Fusion Of Image Features Research Articles

A novel multimodal image feature fusion mechanism:Application to rabbit liveweight estimation in commercial farms

In the realm of commercial rabbit farming, assessment of liveweight for each rabbit is crucial for production management. Traditional manual weighing methods are labor-intensive, time-consuming, and can induce stress and disease transmission. To solve this problem, this paper introduced a liveweight estimation method for rabbit utilizing near-infrared and depth image, including a segmentation stage and a weight estimation stage. Specifically, a dual-stream feature fusion mechanism was proposed to effectively integrate information from both near-infrared and depth imaging. The network was trained and validated on a dataset of 1,957 overhead images from 300 rabbits collected from a commercial farm, achieving superior performance with an R-Square (R2) of 0.95, Root Mean Square Error (RMSE) of 194.95 g, Mean Absolute Deviation (MAD) of 155.10 g, Mean Absolute Percentage Error (MAPE) of 4.08%, and Mean Coefficient of Variation (MCV) of 3.03%. Ablation experiments revealed that employing the proposed dual-stream feature fusion mechanism led to reductions of 52.87g, 38.19g, 1.01%, and 0.75% in RMSE, MAD, MAPE, and MCV, respectively, compared to not employing the mechanism. Compared to machine learning method, our proposed approach demonstrates superior performance in RMSE, MAD, MAPE, and MCV, achieving more accurate liveweight estimation for large-scale commercial farms.

Accuracy and efficiency of 2D/3D single-vertebra spine navigation registration method based on dual-view feature fusion

Objective: To investigate the accuracy and efficiency of spine 2D/3D preoperative CT and intraoperative X-ray registration through a framework for spine 2D/3D single-vertebra navigation registration based on the fusion of dual-position image features. Methods: The preoperative CT and intraoperative anteroposterior (AP) and lateral (LAT) X-ray images of 140 lumbar spine patients who visited Huashan Hospital Affiliated to Fudan University from January 2020 to December 2023 were selected. In order to achieve rapid and high-precision single vertebra registration in clinical orthopedic surgery, a designed transformation parameter feature extraction module combined with a lightweight module of channel and spatial attention (CBAM) was used to accurately extract the local single vertebra image transformation information. Subsequently, the fusion regression module was used to complement the features of the anterior posterior (AP) and lateral (LAT) images to improve the accuracy of the registration parameter regression. Two 1×1 convolutions were used to reduce the parameter calculation amount, improve computational efficiency, and accelerate intraoperative registration time. Finally, the regression module outputed the final transformation parameters. Comparative experiments were conducted using traditional iterative methods (Opt-MI, Opt-NCC, Opt-C2F) and existing deep learning methods convolutional neural network (CNN) as control group. The registration accuracy (mRPD), registration time, and registration success rate were compared among the iterative methods. Results: Through experiments on real CT data, the image-guided registration accuracy of the proposed method was verified. The method achieved a registration accuracy of (0.81±0.41) mm in the mRPD metric, a rotational angle error of 0.57°±0.24°, and a translation error of (0.41±0.21) mm. Through experimental comparisons on mainstream models, the selected DenseNet alignment accuracy was significantly better than ResNet as well as VGG (both P<0.05). Compared to existing deep learning methods [mRPD: (2.97±0.99) mm, rotational angle error: 2.64°±0.54°, translation error: (2.15±0.41) mm, registration time: (0.03±0.05) seconds], the proposed method significantly improved registration accuracy (all P<0.05). The registration success rate reached 97%, with an average single registration time of only (0.04±0.02) seconds. Compared to traditional iterative methods [mRPD: (0.78±0.26) mm, rotational angle error: 0.84°±0.57°, translation error: (1.05±0.28) mm, registration time: (35.5±10.5) seconds], registration efficiency of the proposed method was significantly improved (all P<0.05). The dual-position study also compensated for the limitations in the single-view perspective, and significantly outperforms both the front and side single-view perspectives in terms of positional transformation parameter errors (both P<0.05). Conclusion: Compared to existing methods, the proposed CT and X-ray registration method significantly reduces registration time while maintaining high registration accuracy, achieving efficient and precise single vertebra registration.

