Search Results (1,425)

Digital camera sensors are designed to capture a wide range of incident illuminants, enabling the creation of high-quality images. However, these sensors lack the capability to differentiate between the color of the source illuminant and the actual color (or original color) of the object being captured. For this reason, the computational color constancy (CCC) was introduced and has been developed over decades. The CCC is an approach to modeling the color perception of the human visual system (HVS) by ensuring accurate object color determination under varying source illuminant conditions. At the core of human visual perception (HVP)-based CCC is attaining higher accuracy in scene illuminant estimation. The emergence of deep convolutional neural networks (DCNNs) was a recent innovation in accurate illuminant estimation, fundamentally transforming the CCC research landscape. Nevertheless, accurate illuminant estimation still remains a huge challenge for both traditional and state-of-the-art (SOTA) approaches. To further advance precision in illuminant estimation, this article presents a novel learning-based illumination color cast estimation approach to HVP-based CCC. Most importantly, the proposed approach is intended to integrate informative features into both channel and spatial regions while preserving long-term dependency feature information with the use of dense skip connections. To achieve these objectives, the proposed Dense Dual Connection Aggregated Transform Network (DDCATNet) architecture is designed to comprise several modules: shallow feature extraction, channel-wise and spatial feature-based Dense Dual Connection (DDC), fusion of the dense channel-wise attention (CA) and spatial attention (SA) branches through a gate mechanism (GM) unit, and aggregate transform. It is worth noting that both the CA blocks and the SA blocks in the DDC module are characterized by dense and cascading connections, meant to preserve long-term feature information and modulate different-level feature information at both global and local scales. The densely connected CA branch (DCA) and the densely connected SA branch (DSA) are also highly effective in securing high-contribution information while suppressing redundant data. The GM unit is integrated at the back of the DDC module, fusing the two DCA and DSA branches to ensure the adaptive merging of useful hierarchical feature information and the extraction of more valuable feature information. As a result, the proposed DDCATNet architecture significantly enhanced precision in illuminant estimation, thereby improving performance. In rigorous experiments on a wide range of datasets, the proposed DDCATNet approach outperformed its SOTA counterparts, validating the efficacy and generalization capabilities, as well as robust camera-invariance, across diverse, single- and multi-illuminant datasets and model architectures. Full article

The widespread accumulation of ampicillin (AMP) poses significant ecological and health risks, demanding rapid and portable monitoring tools. Herein, a Fe-Ni bimetallic-doped polydopamine (FeNi@PDA) nanozyme with exceptional peroxidase-like activity was synthesized for the visual and on-site monitoring of AMP. Optimized through bimetallic electronic coupling, FeNi@PDA exhibited enhanced catalytic efficiency (K_M = 0.051 mmol/L for H₂O₂ and 0.049 mmol/L for 3,3′,5,5′-tetramethylbenzidine) and generated ¹O₂ and ·O₂⁻ via H₂O₂ activation. Leveraging the competitive consumption of reactive oxygen species (ROS) by electron-rich AMP, a colorimetry detection mode was developed where AMP concentration inversely correlated with oxidized 3,3′,5,5′-tetramethylbenzidine (oxTMB) formation. This strategy achieved a good linear relationship of between 0.05 to 100 μg/mL, with a limit of detection (LOD) of 10.38 ng/mL. Furthermore, a smartphone-integrated paper-based detection mode was fabricated by immobilizing FeNi@PDA on filter paper. The color gradient of test papers, analyzed via smartphone imaging, enabled on-site AMP quantification with a LOD of 340 ng/mL. This work not only developed a novel Fe-Ni bimetallic nanozyme with enhanced peroxidase-like activity and established a competitive ROS-consumption sensing mechanism but also pioneered a dual-mode detection platform for low-cost, user-friendly ampicillin monitoring in environmental samples. Full article

Aiming at the problems of weak background adaptive ability, high dependence on edge features, high computational complexity of some traditional license plate location algorithms, high deployment cost and strong training dependence of location model based on deep learning, this paper proposes a fast license plate location algorithm based on statistical color features. The algorithm uses the HSV color space as the main processing channel, and quantifies the regional color distribution characteristics by constructing the hue histogram and calculating its standard deviation and other statistics, which significantly improves the discrimination and illumination adaptability of the license plate mask in complex background. Compared with the lightweight deep learning models such as “You Only Look Once Version 12 Nano”, this algorithm does not need GPU acceleration and model loading, eliminates the need for data training, significantly reduces the deployment cost and complexity, and can run efficiently on the general computing platform. The experimental results show that compared with the YOLOv12n model, the average processing time of this algorithm is shortened by 30.81% (when YOLOv12n is evaluated with GPU) or 48.42% (when YOLOv12n is evaluated with CPU) at the cost of sacrificing about 5.8% positioning accuracy. The positioning accuracy still reaches 93.7%, demonstrating high processing efficiency and excellent platform adaptability. The algorithm has the advantages of being lightweight, efficient and interpretable, and is especially suitable for intelligent parking lots, edge devices and other scenes sensitive to real time, cost and energy consumption. Full article

