Mixed Loss Function Research Articles

Revolutionizing diabetic eye disease detection: retinal image analysis with cutting-edge deep learning techniques

Globally, glaucoma is a leading cause of visual impairment and vision loss, emphasizing the critical need for early diagnosis and intervention. This research explores the application of deep learning for automated glaucoma diagnosis using retinal fundus photographs. We introduce a novel cross-sectional optic nerve head (ONH) feature derived from optical coherence tomography (OCT) images to enhance existing diagnostic procedures. Our approach leverages deep learning to automatically detect key optic disc characteristics, eliminating the need for manual feature engineering. The deep learning classifier then categorizes images as normal or abnormal, streamlining the diagnostic process. Deep learning techniques have proven effective in classifying and segmenting retinal fundus images, enabling the analysis of a growing number of images. This study introduces a novel mixed loss function that combines the strengths of focal loss and correntropy loss to handle complex biomedical data with class imbalance and outliers, particularly in OCT images. We further refine a multi-task deep learning model that capitalizes on similarities across major eye-fundus activities and metrics for glaucoma detection. The model is rigorously evaluated on a real-world ophthalmic dataset, achieving impressive accuracy, specificity, and sensitivity of 100%, 99.8%, and 99.2%, respectively, surpassing state-of-the-art methods. These promising results underscore the potential of our deep learning algorithm for automated glaucoma diagnosis, with significant implications for clinical applications. By simultaneously addressing segmentation and classification challenges, our approach demonstrates its effectiveness in accurately identifying ocular diseases, paving the way for improved glaucoma diagnosis and early intervention.

Generating crisp boundaries using multi-scale features and mixed loss function

Generating crisp boundaries using multi-scale features and mixed loss function

A novel image dehazing algorithm for complex natural environments

As an essential preprocessing step in computer vision tasks, image dehazing is preparatory work in many practical applications. To ensure the navigation safety of intelligent ships in complex haze weather, this paper proposes a lightweight real-time dehazing method for the natural environment based on an all-in-one dehazing network. Specifically, a hybrid dilated convolution is used to construct a dehazing network, which effectively expands the receptive field and improves the ability to extract characteristic information without increasing the calculation amount of the model. Secondly, a hybrid attention module is developed to enhance the model's ability to remove haze while retaining more detailed features by giving weights to light haze and heavy haze areas in the image. Finally, considering the subjective perception of human eyes, a mixed loss function is designed to address image dimness and color distortion after dehazing. Through experiments on both synthetic and real-world haze datasets, the proposed method attains ideal dehazing results and has high real-time performance. It can dehaze under different haze conditions, address the critical challenge of degraded visual perception in severe weather conditions of intelligent ship navigation on inland rivers in complex environments, and promote intelligent ships' development.

Enhancing the seismic response of faults by using a deep learning‐based method

AbstractThe accuracy of fault interpretation is generally influenced by the quality of seismic images. Because of the blurring effect of the migration process, faults with small throws may not be clearly imaged in seismic images, which will impose limitations on the fault detection. To address this issue, we propose a deep learning‐based method to enhance faults in poststack seismic images. We generate abundant training samples by convolving the three‐dimensional point‐spread functions with the noisy reflectivity models. The corresponding labels are synthesized using the one‐dimensional seismic wavelet convolution method, simulating conditions with perfect illumination. To train the network for optimal performance, we investigate the impact of different loss functions. Ultimately, we employ a mixed loss function combining structural similarity index measure and gradient difference loss, since the gradient difference loss focuses more on geological edge information, and the structural similarity index measure possesses excellent image perceptual capability and optimization property. Results from one synthetic seismic image and three real seismic data demonstrate that our proposed method can effectively restore the sharpness of fault surfaces, particularly for faults with small displacements. Compared to the structural smoothing method, the network we trained achieves optimal fault enhancement. Furthermore, coherence‐based fault images indicate that seismic images enhanced using our method can improve the accuracy of fault interpretation and yield more continuous fault maps.

Identification of Pepper Leaf Diseases Based on TPSAO-AMWNet.