Development of an online prediction system for soil organic matter and soil moisture content based on multi-modal fusion

Realizing accurate determination of in situ soil organic matter (SOM) content and soil moisture (SM) content in the field is of great importance for improving agricultural production efficiency. However, the feature information of a single sensor is limited, and there is still a certain gap in modeling accuracy compared to traditional laboratory methods. The variations of SM in the field also interfere with data collection, limiting the application of single sensor. In order to realize the efficient detection of in-situ SOM and SM content, this study developed an online detection system for SOM and SM content based on the fusion of characteristic wavelengths, visible images and thermal imaging image features. Firstly, based on a vehicle-mounted platform, a visible-thermal imaging camera and a characteristic wavelength integration device were integrated in a deep pine plow to realize the simultaneous acquisition of in-situ soil multi-sensor data. Then, a lightweight multimodal network was constructed to obtain thermal visible light image features and characteristic wavelength features through branch networks, achieving deep fusion of different modal data and predicting SOM and SM content. Finally, output the forecasting results of SOM and SM content, and transmit the predictive information to the cloud platform for storage. It was verified that the Multi-modal proposed in this study worked best in the laboratory environment, with a predicted R2 of 0.91 and RMSE of 2.9 g/kg for SOM, and a predicted R2 of 0.92 and RMSE of 0.77 % for SM. Compared with single image or characteristic spectral data, the fusion of visible image and characteristic wavelength effectively improved the prediction accuracy of SOM. The real-time prediction of SM was realized by fusing thermal imaging data, and the elimination of the moisture effect of visible images and characteristic wavelengths was also realized with the help of deep fusion of Multi-modal network. After field validation, the R2 of the Multi-modal system was 0.84 and the RMSE was 5.0 g/kg for SOM, and the R2 of the SM was 0.88 and the RMSE was 1.03 %. Despite the differences in soil types between the validation field and the sampling field, the system still demonstrated strong generalization and achieved high accuracy prediction of in-situ SOM and SM in the field, which effectively improves the efficiency and applicability of field. The efficiency and applicability of soil testing effectively improve the efficiency and provide technical guidance for precision management in the field.

FCS-Net: Feather condition scoring of broilers based on dense feature fusion of RGB and thermal infrared images

Assessing the feather condition of broilers is crucial for monitoring the animal welfare status and detecting the occurrence of feather pecking activities. Currently, the feather condition of individual broilers is manually scored by trained experts. To provide a more objective and efficient tool for feather condition scoring, a novel deep learning-based model, named Feather Condition Scoring Network (FCS-Net), was proposed based on RGB and thermal infrared images. The FCS-Net model combined the ResNet18 architecture with the proposed Dense Feature Fusion (DFF) module, which can effectively learn the feature mapping relationship between RGB and thermal infrared images. Before inputting the images into the network, an image registration process was conducted to align the RGB and thermal infrared images. The results showed that the FCS-Net model had a good performance for feather condition scoring, with the Accuracy of 97.02%, the Precision of 96.99%, the Recall of 97.04%, the F1 of 97.01%, and the Inference speed of 15.34 fps. Compared to the ResNet18_RGB model, which only utilise RGB images, the FCS-Net model showed notable improvements in Accuracy by 4.02%, Precision by 3.90%, Recall by 4.08%, and F1 by 4.01%. Moreover, it was observed that the FCS-Net model focused more on the back region of the broilers through heatmap visualization. Furthermore, the algorithm was compared with six typical image recognition algorithms including VGG16, ResNet18, SE-ResNet18, DenseNet121, Mobilenet_V2, and Shufflenet_V2_x1_0, as well as the state-of-the-art (SOTA) feather condition assessment methods. The results showed that the FCS-Net model achieved better performance than the six algorithms and the SOTA feather condition assessment methods. This study provided a valuable reference for automated monitoring of feather condition scoring of broilers in smart farming.