Foundation segmentation models exhibit strong generalization on natural images yet degrade substantially in underwater scenes due to color distortion, scattering, and low contrast, which collectively impair feature representation. Parameter-efficient fine-tuning strategies have been explored to adapt SAM to marine domains while preserving generalization, but degraded image quality still hampers feature extraction. Moreover, existing SAM-based underwater methods typically rely on ground-truth box prompts during inference. Since ground-truth boxes are inherently unavailable in real-world underwater scenarios, this dependence yields evaluation outcomes that fail to reflect actual deployment conditions, thereby limiting their practical applicability. To address these issues, Water-AutoSAM is introduced—a dual-domain enhanced auto-prompting framework tailored for underwater image segmentation. The auto-prompting mechanism decouples semantic and positional representations for generalized point generation, which are optimized via enhanced sharpness, correctness, and diversity losses under staged training. To counter the degrading effects typical of underwater imagery, a lightweight module designated SS-UIE is integrated as a frozen pre-enhancement stage. This module operates with spatial–frequency dual-branch processing and utilizes a fixed residual fusion coefficient to combine the two streams. Operating entirely without box prompts, Water-AutoSAM achieves competitive annotation-free performance, attaining 92.38% mIoU on SUIM and reducing the gap to the fully supervised upper bound to 2.08% on COD10K. Full article

Accurate estimation of atmospheric light (AL) is essential within the atmospheric scattering model (ASM) to achieve high-quality image dehazing. Most existing methods, however, typically assume spatial uniformity of AL and rely on heuristic estimation from distant pixels, which often results in color distortion and exposure imbalance in dehazed outputs. To address this issue, we propose a novel framework that decouples AL into distinct color and intensity components. Specifically, a neutral pixel prior (NPP) is introduced for precise AL color estimation, which can eliminate color casts. For AL intensity estimation, an adaptive global-local fusion strategy integrating luminance perception transformation and a depth-related color prior (DRCP) is developed to realize balanced exposure. Extensive experiments demonstrate that the proposed method significantly outperforms state-of-the-art AL estimation methods, yielding dehazed images with enhanced color fidelity and more natural illumination. Full article

Underwater image enhancement is essential for marine visual perception tasks. However, the highly heterogeneous optical degradations in real-world waters, the scarcity of paired training data, and the inherent dilemma for existing models in balancing long-range dependency modeling with computational overhead pose significant challenges. To address these issues, this paper proposes a prior-guided, self-supervised underwater image enhancement framework called Scene-Aware Retinex Mamba (SARM). This framework seamlessly integrates Retinex theoretical priors with state space models (SSMs) and operates without paired supervision by employing a prior-guided pseudo-labeling strategy to guide network optimization. Architecturally, SARM deeply couples the physical Retinex prior with SSM. Its core module integrates multi-color space features and leverages a 2D selective scan mechanism to achieve global context modeling with linear complexity $\mathcal{O}\left(HW\right)$, effectively removing complex color casts and suppressing non-uniform scattering noise. To further overcome the generalization bottlenecks in cross-domain underwater testing, this paper introduces a Scene-Aware Adapter (SAA), which facilitates dynamic loss scheduling and adaptive feature gating by quantifying scene-specific degradation characteristics. Comprehensive evaluations on multiple benchmark datasets, including UIEB, EUVP, and UCCS, demonstrate that SARM achieves state-of-the-art subjective and objective enhancement quality (e.g., yielding a URanker score of 2.491 and a CCF score of 35.76), while maintaining an ultra-fast inference speed of 136.52 FPS on the UIEB dataset. Furthermore, extended experiments reveal that SARM can significantly boost the performance of downstream vision tasks, validating its potential as a robust preprocessing module for various practical marine vision applications. Full article

