matrix format and each element was transformed using the affine cipher. This transformed matrix is again converted inform from the image to visualize. The Histogram and adjacent pixel correction for these images were much improved.

Intra-Block Correlation Based Reversible Data Hiding in Encrypted Images Using Parametric Binary Tree Labeling

In this paper, a high capacity reversible data hiding technique using a parametric binary tree labeling scheme is proposed. The proposed parametric binary tree labeling scheme is used to label a plaintext image’s pixels as two different categories, regular pixels and irregular pixels, through a symmetric or asymmetric process. Regular pixels are only utilized for secret payload embedding whereas irregular pixels are not utilized. The proposed technique efficiently exploits intra-block correlation, based on the prediction mean of the block by symmetry or asymmetry. Further, the proposed method utilizes blocks that are selected for their pixel correlation rather than exploiting all the blocks for secret payload embedding. In addition, the proposed scheme enhances the encryption performance by employing standard encryption techniques, unlike other block based reversible data hiding in encrypted images. Experimental results show that the proposed technique maximizes the embedding rate in comparison to state-of-the-art reversible data hiding in encrypted images, while preserving privacy of the original contents.

Medical Image Protection Algorithm Based on Deoxyribonucleic Acid Chain of Dynamic Length

Current image encryption algorithms have various deficiencies in effectively protecting medical images with large storage capacity and high pixel correlation. This article proposed a new image protection algorithm based on the deoxyribonucleic acid chain of dynamic length, which achieved image encryption by DNA dynamic coding, generation of DNA dynamic chain, and dynamic operation of row chain and column chain. First, the original image is encoded dynamically according to the binary bit from a pixel, and the DNA sequence matrix is scrambled. Second, DNA sequence matrices are dynamically segmented into DNA chains of different lengths. After that, row and column deletion operation and transposition operation of DNA dynamic chain are carried out, respectively, which made DNA chain matrix double shuffle. Finally, the encrypted image is got after recombining DNA chains of different lengths. The proposed algorithm was tested on a list of medical images. Results showed that the proposed algorithm showed excellent security performance, and it is immune to noise attack, occlusion attack, and all common cryptographic attacks.

Empirical Analysis of Robust Chaotic Maps for Image Encryption

The rate of transferring data in the form of text, image, video or audio over an open network has drastically increased. As these are carried out in highly sophisticated fields of medicine, military and banking, security becomes important. Inorder to enhance security for transmission, encryption algorithms play a vital role. So as to enhance the proficiency of the existing encryption methods and for stronger anti attack abilities, chaotic based cryptography is essential. Chaotic based encryption has advantages of being sensitive to initial conditions and control parameters. Images have features like bulk data capacity and high inter pixel correlation. Transmission of such medical data should be highly confidential, integral and authorized. Hence chaos-based image encryption is an efficient way of fast and high-quality image encryption. It has features like good speed, complexity, highly secure, reasonable computational overhead and power. In this paper a comprehensive analysis and an evaluation regarding the capabilities of different discrete time domain chaotic maps were carried out on a proposed image encryption method. The experimental results show high efficiency for the proposed image encryption technique.