Road adhesion coefficient Estimation: Physics-informed deep learning method with vehicle dynamics model

Road adhesion coefficient (RAC) is not only a significant basis for trajectory planning and decision-making of autonomous vehicles but also a critical parameter to determine the control potential of autonomous vehicles under complex operating conditions. Accurate, timely, and reliable estimation of RAC facilitates many function units of autonomous vehicles and improves driving safety. To address the issues of low accuracy and transparency of RAC estimation method based on road surface image recognition, a vehicle dynamics model-informed deep learning (VDMIDL) method for RAC estimation is proposed in this work. The physics information related to vehicle dynamics is integrated into a CNN-based deep learning model via the fusion of vehicle physical features and high-dimensional road surface image features to realize accurate estimation of RAC. The results of the model training show that the proposed VDMIDL model for RAC estimation possesses higher prediction accuracy compared with the conventional deep learning (CDL) model. The vehicle test results indicate that the proposed VDMIDL model can provide more prospective and precise RAC for trajectory planning, decision-making, and control of the vehicle in response to sudden variations in road conditions. The VDMIDL model is applicable to a wide range of road conditions. RAC estimated by the VDMIDL model is accurate and stable under poor road condition in particular. The proposed fusion estimation model incorporates more physics information into deep learning model, endowing more interpretability and transparency to the black-box model.

Radiogenomics based survival prediction of small-sample glioblastoma patients by multi-task DFFSP model.

In this paper, the multi-task dense-feature-fusion survival prediction (DFFSP) model is proposed to predict the three-year survival for glioblastoma (GBM) patients based on radiogenomics data. The contrast-enhanced T1-weighted (T1w) image, T2-weighted (T2w) image and copy number variation (CNV) is used as the input of the three branches of the DFFSP model. This model uses two image extraction modules consisting of residual blocks and one dense feature fusion module to make multi-scale fusion of T1w and T2w image features as backbone. Also, a gene feature extraction module is used to adaptively weight CNV fragments. Besides, a transfer learning module is introduced to solve the small sample problem and an image reconstruction module is adopted to make the model anatomy-aware under a multi-task framework. 256 sample pairs (T1w and corresponding T2w MRI slices) and 187 CNVs of 74 patients were used. The experimental results show that the proposed model can predict the three-year survival of GBM patients with the accuracy of 89.1 %, which is improved by 3.2 and 4.7 % compared with the model without genes and the model using last fusion strategy, respectively. This model could also classify the patients into high-risk and low-risk groups, which will effectively assist doctors in diagnosing GBM patients.

A Method for Sorting High-Quality Fresh Sichuan Pepper Based on a Multi-Domain Multi-Scale Feature Fusion Algorithm.

Post-harvest selection of high-quality Sichuan pepper is a critical step in the production process. To achieve this, a visual system needs to analyze Sichuan pepper with varying postures and maturity levels. To quickly and accurately sort high-quality fresh Sichuan pepper, this study proposes a multi-scale frequency domain feature fusion module (MSF3M) and a multi-scale dual-domain feature fusion module (MS-DFFM) to construct a multi-scale, multi-domain fusion algorithm for feature fusion of Sichuan pepper images. The MultiDomain YOLOv8 Model network is then built to segment and classify the target Sichuan pepper, distinguishing the maturity level of individual Sichuan peppercorns. A selection method based on the average local pixel value difference is proposed for sorting high-quality fresh Sichuan pepper. Experimental results show that the MultiDomain YOLOv8-seg achieves an mAP50 of 88.8% for the segmentation of fresh Sichuan pepper, with a model size of only 5.84 MB. The MultiDomain YOLOv8-cls excels in Sichuan pepper maturity classification, with an accuracy of 98.34%. Compared to the YOLOv8 baseline model, the MultiDomain YOLOv8 model offers higher accuracy and a more lightweight structure, making it highly effective in reducing misjudgments and enhancing post-harvest processing efficiency in agricultural applications, ultimately increasing producer profits.