Pepper is a high-economic-value agricultural crop that faces diverse disease challenges such as blight and anthracnose. These diseases not only reduce the yield of pepper but, in severe cases, can also cause significant economic losses and threaten food security. The timely and accurate identification of pepper diseases is crucial. Image recognition technology plays a key role in this aspect by automating and efficiently identifying pepper diseases, helping agricultural workers to adopt and implement effective control strategies, alleviating the impact of diseases, and being of great importance for improving agricultural production efficiency and promoting sustainable agricultural development. In response to issues such as edge-blurring and the extraction of minute features in pepper disease image recognition, as well as the difficulty in determining the optimal learning rate during the training process of traditional pepper disease identification networks, a new pepper disease recognition model based on the TPSAO-AMWNet is proposed. First, an Adaptive Residual Pyramid Convolution (ARPC) structure combined with a Squeeze-and-Excitation (SE) module is proposed to solve the problem of edge-blurring by utilizing adaptivity and channel attention; secondly, to address the issue of micro-feature extraction, Minor Triplet Disease Focus Attention (MTDFA) is proposed to enhance the capture of local details of pepper leaf disease features while maintaining attention to global features, reducing interference from irrelevant regions; then, a mixed loss function combining Weighted Focal Loss and L2 regularization (WfrLoss) is introduced to refine the learning strategy during dataset processing, enhancing the model's performance and generalization capabilities while preventing overfitting. Subsequently, to tackle the challenge of determining the optimal learning rate, the tent particle snow ablation optimizer (TPSAO) is developed to accurately identify the most effective learning rate. The TPSAO-AMWNet model, trained on our custom datasets, is evaluated against other existing methods. The model attains an average accuracy of 93.52% and an F1 score of 93.15%, demonstrating robust effectiveness and practicality in classifying pepper diseases. These results also offer valuable insights for disease detection in various other crops.

Vehicle detection algorithm for foggy based on improved AOD-Net

To strengthen the safety monitoring of foggy road traffic and maintain the safety of vehicle driving on foggy roads, image dehazing algorithms are used to improve the clarity of road images detected in foggy environments, thereby improving the detection ability and monitoring efficiency of intelligent transportation systems for vehicle targets. Due to the low accuracy of vehicle detection and serious problem of missed detections in haze environments, this paper proposes an improved All-in-One Dehazing Network (AOD-Net) algorithm for detecting foggy vehicles, which adds batch normalization (BN) layers after each layer of convolution in AOD-Net, accelerating the convergence of the model and controlling overfitting. To enhance image detail information, an effective pyramid-shaped PSA attention module is embedded to extract richer feature information, enrich model representation, and improve the loss function to a multi-scale structural similarity (MS-SSIM) + L1 mixed loss function, thereby improving the quality, brightness, and contrast of dehazing images. Compared with current image dehazing algorithms, the dehazing quality of our algorithm is superior to other dehazing algorithms, such as dark channel prior (DCP), Dehaze-Net, and Fusion Feature Attention Network (FFA-Net). Compared with AOD-Net, the improved algorithm has increased the peak signal-to-noise ratio by 3.23 dB. At the same time, after the improved AOD-Net image dehazing processing, YOLOv7 object detection was performed and experimentally validated on a real foggy dataset. The results showed that compared with the previous method, it had better recognition performance in foggy detection and recognition, and higher detection accuracy for vehicles.

Design of sports achievement prediction system based on U-net convolutional neural network in the context of machine learning

Sports plays a pivotal role in national development. To accurately predict college students' sports performance and motivate them to improve their physical fitness, this study constructs a sports achievement prediction system by using a U-Net Convolutional Neural Network (CNN) in machine learning. Firstly, the current state of physical education teachers’ instructional proficiency is investigated and analyzed to identify existing problems. Secondly, an improved U-Net-based sports achievement prediction system is proposed. This method enhances the utilization and propagation of network features by incorporating dense connections, thus addressing gradient disappearance issues. Simultaneously, an improved mixed loss function is introduced to alleviate class imbalance. Finally, the effectiveness of the proposed system is validated through testing, demonstrating that the improved U-Net CNN algorithm yields superior results. Specifically, the prediction accuracy of the improved network for sports performance surpasses that of the original U-Net by 4.22 % and exceeds that of DUNet by 5.22 %. Compared with other existing prediction networks, the improved U-Net CNN model exhibits a superior achievement prediction ability. Consequently, the proposed system enhances teaching and learning efficiency and offers insights into applying artificial intelligence technology to smart classroom development.

