Light fields (LFs) capture both angular and spatial information of light rays, providing an immersive and detailed representation of the visual world. However, the high dimensionality of LF data presents challenges for compression and transmission algorithms, which often introduce degradations that affect visual quality. To address this, we propose GCNN-LFIQA, a novel no-reference LF image quality assessment method that leverages the power of deep graph convolutional neural networks (GCNNs). The method employs a single-stream deep GCNN architecture to model the complex structural and geometric relationships within LF data, enabling accurate quality predictions. A key innovation of the proposed approach is its input preparation pipeline, which converts horizontal epipolar plane images into skeleton-based graph representations enriched with node-level features such as betweenness centrality. These graph representations serve as input to the GCNN, which predicts quality scores using a regression block. We evaluated GCNN-LFIQA on two widely used LF quality datasets, Win5-LID and LFDD, where it achieved high correlation values and outperformed other state-of-the-art methods. The proposed method demonstrates robustness, computational efficiency, and the potential to address the unique challenges of LF image quality assessment in real-world applications.
As a porous sandwich plate system, the honeycomb paperboard is widely used in cushioning packaging of products due to its energy-absorbing properties. The drop impact on a honeycomb paperboard with cell sizes of 4 mm, 6 mm, and 8 mm was simulated and experimented to investigate the paperboard’s energy absorption and deformation modes. The peak impact force of the impactor with velocity 5.47 m s−1, the dent depth of the honeycomb paperboard, and the absorbed energy were obtained. The peak forces corresponding to the core layer cell sizes of 4 mm, 6 mm, and 8 mm were 141.2 N, 108.7 N, and 97.7 N, and the absorbed energy values were 76%, 83%, and 84%, respectively. The model of honeycomb paperboard under drop impact was established. The plastic deformation and force distribution showed good agreement with experimental results, which verified the accuracy of the model. The results are expected to provide a reference for the lightweight structure and protective design of the honeycomb paperboard.
To boost the security of color image encryption algorithms and enlarge their key space, an encryption algorithm of color image based on cellular neural networks (CNNs) is proposed. The sequence produced by the 6D CNN system is segmented into two groups and combined at a specific ratio. The new chaotic sequence obtained is used as the key source for a 4D hyperchaos system. The key is selected based on the logical operation results of the plaintext pixel mean, and the final chaotic encryption sequences X and Y are obtained. Pixel scrambling, diffusion on each layer of R, G, B, and pixel value replacement encryption operations are performed on color images, which are encrypted as ciphertext images. The results of the experimental simulation demonstrate that the image encryption algorithm outlined in this paper possesses significant key space, robust sensitivity to both keys and plaintext, uniform distribution of ciphertext pixels, and a correlation coefficient near 0 among neighboring pixels. It is capable of effectively thwarting exhaustive attacks, statistical analysis attacks, and differential attacks, and produces a notable encryption impact on color images. It possesses specific utility in the realm of color image information security.
Image compression is an essential technology in image processing as it reduces video storage, which is increasingly popular. Deep learning-based image compression has made significant progress, surpassing traditional coding and decoding approaches in specific cases. Current methods employ autoencoders, typically consisting of convolutional neural networks, to map input images to lower-dimensional latent spaces for compression. However, these approaches often overlook low-frequency information, leading to sub-optimal compression performance. To address this challenge, this study proposed a novel image compression technique, Transformer and Convolutional Dual Channel Networks (TCDCN). This method extracts both edge detail and low-frequency information, achieving a balance between high and low-frequency compression. The study also utilized a variational autoencoder architecture with parallel stacked transformer and convolutional networks to create a compact representation of the input image through end-to-end training. This content-adaptive transform captured low-frequency information dynamically, leading to improved compression efficiency. Compared to the classic JPEG method, our model showed significant improvements in Bjontegaard Delta rate up to 19.12% and 18.65% on Kodak and CLIC test datasets, respectively. These improvements also surpassed the state-of-the-art solutions by notable margins of 0.47% and 0.74%, signifying a substantial enhancement in the image compression encoding efficiency. The results underscore the effectiveness of our approach in enhancing the capabilities of existing techniques, marking a significant step forward in the field of image compression.
In recent years, with the rapid development of digital printing technology, an increasing number of counterfeit products have entered the market. The anti-counterfeiting technique for QR Codes has been attracting increasing attention nowadays. There exist many image inpainting methods that can be applied in the image restoration field. Some image completion methods may restore coating QR Code images to a point where the covered digital number underneath is revealed even though the original coating QR Code has not been scratched off. In this paper, we extend the pluralistic image completion (PIC) method to scratched coating QR Code image restoration. Based on the binary characteristic of QR Codes, we present a specific type of deep learning model for scratched coating QR Code image completion. Experimental results demonstrate that the extended PIC is an effective approach to the restoration of scratched coating QR Code images.