Paddy seed viability prediction based on feature fusion of color and hyperspectral image with multivariate analysis

Seed viability is essential to have a homogeneous plant population. The seed industry cannot adopt traditional procedures for seed viability evaluation since they are destructive, time-consuming, and need chemicals. This study aimed to investigate the potential of combining hyperspectral and color image features to differentiate viable and non-viable paddy seeds. The hyperspectral and color image of the 355 paddy seeds was captured and later used to examine their viability. An image processing algorithm was developed to extract features from color images of paddy seeds and investigated significant differences in the retrieved feature data using variance analysis. The spectra were extracted from the selected region of interest (ROI) of the hyperspectral paddy seed image and averaged. In the next step, the partial least square discrimination analysis (PLS-DA) model was developed to distinguish viable and non-viable paddy seeds. Initially, the PLS-DA model was developed using spectral data with different preprocessing techniques, and the result obtained an accuracy of 88.9 % in the calibration set and 86.1 % in the prediction set using Savitzky-Golay 2nd derivative preprocessed spectra. With the fusion of spectral and significant color image features, the model's accuracy improved to 93.3 % and 90.9 % in the calibration and prediction sets, respectively. Results also showed that the fusion of selected color image features with Savitzky-Golay 2nd derivative preprocessed spectra could achieve higher F1-score, recall, and precision values. The visualization map for the viable and non-viable paddy seeds was also developed utilizing the most effective predictive model. The results demonstrate the possibility of using the fusion of the hyperspectral and color image features to sort seeds according to viability, which may be applied in developing an online seed sorting method.

Multi-Stage Feature Fusion of Multispectral and SAR Satellite Images for Seasonal Crop-Type Mapping at Regional Scale Using an Adapted 3D U-Net Model

Multi-Stage Feature Fusion of Multispectral and SAR Satellite Images for Seasonal Crop-Type Mapping at Regional Scale Using an Adapted 3D U-Net Model

Underground coal gangue recognition based on composite fusion of feature and decision

Abstract The underground coal gangue separation and in-situ filling can reduce environmental pollution, promote the recycling of resources, and ensure the safe operation of mining. However, the harsh environment and abnormal working conditions are a significant challenge to the separation technology. Therefore, it is essential to develop a coal gangue classification method that is highly accurate, robust, and can handle abnormal working conditions. To address the above problems, this paper innovatively combines spectral modalities with image modalities to establish a multimodal fusion idea of composite fusion. Firstly, the feasibility of spectral-image fusion and effective fusion criterion are explored under the concat fusion strategy through various feature combinations and classification algorithms under ideal conditions to improve the performance of the model; Secondly, feature fusion is introduced into the single-layer perceptron and its potential in deep learning is explored to improve the performance of the model; Then the quantitative criteria of the judgment matrix are improved based on the analytical hierarchy method (AHP) to improve the scientificity and objectivity of decision making; Finally, the effectiveness of our method is verified by testing the bimodal dataset of simulated working conditions. The results show that the accuracy of the composite fusion of spectral and image features reaches 91.43%, and our AHP can be applied to all basic model scenarios, which makes the method highly applicable and feasible. The fusion of deep neural networks shows the strong potential of modal fusion in deep learning. This method can provide a new idea for intelligent separation of underground coal gangue.

