To solve the problem of color separation of printed images, this paper proposes a black generation algorithm that can maximize the gamut of the CMYK output color space. This proposed method considers the printing gamut space as a CMY color cube and divides it into six sub-gamut spaces using the gamut center diagonal. First, the color targets and algorithms are designed to calculate a gamut center diagonal black lookup table and two gamut boundary black lookup tables for each sub-gamut. Then, for an input color, the three corresponding lookup tables are found by determining the sub-gamut space where the input color is located, and the final CMYK amounts are determined by interpolating the corresponding color points on these lookup tables. Finally, the interpolation calculation results are further optimized using the neighborhood-search strategy. The color-difference evaluation experiment shows that the proposed algorithm can achieve a mean color difference of less than 1 CIEDE2000 standard color-difference unit when reproducing the standard test color target. The gamut reproduction evaluation experiment shows that the gamut distribution and gamut size obtained by the proposed algorithm are closer to the source gamut space than the Gray Balance algorithm. The image reproduction test experiment shows that the proposed algorithm can effectively reproduce the dark details of images and meet the image reproduction requirements of the printing industry.
Accurate segmentation and recognition of retinal vessels is a very important medical image analysis technique, which enables clinicians to precisely locate and identify vessels and other tissues in fundus images. However, there are two problems with most existing U-net-based vessel segmentation models. The first is that retinal vessels have very low contrast with the image background, resulting in the loss of much detailed information. The second is that the complex curvature patterns of capillaries result in models that cannot accurately capture the continuity and coherence of the vessels. To solve these two problems, we propose a joint Transformer–Residual network based on a multiscale attention feature (MSAF) mechanism to effectively segment retinal vessels (MATR-Net). In MATR-Net, the convolutional layer in U-net is replaced with a Residual module and a dual encoder branch composed with Transformer to effectively capture the local information and global contextual information of retinal vessels. In addition, an MSAF module is proposed in the encoder part of this paper. By combining features of different scales to obtain more detailed pixels lost due to the pooling layer, the segmentation model effectively improves the feature extraction ability for capillaries with complex curvature patterns and accurately captures the continuity of vessels. To validate the effectiveness of MATR-Net, this study conducts comprehensive experiments on the DRIVE and STARE datasets and compares it with state-of-the-art deep learning models. The results show that MATR-Net exhibits excellent segmentation performance with Dice similarity coefficient and Precision of 84.57%, 80.78%, 84.18%, and 80.99% on DRIVE and STARE, respectively.
When two colors are presented under identical viewing conditions, they may appear the same to one observer but different to another, a phenomenon known as observer metamerism (OM). This effect is caused by individual differences in their color matching functions (CMFs). To achieve accurate color reproduction between displays, it is essential to minimize OM. This study first investigated OM between four displays in cross-media reproduction through computer simulations. Next, the relationship between metamerism in the displays and the RGB peak wavelengths was examined. The simulations were also used to explain the effects of individual variation on the orientation of OM ellipses in displays, from the perspective of human visual physiological parameters. The analytical approach developed in this study provides a valuable framework for predicting OM across different display technologies.
Real-world scenes typically have a larger dynamic range than what a camera can capture. Temporally and spatially varying exposures have become widely used techniques to capture high dynamic range (HDR) images. One of the key questions is what the optimal set of exposure settings should be in order to achieve good image quality. In response to this question, this paper introduces a lightweight learning-based exposure strategy network. The proposed network is designed to optimize the exposure strategy for direct fusion of standard dynamic range (SDR) images without access to RAW-domain images. Unlike most of the direct fusion exposure strategies that primarily focus on tone optimization alone, the proposed method also incorporates the worst-case signal-to-noise ratio (SNR) in the loss function design. This ensures that the SNR remains consistently above an acceptable threshold while enabling visually pleasing tones in lower noise regions. This lightweight network achieves a significantly shorter inference time compared to other state-of-the-art methods. It is a more practical HDR enhancement technique for real-time and on-device applications. The code can be found at https://github.com/JieyuLi/exposure-bracketing-strategy.
Digital watermarking is an important way to ensure the copyright protection of Thangka element images in Tibetan culture. These images exhibit rich foreground content and dense lines. However, existing digital watermarking methods often overlook these characteristics and employ a single watermark embedding strength that compromises performance. To address these issues, this paper proposes a robust Just Noticeable Distortion (JND) guided perceptually Thangka digital watermarking method. First, by considering the characteristics of texture distribution, it selectively identifies local regions of interest for large and small Thangka element images. Second, it constructs a visual perception JND to adaptively obtain the watermark embedding intensity. Finally, to enhance the robustness to JPEG compression and geometric attacks, it introduces a compression regulator factor and employs a Speeded-Up Robust Features feature matching algorithm. The experimental results show that the method achieves better performance compared with several classical methods in terms of imperceptibility and robustness.
To effectively solve the problems of insufficient brightness, poor contrast, and high noise in low-light environments, this paper proposes a low-light image enhancement method based on multiscale feature fusion. The multilevel features of images are extracted by convolution kernels of different scales, and these multiscale features are fused organically by a feature fusion module. Finally, combining the processing technology of light enhancement and noise suppression, the visual effect of low-light image is significantly improved. The experimental results show that the proposed method has excellent performance in improving image brightness, contrast, and detail information, can effectively suppress noise, and has good adaptability and robustness.
This study utilizes factor extraction method and factor analysis to investigate the demand position of China Brand IP emotional preference, and build a reference system suitable for China Brand IP image. Using a semantic-emotional word classification method, the group’s emotional perception preferences for brand IP images were systematically validated. The 7-point Likert scale analysis was used to extract the semantic meanings of three public factors, namely “emotional experience factor”, “trendy factor” and “personality style factor”. These three public factors explain the emotional perception preferences of local consumers towards representative brand IP images. This study also analyses the emotional cognitive positioning of brand IP images and provides effective design references for brand IP images towards marketing strategies and optimizing design.
Based on the guidance of the national industrial by-product gypsum resource utilization policy, a set of gypsum mold box automated molding system was studied, which overcame the major industry problems of complex three-dimensional structure gypsum profile one-pouring process and the difficulty of continuous matching molding of multi-module production system and realized the stable convergence of process tasks and high-speed operation of production modules. The structural adaptation scheme for the production modules of intermittent pulping, rotary molding and mold box finishing in three-layer space was designed, which automatically adapts to complete all 11 processes of continuous molding of gypsum mold box. The actuators and control models of the production modules focus on the use of mechanical bionic technology, and are adapted to the multi-level visual human-machine interaction operating system, which realizes highly accurate and flexible operation and collaborative processing capability, and has intelligent manufacturing characteristics of workshop-level industrial robots.
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.