Taking the Jurong Dongshan River as an illustrative case, we employed a Dajiang Phantom 4 RTK SE unmanned aerial vehicle (UAV) for single-lens tilt photography. Our investigation focused on examining the influence of varying flight altitudes (FA: 30 m, 60 m and 90 m) and the configuration of photo control points on two-dimentional (2D) or three-dimensional (3D) mapping accuracy of river embankments, slopes, and hydraulic structures, as well as the analysis of outcomes of 2D and 3D modeling under different FA conditions augmented with supplementary photographs. Our observations revealed that the longitude and elevation accuracy at 30 m FA were higher without photo control operations compared to those at 60 m and 90 m FA, and accuracy diminished as FA increased. Specifically, the longitude accuracy of the embankment photo control points exceeded that of the slope photo control points, whereas the elevation accuracy of the embankment photo control points was superior at FAs of 60 m and 90 m. The geographical location deviation of hydraulic structures (irrigation intake gates) in the 2D model was larger than that obtained in the 3D model. Notably, the incorporation of additional detailed photographs significantly augmented the modeling efficacy of UAV aerial survey data, especially in capturing intricate plant and slope details. It is recommended that the Phantom 4 RTK SE be used at FA of 90 m to establish a foundational channel model, along with capture of additional detailed photographs of crucial structures, slopes, etc., and obtaining geographic location information in 3D Models.
The preparation of active food packaging materials with both high gas barrier and antioxidant properties remains a great challenge. Hemicellulose is an abundant raw material with excellent oxygen barrier performance and biodegradability, and tea polyphenols (TPs) are natural antioxidants with the function of reducing water sensitivity of polysaccharide-based films. Herein, a synergistic strategy is reported for the preparation of active-controlled food packaging material by incorporating TP into the poly(vinyl alcohol) hemicellulose (PVA/HC) matrix. The obtained film with 5 wt% addition of TP exhibited the highest tensile strength of 41.09 MPa, which was increased by 84% than that of the PVA/HC film. Meanwhile, water vapor permeability was decreased by 25% to 7.39 × 10−12 g⋅cm/(cm2⋅s⋅Pa) compared to a PVA/HC film. Moreover, the addition of TP improved the thermal stability of the (PVA/HC/TP)n film. The outstanding packaging performances were attributed to the robust hydrogen bonding between TP and the PVA/HC matrix as demonstrated by Fourier transform infrared spectroscopy. The DPPH free radical scavenging of (PVA/HC/TP)15 films reached 87.99%, and the film also exhibited enhanced inhibition efficiency against the microorganism E. coli. Compared with the polyethylene plastic wrap, the film showed excellent food packaging applicability by preventing air oxidation of fresh-cut potato. This study sheds light on a design strategy for active packaging films using a natural substance and easily available biomass.
Deep learning (DL) has advanced computer-aided diagnosis, yet the limited data available at local medical centers and privacy concerns associated with centralized AI approaches hinder collaboration. Federated learning (FL) offers a privacy-preserving solution by enabling distributed DL training across multiple medical centers without sharing raw data. This article reviews research conducted from 2016 to 2024 on the use of FL in cancer detection and diagnosis, aiming to provide an overview of the field’s development. Studies show that FL effectively addresses privacy concerns in DL training across centers. Future research should focus on tackling data heterogeneity and domain adaptation to enhance the robustness of FL in clinical settings. Improving the interpretability and privacy of FL is crucial for building trust. This review promotes FL adoption and continued research to advance cancer detection and diagnosis and improve patient outcomes.
Color accuracy in conventional and digital printing processes relies on press characterization to establish the relationship between input device values and output colorimetric or spectral reflectance values. Conventional models, such as Murray–Davies, Clapper–Yule, Yule–Nielsen, and Yule–Nielsen modified spectral Neugebauer, are renowned for providing accurate chromatic and spectral predictions. However, they fall short of accounting for the effects of black ink use and struggle to predict light hues accurately. In order to predict more accurately the color fingerprint, spectral reflectance, of halftone printed images, this study introduces a novel machine-learning-based deep neural network combined with the improved particle swarm optimization algorithm. This enables implementing a spectral reflectance color prediction model for CMYK printing, which eliminates the need to adjust for dot gain during printing. By evaluating this model on a lithographic offset press, we demonstrate its superior performance evidenced by significantly lower root mean square error and color difference (ΔE ∗ 00) values compared to existing methods. This approach minimizes color deviations during printing and reduces material and energy consumption, thereby ultimately enhancing the quality of printed materials.
This study proposes an improved spectral reflectance reconstruction method to convert the response data captured by RGB digital cameras to the object surface spectral reflectance. Additionally, a system noise model is also proposed, which incorporates both signal-dependent and signal-independent components, thus rendering it more closely aligned with real-world conditions. Image data captured with RGB digital cameras under multiple LED light sources, and the inverse distance of the Euclidean distance between the training sample test samples in RGB color space is used as a weighting coefficient to reconstruct the spectral reflectance of the object surface, as accurately as possible. Experimental results show that the proposed method has better reconstruction accuracy than existing methods. The root mean square error values and the reconstructed goodness of fit coefficient higher than 0.9958 indicate that the spectral reconstruction performance has greatly improved compared to existing methods, thus proving the validity of the proposed weighting method.