An intelligent analysis method for power flow of bulk power grid based on fusion of indicator system feature and image feature

Abstract At present, the adjustment of power flow non-convergence in large power grid is still mostly based on manual trial and error, which relies heavily on expert experience and is inefficient. Therefore, an intelligent power flow analysis method based on index system and image feature fusion is proposed. Firstly, the power flow index system is constructed according to the actual demand, which can provide data support for the following power flow convergence judgment problems. Secondly, taking into account the constraints of similarity and ternary loss, the image feature information is used to guide the output of the information feature representation and to realize the organic interaction of the two modal features. On this basis, the calculation result of the index is correlated with the actual state of power flow to judge the convergence of power flow. Finally, the proposed method is verified on the CEPRI36 nodes system and a large power grid in China. The simulation results show that the accuracy of power flow convergence judgment is 100% for CEPRI36 nodes system and 99.83% for some domestic large power grid. It can effectively predict the convergence of the power flow before the calculation of the power flow required by the project and can provide the basis and theoretical support for the power flow optimization adjustment based on the indicator data.

Gender Classification Based on Spatio-Frequency Feature Fusion of OCT Fingerprint Images in the IoT Environment

Gender Classification Based on Spatio-Frequency Feature Fusion of OCT Fingerprint Images in the IoT Environment

SGDFormer: One-stage transformer-based architecture for cross-spectral stereo image guided denoising

Cross-spectral image guided denoising has shown its great potential in recovering clean images with rich details, such as using the near-infrared image to guide the denoising process of the visible one. To obtain such image pairs, a feasible and economical way is to employ a stereo system, which is widely used on mobile devices. In this case, it is necessary to first model the stereo correspondence between two images before guided denoising. Current works attempt to generate an aligned guidance image to handle the disparity. However, due to occlusion, spectral differences and noise degradation, the aligned guidance image generally exists ghosting and artifacts, leading to an unsatisfactory denoised result. To address this issue, we propose a one-stage transformer-based architecture, named SGDFormer, for cross-spectral Stereo image Guided Denoising. The architecture integrates the correspondence modeling and feature fusion of stereo images into a unified network. Our transformer block contains a noise-robust cross-attention (NRCA) module and a spatially variant feature fusion (SVFF) module. The NRCA module captures the long-range correspondence of two images in a coarse-to-fine manner to alleviate the interference of noise. The SVFF module further enhances salient structures and suppresses harmful artifacts through dynamically selecting useful information. Thanks to the above design, our SGDFormer can restore artifact-free images with fine structures, and achieves state-of-the-art performance on various datasets. Additionally, our SGDFormer can be extended to handle other unaligned cross-model guided restoration tasks such as guided depth super-resolution.

Generalizing event-based HDR imaging to various exposures

Single-exposure High Dynamic Range Imaging (HDRI), as a typical ill-posed problem, has attracted extensive attention from researchers. However, restoration in real-world scenarios has always been an intractable task due to various exposures and noise artifacts. This work proposes an event-based HDRI framework that generalizes to scenes under various exposures by exploiting the high dynamic range of events. To address the challenge of processing diverse exposures, we propose an exposure-aware network incorporating the exposure attention fusion module, which facilitates the adaptive fusion of SDR image and event features. Moreover, the problem of noise in extremely under-exposed regions and events is effectively alleviated by introducing a self-supervised loss, namely EDDN, which effectively enhances the details of saturated areas while simultaneously decreasing noise. We conduct novel event-based HDRI datasets to evaluate our proposed method for benchmarking with diverse exposed images. Comprehensive experiments have demonstrated that our method outperforms the state-of-the-art.