Cascade Residual Multiscale Convolution and Mamba-Structured UNet for Advanced Brain Tumor Image Segmentation

In brain imaging segmentation, precise tumor delineation is crucial for diagnosis and treatment planning. Traditional approaches include convolutional neural networks (CNNs), which struggle with processing sequential data, and transformer models that face limitations in maintaining computational efficiency with large-scale data. This study introduces MambaBTS: a model that synergizes the strengths of CNNs and transformers, is inspired by the Mamba architecture, and integrates cascade residual multi-scale convolutional kernels. The model employs a mixed loss function that blends dice loss with cross-entropy to refine segmentation accuracy effectively. This novel approach reduces computational complexity, enhances the receptive field, and demonstrates superior performance for accurately segmenting brain tumors in MRI images. Experiments on the MICCAI BraTS 2019 dataset show that MambaBTS achieves dice coefficients of 0.8450 for the whole tumor (WT), 0.8606 for the tumor core (TC), and 0.7796 for the enhancing tumor (ET) and outperforms existing models in terms of accuracy, computational efficiency, and parameter efficiency. These results underscore the model’s potential to offer a balanced, efficient, and effective segmentation method, overcoming the constraints of existing models and promising significant improvements in clinical diagnostics and planning.

Distilling Knowledge from a Transformer-Based Crack Segmentation Model to a Light-Weighted Symmetry Model with Mixed Loss Function for Portable Crack Detection Equipment

The detection of cracks is extremely important for maintenance of concrete structures. Deep learning-based segmentation models have achieved high accuracy in crack segmentation. However, mainstream crack segmentation models have very high computational complexity, and therefore cannot be used in portable crack detection equipment. To address this problem, a knowledge distilling structure is designed by us. In this structure, a large teacher model named TBUNet is proposed to transfer crack knowledge to a student model with symmetry structure named ULNet. In the TBUNet, stacked transformer modules are used to capture dependency relationships between different crack positions in feature maps and achieve contextual awareness. In the ULNet, only a tiny U-Net with light-weighted parameters is used to maintain very low computational complexity. In addition, a mixed loss function is designed to ensure detail and global features extracted by the teacher model are consistent with those of the student model. Our designed experiments demonstrate that the ULNet can achieve accuracies of 96.2%, 87.6%, and 75.3%, and recall of 97.1%, 88.5%, and 76.2% on the Cracktree200, CRACK500, and MICrack datasets, respectively, which is 4–6% higher than most crack segmentation models. However, the ULNet only has a model size of 1 M, which is suitable for use in portable crack detection equipment.

Damage detection of road domain waveform guardrail structure based on machine learning multi-module fusion.

The current highway waveform guardrail recognition technology has encountered problems with low segmentation accuracy and strong noise interference. Therefore, an improved U-net semantic segmentation model is proposed to improve the efficiency of road maintenance detection. The model training is guided by mixed expansion convolution and mixed loss function, while the presence of guardrail shedding is investigated by using partial mean values of gray values in ROI region based on segmentation results, while the first-order detail coefficients of wavelet transform are applied to detect guardrail defects and deformation. It has been determined that the Miou and Dice of the improved model are improved by 8.63% and 17.67%, respectively, over the traditional model, and that the method of detecting defects in the data is more accurate than 85%. As a result of efficient detection of highway waveform guardrail, the detection process is shortened and the effectiveness of the detection is improved later on during road maintenance.