Melanin-like polymers, particularly polydopamine, have gained significant attention as photothermal materials due to their broad light absorption (ultraviolet to near-infrared), high photothermal conversion efficiency, negligible fluorescence, good biocompatibility regarding unmodified melanin-like polymers, and universal adhesion. Upon light irradiation, these bioinspired polymers convert absorbed optical energy into heat through molecular vibration and electron–phonon coupling, making them ideal for diverse photothermal applications. This review comprehensively summarizes recent advances in using melanin-like polymers for photothermal purposes. In biomedical engineering, they serve as efficient agents for photothermal therapy and synergistic antibacterial treatment. In catalysis, their photothermal effect enhances pollutant degradation, hydrogen production, and chemical warfare agent detoxification. For water remediation, melanin-like polymers are fabricated into evaporators, membranes, and aerogels for solar-driven steam generation, desalination, and oil spill cleanup. They also enable sensitive photothermal sensing, near-infrared imaging, and laser desorption ionization mass spectrometry imaging. Furthermore, these materials are incorporated into soft actuators and self-healing elastomers for light-controlled shape memory, programmable folding, and remote manipulation. Finally, we discuss remaining challenges such as long-term stability, biocompatibility, scalability, and color limitations and provide future perspectives for advancing melanin-like photothermal materials toward practical applications. Full article

Automatic classification of anime character painting styles is of great significance to the digital cultural industry and visual content production. Existing methods are prone to shortcut learning when handling complex color rendering and cannot fully decouple high-frequency line drafts from low-frequency colors. To solve this problem, this study proposes an improved deep learning classification method based on EfficientNetV2-B0. This method introduces random amplitude scaling (RAS) at the data input terminal. It realizes effective decoupling of colors and line-draft structures through random low-frequency amplitude perturbation, and suppresses the model’s excessive dependence on global color information from the source. Edge-guided coordinate attention (EG-CA) is integrated into the backbone network. It enhances the perception of line and contour features through edge weights and improves the model’s ability to capture fine-grained structural features. Adaptive scale feature aggregation (ASFA) is designed in the multi-scale feature fusion stage. It achieves efficient fusion of shallow textures and deep semantics through dynamic weighting, so as to enhance the model’s discriminative ability under complex painting styles. On a dataset containing 7887 images of four categories, the classification accuracy of the model reaches 95.81%. It significantly outperforms mainstream models such as MViTv2-T. Meanwhile, the number of parameters is only 7.84 M and the inference speed reaches 68.83 FPS. Ablation experiments show that the synergistic effect of the three modules improves the accuracy of the baseline model by 6.06%. It proves that the proposed method provides reliable technical support for the structured management and copyright traceability of anime images. Full article

Due to dust, haze and uneven lighting conditions, images captured in coal mines frequently suffer severe quality degradation. Traditional dehazing methods typically overlook color characteristics and employ single algorithms, and deep-learning-based approaches require substantial training data and demand high hardware specifications, which restricts their dehazing performance and efficiency. This research proposes an efficient image dehazing framework. This method integrates bright and dark channel information to derive contrast feature values based on their linear differences. These values reflect dust concentration levels in the environment. By incorporating dust sensor data, the adaptive scaling coefficient and dust compensation terms are established. The adaptive scaling coefficient serves as a dynamic pixel selection ratio during ambient light estimation, effectively preserving the brightest pixel points. The global color mean functions as the criterion for determining image color characteristics, distinguishing between color images and low-light grayscale images to enable different dehazing approaches. This process achieves state verification and information complementarity between visual perception and dust measurement. The weighted fusion of bright and dark channels yields more accurate estimation for ambient light and transmission. Additionally, a weighted guided filter is designed with dust compensation terms incorporated. Ablation studies were conducted to validate the effectiveness of this method in enhancing image features. Finally, comparative experiments were performed using a self-constructed coal mine hazy image dataset, along with SOTS-indoor and SOTS-outdoor datasets. Experimental results demonstrate that, compared with other state-of-the-art methods, this method effectively removes haze while restoring image features and details, exhibiting superior stability, adaptability, and computational efficiency. Full article