Language-vision matching for text-to-image synthesis with context-aware GAN

Text-to-image generation (T2I) aims to produce visually compelling images while maintaining a high degree of semantic consistency with textual descriptions. Despite the impressive progress made by existing methods, there are problems with limited details of synthesized images and insufficient correlation between the provided text description and the generated images. To address these issues, we propose a Context-Aware Generative Adversarial Network (CA-GAN), which generates images aligned with the input text representations. Specifically, the Context-Aware Block (CA-Block) learns a semantic-adaptive transformation based on text style, enabling the effective fusion of text descriptions and image features for high-quality image generation with better language-vision matching. Furthermore, we propose an Attention Convolution Module (ACM) to identify greater representative traits and avoid the inability to capitalize on non-local contextual information, which enables our model to generate images that exhibit numerous detailed attributes while maintaining high-quality and semantic consistency. Thereafter, we integrate self-attention with convolution to enhance feature maps to reinforce the semantic information in the discriminator, emphasizing critical feature channels while suppressing extraneous information, ultimately yielding more detailed and richer images. The experimental results demonstrate the superiority of our method over the SOTA approaches. In addition, further studies confirm the effectiveness of the generated visual details, which exhibit a high degree of alignment with the input text descriptions. Notably, our attention mechanism showcases cooperative effects contributing to overall performance improvement. The code is available at: https://github.com/hylneu/CAGAN.

Construction of Practical Teaching Mode of Law Course Based on Multi-Mode and Low-Resource Language Learning

China's legal profession is a new profession emerging in the process of China's rule of law. Its development and refinement are increasing in accordance with the rapid social, economic and political development. However, as a lawyer, people need to learn real kung fu. For complex and changeable legal practice problems, people should use the simplest and most effective way to solve them. Therefore, it is necessary to study the curriculum of law specialty to achieve its purpose. It is also necessary to reform the traditional education methods, introduce modern scientific and technological means, and improve their effectiveness, so as to construct a practical teaching mode of Professional Courses in Law (PCL). For practical legal courses, it can be conducted through multi-mode low-resource language learning method. This paper aimed to study how to study the ability of students to obtain information, process information and analyze and process information based on intelligent multi-image feature fusion. Based on the investigation of teaching objectives and teachers' teaching requirements, and the analysis and arrangement of the investigation results, a multi-task learning system model based on image intelligent features was established. In this experiment, 494 valid questionnaires were used to analyze the current situation of PCL practical teaching. Among them, 10.32% and 65.59% of the students were very satisfied with the results of PCL practical teaching, and 18.02% and 59.31% of the students liked PCL practical teaching very much. In the survey on the influencing factors of PCL practical teaching activities, 61.34% of the students thought that the students did not know enough and were not enthusiastic enough. On the whole, the students are satisfied with the practical teaching arrangement of PCL, but from the actual situation, the effect of practical teaching is not very good, and the expected goal has not been achieved.

3D Object Detection under Urban Road Traffic Scenarios Based on Dual-Layer Voxel Features Fusion Augmentation.

To enhance the accuracy of detecting objects in front of intelligent vehicles in urban road scenarios, this paper proposes a dual-layer voxel feature fusion augmentation network (DL-VFFA). It aims to address the issue of objects misrecognition caused by local occlusion or limited field of view for targets. The network employs a point cloud voxelization architecture, utilizing the Mahalanobis distance to associate similar point clouds within neighborhood voxel units. It integrates local and global information through weight sharing to extract boundary point information within each voxel unit. The relative position encoding of voxel features is computed using an improved attention Gaussian deviation matrix in point cloud space to focus on the relative positions of different voxel sequences within channels. During the fusion of point cloud and image features, learnable weight parameters are designed to decouple fine-grained regions, enabling two-layer feature fusion from voxel to voxel and from point cloud to image. Extensive experiments on the KITTI dataset demonstrate the significant performance of DL-VFFA. Compared to the baseline network Second, DL-VFFA performs better in medium- and high-difficulty scenarios. Furthermore, compared to the voxel fusion module in MVX-Net, the voxel feature fusion results in this paper are more accurate, effectively capturing fine-grained object features post-voxelization. Through ablative experiments, we conducted in-depth analyses of the three voxel fusion modules in DL-VFFA to enhance the performance of the baseline detector and achieved superior results.