Identification of rice disease under complex background based on PSOC-DRCNet

Rice is a crucial agricultural crop, yet it frequently suffers from various diseases, leading to decreased yields and, in severe cases, crop failure. Diseases significantly affect rice growth and yield, resulting in economic losses and food security challenges. The role of image recognition in identifying rice diseases is critical in agricultural production. It enables automated and efficient detection of rice diseases, which is essential for effective management, ensuring food security and sustainable agriculture. To address issues like background noise and edge blurring in rice disease image capture, as well as challenges in determining the optimal learning rate during the training of traditional rice disease recognition networks, a novel method based on PSOC-DRCNet is proposed for rice disease recognition.. First, tto solve the problem of background interference, Dual Mode Attention (DMA) is proposed to adaptively capture meaningful regions in rice disease images. Second, the Residual Adaptive Block(RAB) is proposed, which utilizes dimensional changes and channel attention to solve edge blur problems. Then, a Cross entropy and regularized mixed Loss function (CerLoss), is proposed to optimize the learning strategy of the model in the process of processing datasets and enhance the performance and generalization ability of the model to avoid overfitting problems. Ultimately, In response to the cumbersome problem of finding the optimal learning rate, we propose using Particle Swarm Optimization Chameleon (PSOC) to find the optimal learning rate and train the PSOC-DRCNet model on our custom dataset and compare it with other existing methods and the final average classification accuracy of PSOC-DRCNet is 93.88% with an F1 score of 0.940. We compare it with other existing methods. It is proved that the average classification accuracy of our model under hyper-parameter unification is 92.65% F1 score is 0.928. We validated the PSOC-DRCNet by conducting comparative analyses with other models and through generalization experiments and module effectiveness tests. Additionally, the practicality of PSOC-DRCNet was confirmed through its application in real-world scenarios. The methods proposed in this paper successfully enable the identification of various diseases in rice leaves, offering a practical solution for incorporating deep learning into the agricultural production process. Furthermore, these findings serve as a valuable reference for disease identification in other crops.

A lightweight bladder tumor segmentation method based on attention mechanism.

In the endoscopic images of bladder, accurate segmentation of different grade bladder tumor from blurred boundary regions and highly variable shapes is of great significance for doctors' diagnosis and patients' later treatment. We propose a nested attentional feature fusion segmentation network (NAFF-Net) based on the encoder-decoder structure formed by the combination of weighted pyramid pooling module (WPPM) and nested attentional feature fusion (NAFF). Among them, WPPM applies the cascade of atrous convolution to enhance the overall perceptual field while introducing adaptive weights to optimize multi-scale feature extraction, NAFF integrates deep semantic information into shallow feature maps, effectively focusing on edge and detail information in bladder tumor images. Additionally, a weighted mixed loss function is constructed to alleviate the impact of imbalance between positive and negative sample distribution on segmentation accuracy. Experiments illustrate the proposed NAFF-Net achieves better segmentation results compared to other mainstream models, with a MIoU of 84.05%, MPrecision of 91.52%, MRecall of 90.81%, and F1-score of 91.16%, and also achieves good results on the public datasets Kvasir-SEG and CVC-ClinicDB. Compared to other models, NAFF-Net has a smaller number of parameters, which is a significant advantage in model deployment.

A New Two-Stream Structure Preserving Network for X-Ray Enhancement

Generative adversarial network (GAN) is applied in X-ray enhancement, but the tiny structure of X-ray is hard to be maintained by the generator of the GAN. To solve this problem, a new two-stream GAN (named T-S GAN) is proposed to enhance X-rays and guarantee better structure preserving at the same time. In the proposed generator of T-S GAN, the two-stream feature maps are extracted from original and detail X-rays, and then the two-stream features are fused by a coarse-to-fine strategy. Meanwhile, by introducing detail and structure constraints, a mixed loss function is designed for optimizing the T-S GAN. Lastly, to prove the correctness and effectiveness of T-S GAN, a novel dataset of X-ray with finger and wrist fracture is originally created. There are plenty of hairline fractures in the dataset, which can be used to solve the problem of verifying that the tiny structure is preserved by T-S GAN. Experimental results show that the proposed method, compared with the state-of-the-art methods, has lower mean absolute error (MAE), higer peak signal to noise ratio (PSNR) and structure similarity (SSIM) values. A combination of T-S GAN and the state-of-the-art object detection method can be potentially applied for modern medical detection.

Multimodal brain tumor image segmentation based on DenseNet.

A brain tumor magnetic resonance image processing algorithm can help doctors to diagnose and treat the patient's condition, which has important application significance in clinical medicine. This paper proposes a network model based on the combination of U-net and DenseNet to solve the problems of class imbalance in multi-modal brain tumor image segmentation and the loss of effective information features caused by the integration of features in the traditional U-net network. The standard convolution blocks of the coding path and decoding path on the original network are improved to dense blocks, which enhances the transmission of features. The mixed loss function composed of the Binary Cross Entropy Loss function and the Tversky coefficient is used to replace the original single cross-entropy loss, which restrains the influence of irrelevant features on segmentation accuracy. Compared with U-Net, U-Net++, and PA-Net the algorithm in this paper has significantly improved the segmentation accuracy, reaching 0.846, 0.861, and 0.782 respectively in the Dice coefficient index of WT, TC, and ET. The PPV coefficient index has reached 0.849, 0.883, and 0.786 respectively. Compared with the traditional U-net network, the Dice coefficient index of the proposed algorithm exceeds 0.8%, 4.0%, and 1.4%, respectively, and the PPV coefficient index in the tumor core area and tumor enhancement area increases by 3% and 1.2% respectively. The proposed algorithm has the best performance in tumor core area segmentation, and its Sensitivity index has reached 0.924, which has good research significance and application value.