Background/Objectives: Bladder cancer remains one of the most frequently diagnosed malignancies worldwide and is characterized by high recurrence rates requiring long-term surveillance. Conventional white-light cystoscopy (WLC) remains the standard diagnostic method; however, it may fail to detect flat lesions such as carcinoma in situ or small papillary tumors. In recent years, enhanced imaging techniques, including fluorescence cystoscopy and autofluorescence-based systems, have been introduced to improve diagnostic accuracy. The aim of this study is to evaluate the usefulness of fluorescence-based diagnostic techniques and autofluorescence imaging supported by numerical color value (NCV) analysis in the detection and assessment of bladder lesions. Methods: The study was conducted at the Center of Photodynamic Diagnostics and Therapy, Department of Internal Medicine, Angiology and Physical Medicine, Medical University of Silesia in Bytom. Bladder mucosa was assessed using the Onco-LIFE optical imaging system, which enables visualization under both white-light and autofluorescence conditions. The study included 30 patients diagnosed with non-muscle-invasive bladder cancer or suspected bladder lesions, who underwent cystoscopic evaluation using white-light cystoscopy and autofluorescence imaging. From this cohort, three representative cases were selected for detailed qualitative presentation to illustrate different pathological conditions of the bladder mucosa. In selected cases, photodynamic diagnosis (PDD) using intravesical administration of 5-aminolevulinic acid (ALA) was performed prior to cystoscopic examination. Autofluorescence signals were analyzed using red and green fluorescence channels, and tissue characteristics were evaluated using the numerical color value parameter. Results: Representative cases of non-muscle-invasive bladder lesions were analyzed and compared using conventional white-light cystoscopy and autofluorescence imaging. The use of fluorescence-based imaging enabled improved visualization of suspicious mucosal changes compared with standard WLC. Differences in fluorescence patterns were observed between malignant lesions, inflammatory changes, and carcinoma in situ. NCV analysis allowed quantitative assessment of fluorescence signals and supported differentiation of pathological tissue from normal bladder mucosa. Conclusions: Fluorescence cystoscopy and autofluorescence-based imaging systems represent valuable tools for improving the detection of bladder lesions during endoscopic examination. The integration of enhanced optical imaging techniques with quantitative fluorescence analysis may increase diagnostic sensitivity and support targeted biopsy and tumor resection. Continued technological development and clinical experience may further expand the role of fluorescence diagnostics in the early detection and management of bladder cancer. Full article

Background/Objectives: Esophageal cancer is one of the major global causes of cancer mortality, and the 5-year survival rate remains below 20% because many cases are detected late. In this study, a Spectral-Aided Vision Enhancer (SAVE) algorithm was utilized to convert conventional white-light endoscopic images (WLI) into hyperspectral-like narrow-band imaging (NBI) images for machine-learning classification of Dysplasia, Normal, and Squamous Cell Carcinoma (SCC). Methods: A total of 762 WLI images obtained from Kaohsiung Medical University were augmented to 1074 using the Al bumentations library, employing vertical flipping, horizontal flipping, and rotations. The SAVE conversion pipeline employs a 24-patch Macbeth color checker for calibration, γ-correction, CIE XYZ transformation, and multivariate regression to interpolate spectral bands, yielding an average color difference of 2.79 (CIEDE2000) from true NBI. The training outcomes and performance metrics illustrate the versatility of the machine learning/deep learning models—Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN)—which were trained and evaluated on both the original WLI and SAVE datasets. Performance metrics were analyzed based on precision, recall, accuracy, and F1-score. Results: The CNN sample achieved an accuracy of 100 percent on SAVE data, compared to 93 percent for WLI. The accuracy of RF improved, with WLI at 91% and SAVE at 96%, while SVM increased from 79% to 84%. These improvements indicate the diagnostically valuable spectral variations that can be amplified with SAVE, resulting in significant enhancements in pre-cancer/SCC sensitivity. Conclusions: The proposed SAVE method demonstrates significant potential for enhancing endoscopic imaging and advancing computer-aided diagnosis in esophageal cancer screening, with applicability in other gastrointestinal imaging scenarios as well. Full article

Remote sensing (RS) imagery often suffers from non-uniform atmospheric scattering, resulting in severe contrast degradation, detail blurring, and spectral distortion. While recent advanced State Space Models (SSMs) offer efficient long-range modeling, they frequently struggle with spectral–spatial coupling interference and lack explicit physical constraints, leading to over-smoothed textures and color biases in high-reflectance regions. In this paper, we propose PhysWave-SSN, a Physics-Inspired Frequency-Decoupled Network specifically designed for high-fidelity RS image dehazing. The architecture employs a task-adaptive frequency-specific screening strategy to effectively isolate structural details from atmospheric interference. Specifically, we first introduce a Frequency-Aware Selection Gate (FASG) that unifies adaptive channel screening with physical transmission estimation, enabling precise recalibration of frequency components. To bridge the gap between physical scattering principles and state space representation learning, we develop a Physics-Informed SSM (PI-SSM), where the discretization step size of Mamba is dynamically modulated by the estimated haze density. This mechanism allows the model to adaptively adjust its spatial receptive field according to local degradation levels, enhancing physical interpretability. Furthermore, a Luminance-Adaptive Fusion Module (LAFM) is presented to protect high-reflectance land covers and maintain spectral consistency. Extensive experiments on multiple RS datasets demonstrate that PhysWave-SSN achieves superior performance, notably attaining a maximum PSNR gain of 2.49 dB while ensuring high structural and spectral fidelity. Full article