Multi wavelength optical image feature fusion method for improving the reconstruction effect of cerebral hematoma

Diffuse optical tomography (DOT) under medical image guidance can be used to reconstruct images of subdural hematomas for monitoring purposes. However, when the shape of hematoma is irregular or the signal to noise ratio(SNR) of input signal is low, the accuracy and robustness of this method both decrease. In order to alleviate this situation and improve the reconstruction effect of cerebral hematoma images, a multi wavelength optical image feature fusion method was proposed in this paper. Experimental results show that the mean relative volume error (VRE) of the hematoma reconstruction model based on the multi-wavelength optical image feature fusion method is only 0.66 %, and the mean value of the average Manhattan distance (AMD) between the reconstructed absorption coefficient and the true absorption coefficient is 0.0052 mm−1. Compared with the model using single-wavelength optical image mapping feature extraction method, the multi-wavelength optical image feature fusion method reduces the average VRE of the model by 16.5 %, and the average AMD by 16.1 %. And compared with the model directly inputting single-wavelength optical information, its average VRE and average AMD decreased by 80.0 % and 27.8 %. This method has clinical application significance for the monitoring of subdural hematomas.

High-Accuracy Tomato Leaf Disease Image-Text Retrieval Method Utilizing LAFANet.

Tomato leaf disease control in the field of smart agriculture urgently requires attention and reinforcement. This paper proposes a method called LAFANet for image-text retrieval, which integrates image and text information for joint analysis of multimodal data, helping agricultural practitioners to provide more comprehensive and in-depth diagnostic evidence to ensure the quality and yield of tomatoes. First, we focus on six common tomato leaf disease images and text descriptions, creating a Tomato Leaf Disease Image-Text Retrieval Dataset (TLDITRD), introducing image-text retrieval into the field of tomato leaf disease retrieval. Then, utilizing ViT and BERT models, we extract detailed image features and sequences of textual features, incorporating contextual information from image-text pairs. To address errors in image-text retrieval caused by complex backgrounds, we propose Learnable Fusion Attention (LFA) to amplify the fusion of textual and image features, thereby extracting substantial semantic insights from both modalities. To delve further into the semantic connections across various modalities, we propose a False Negative Elimination-Adversarial Negative Selection (FNE-ANS) approach. This method aims to identify adversarial negative instances that specifically target false negatives within the triplet function, thereby imposing constraints on the model. To bolster the model's capacity for generalization and precision, we propose Adversarial Regularization (AR). This approach involves incorporating adversarial perturbations during model training, thereby fortifying its resilience and adaptability to slight variations in input data. Experimental results show that, compared with existing ultramodern models, LAFANet outperformed existing models on TLDITRD dataset, with top1, top5, and top10 reaching 83.3% and 90.0%, and top1, top5, and top10 reaching 80.3%, 93.7%, and 96.3%. LAFANet offers fresh technical backing and algorithmic insights for the retrieval of tomato leaf disease through image-text correlation.

Progressive feature fusion for SNR-aware low-light image enhancement

Restoring image quality in low-light environments is an intriguing topic. While deep learning models have made significant strides in low-light enhancement, most models do not take into account the inherent characteristics of objects themselves. In this paper, we use the characteristics of the image itself to construct a Signal-to-Noise Ratio (SNR) map that guides the signal space variation to dynamically stretch the pixel values. Specifically, we propose a novel signal-to-noise ratio image-guided enhancement framework that uses the feature information of the original image to guide spatial variations in the image. It involves step-wise guidance for image feature fusion, gradually emphasizing high-frequency feature information within the image. Meanwhile, we introduced a texture optimization module that utilizes the feature information extracted by the feature fusion module to address the issues of overexposure and detail loss. We performed qualitative and quantitative evaluations on synthetic and real low-light image datasets to demonstrate the performance of our method. The experimental results show that our model outperforms other state-of-the-art methods (SOTA) in robust low-light enhancement, especially in processing images captured in complex scenes.