Mitigating Aberration-Induced Noise: A Deep Learning-Based Aberration-to-Aberration Approach.

One of the primary sources of suboptimal image quality in ultrasound imaging is phase aberration. It is caused by spatial changes in sound speed over a heterogeneous medium, which disturbs the transmitted waves and prevents coherent summation of echo signals. Obtaining non-aberrated ground truths in real-world scenarios can be extremely challenging, if not impossible. This challenge hinders the performance of deep learning-based techniques due to the domain shift between simulated and experimental data. Here, for the first time, we propose a deep learning-based method that does not require ground truth to correct the phase aberration problem and, as such, can be directly trained on real data. We train a network wherein both the input and target output are randomly aberrated radio frequency (RF) data. Moreover, we demonstrate that a conventional loss function such as mean square error is inadequate for training such a network to achieve optimal performance. Instead, we propose an adaptive mixed loss function that employs both B-mode and RF data, resulting in more efficient convergence and enhanced performance. Finally, we publicly release our dataset, comprising over 180,000 aberrated single plane-wave images (RF data), wherein phase aberrations are modeled as near-field phase screens. Although not utilized in the proposed method, each aberrated image is paired with its corresponding aberration profile and the non-aberrated version, aiming to mitigate the data scarcity problem in developing deep learning-based techniques for phase aberration correction. Source code and trained model are also available along with the dataset at http://code.sonography.ai/main-aaa.

Blind Image Despeckling Using a Multiscale Attention-Guided Neural Network

Coherent imaging systems have been applied in the detection of target of interest, natural resource exploration, ailment diagnosis, etc. However, it is easy to generate speckle-degraded images due to the coherent interference of reflected echoes, restricting these practical applications. Speckle noise is a granular interference that affects the observed reflectivity. It is often modeled as multiplicative noise with a negative exponential distribution. This non-linear property makes despeckling of imaging data an intractable problem. To enhance despeckling performance, we propose to blindly remove speckle noise using an intelligent computing-enabled multi-scale attention-guided neural network (termed MSANN). In particular, we first introduce the logarithmic transformation to convert the multiplicative speckle noise model to an additive version. Our MSANN, essentially a feature pyramid network (FPN), is then exploited to restore degraded images in the logarithmic domain. To enhance the generalization ability of MSANN, a multi-scale feature enhancement attention module (FEAM) is incorporated into MSANN to extract multi-scale features for improving imaging quality. A multi-scale mixed loss function is further presented to increase network robustness during training. The final despeckled images are naturally equivalent to the exponential versions of the output of MSANN. Experimental results have shown that MSANN has the capacity of effectively removing speckle noise while preserving essential structures. It can achieve superior despeckling results in terms of visual quality and quantitative measures.

Blend U-Net: Redesigning Skip Connections to Obtain Multiscale Features for Lung CT Images Segmentation.

Lung cancer is a pervasive and persistent issue worldwide, with the highest morbidity and mortality among all cancers for many years. In the medical field, computer tomography (CT) images of the lungs are currently recognized as the best way to help doctors detect lung nodules and thus diagnose lung cancer. U-Net is a deep learning network with an encoder-decoder structure, which is extensively employed for medical image segmentation and has derived many improved versions. However, these advancements do not utilize various feature information from all scales, and there is still room for future enhancement. In this study, we proposed a new model called Blend U-Net, which incorporates nested structures, redesigned long and short skip connections, and deep supervisions. The nested structures and the long and short skip connections combined characteristic information of different levels from feature maps in all scales, while the deep supervision learning hierarchical representations from all-scale concatenated feature maps. Additionally, we employed a mixed loss function to obtain more accurate results. We evaluated the performance of the Blend U-Net against other architectures on the publicly available Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset. Moreover, the accuracy of the segmentation was verified by using the dice coefficient. Blend U-Net with a boost of 0.83 points produced the best outcome in a number of baselines. Based on the results, our method achieves superior performance in terms of dice coefficient compared to other methods and demonstrates greater proficiency in segmenting lung nodules of varying sizes.