Visual similarity analysis of ethnic brocades is valuable for image retrieval, style comparison, and digital archiving in cultural heritage informatics. However, although deep neural networks provide powerful visual representations, their encoded similarity structures are often difficult to interpret. This study presents an interpretable deep feature framework for analyzing inter-ethnic visual similarity in brocade images from ten minority groups in Southwest China. Four convolutional neural network backbones, including AlexNet, VGG-16, ResNet-18, and an SE-enhanced ResNet-18 (SResNet-18), were first evaluated to identify a reliable feature extractor. The best-performing model was then used to construct deep feature-based similarity and distance relationships among ethnic categories. To interpret this structure, five handcrafted descriptor types, namely color, texture, geometric, local-structure, and frequency-domain features, were compared with the deep feature similarity matrix using Spearman correlation analysis and weighted descriptor fusion. Experimental results showed that SResNet-18 achieved the best classification performance, with an accuracy of 95.15% and an F1-score of 95.14%. Among the handcrafted descriptors, color showed the strongest correspondence with the RGB-based deep similarity structure ($r=0.643$), followed by local-structure descriptors ($r=0.416$), whereas classical texture descriptors showed near-zero correspondence ($r=-0.063$). The optimal weighted fusion further improved the correlation to $r=0.731$. These findings suggest that the SResNet-18 feature space is more strongly associated with color composition and local motif organization than with the specific grayscale texture, global geometric, or frequency-domain descriptors used in this study. The proposed framework provides an interpretable approach for understanding deep visual similarity in cultural heritage images and offers methodological support for pattern-based retrieval, comparative style analysis, and digital documentation. Full article

There are differences in imaging characteristics between infrared and visible light images: visible light images can provide rich texture and color information, but imaging is limited in harsh weather conditions. Infrared images are based on the target’s thermal radiation characteristics and have the ability to resist environmental interference but lack details and background information. Effectively integrating the two can significantly enhance scene understanding ability and improve environmental perception and target recognition performance in applications such as intelligent driving. However, existing fusion methods still face challenges, especially in complex scenes where it is difficult to balance the full preservation of target information with the complete presentation of background details, often resulting in difficulties in extracting differentiated features from different modalities. This article proposes a target detection method based on the visible light infrared fusion diffusion model. This method introduces the Stable Diffusion architecture and designs a target perception spatial fusion weight module that can adaptively generate a spatial fusion weight map based on modal differences. By implementing a multi-stage dynamic fusion strategy, the fusion ratio is automatically adjusted at different diffusion stages. A full-step multi-step prediction mechanism is adopted to improve fusion quality and stability. Compared with existing methods, the method proposed in this article has significant advantages. Experiments on multiple publicly available datasets have shown that this method outperforms existing mainstream methods in key metrics such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), and ean Absolute Error (MAE) and also demonstrates good detection performance in downstream tasks for object detection. Full article

Chemical probe-based pattern analysis offers a powerful approach for evaluating complex mixtures, particularly in non-target sensing scenarios where components are unknown or where multivariate interactions, such as those involved in taste perception, dominate the response behavior. However, its broader applicability has been limited by challenges in generating sufficiently diverse probe sets and in acquiring multidimensional response data from large probe arrays. In this study, we address both limitations by constructing a high-capacity sensing platform that integrates artificial DNA-derived chemical probes with conventional fluorescent probes. Artificial DNA probes were synthesized following established modular assembly methods, enabling large-scale generation of structurally diverse sensing elements. An imaging-based detection instrument—combining controlled excitation and high-resolution fluorescence capture—was developed to simultaneously quantify color and intensity responses from up to 88 probes. We applied this system to the analysis of 20 taste-related compounds, demonstrating clear discrimination based on multidimensional fluorescence patterns. Furthermore, systematic evaluation of probe number versus classification accuracy revealed that increased probe diversity substantially enhances non-target discrimination performance, supporting the value of using low-specificity artificial DNA probes in high-density arrays. These results establish a versatile and scalable platform for non-target pattern analysis and highlight the importance of probe multiplicity in complex mixture sensing. Full article