MSRL-Net: An Automatic Segmentation of Intracranial Hemorrhage for CT Images Based on the U-Net Framework

Intracranial hemorrhage (ICH) is a hemorrhagic disease occurring in the ventricle or brain, but we found that the U-Net network has poor segmentation performance for small lesion areas. In order to improve the segmentation accuracy, a new convolutional neural network called MSRL-Net is proposed in this paper to accurately segment the lesion regions in the CT images of intracranial hemorrhage. Specifically, to avoid the problem of missing information in the downsampling process, we propose a strategy combining MaxPool and SoftPool. In addition, the mixed loss function is used to optimize the unbalance of medical images. Finally, at the bottleneck layer, an MRHDC module is designed to represent the rich spatial information in the underlying features, in order to obtain multi-scale features with different receptive fields. Our model achieves 0.712 average Dice on a dataset. The experimental results show that this model has a good segmentation effect and potential clinical prospects.

DSKCA-UNet: Dynamic selective kernel channel attention for medical image segmentation.

U-Net has attained immense popularity owing to its performance in medical image segmentation. However, it cannot be modeled explicitly over remote dependencies. By contrast, the transformer can effectively capture remote dependencies by leveraging the self-attention (SA) of the encoder. Although SA, an important characteristic of the transformer, can find correlations between them based on the original data, secondary computational complexity might retard the processing rate of high-dimensional data (such as medical images). Furthermore, SA is limited because the correlation between samples is overlooked; thus, there is considerable scope for improvement. To this end, based on Swin-UNet, we introduce a dynamic selective attention mechanism for the convolution kernels. The weight of each convolution kernel is calculated to fuse the results dynamically. This attention mechanism permits each neuron to adaptively modify its receptive field size in response to multiscale input information. A local cross-channel interaction strategy without dimensionality reduction was introduced, which effectively eliminated the influence of downscaling on learning channel attention. Through suitable cross-channel interactions, model complexity can be significantly reduced while maintaining its performance. Subsequently, the global interaction between the encoder features is used to extract more fine-grained features. Simultaneously, the mixed loss function of the weighted cross-entropy loss and Dice loss is used to alleviate category imbalances and achieve better results when the sample number is unbalanced. We evaluated our proposed method on abdominal multiorgan segmentation and cardiac segmentation datasets, achieving Dice similarity coefficient and 95% Hausdorff distance metrics of 80.30 and 14.55%, respectively, on the Synapse dataset and Dice similarity coefficient metrics of 90.80 on the ACDC dataset. The experimental results show that our proposed method has good generalization ability and robustness, and it is a powerful tool for medical image segmentation.

UN-PUNet for phase unwrapping from a single uneven and noisy ESPI phase pattern.

The wrapped phase patterns of objects with varying materials exhibit uneven gray values. Phase unwrapping is a tricky problem from a single wrapped phase pattern in electronic speckle pattern interferometry (ESPI) due to the gray unevenness and noise. In this paper, we propose a convolutional neural network (CNN) model named UN-PUNet for phase unwrapping from a single wrapped phase pattern with uneven grayscale and noise. UN-PUNet leverages the benefits of a dual-branch encoder structure, a multi-scale feature fusion structure, a convolutional block attention module, and skip connections. Additionally, we have created an abundant dataset for phase unwrapping with varying degrees of unevenness, fringe density, and noise levels. We also propose a mixed loss function MS_SSIM+L2. Employing the proposed dataset and loss function, we can successfully train the UN-PUNet, ultimately realizing effective and robust phase unwrapping from a single uneven and noisy wrapped phase pattern. We evaluate the performance of our method on both simulated and experimental ESPI wrapped phase patterns, comparing it with DLPU, VUR-Net, and PU-M-Net. The unwrapping performance is assessed quantitatively and qualitatively. Furthermore, we conduct ablation experiments to evaluate the impact of different loss functions and the attention module utilized in our method. The results demonstrate that our proposed method outperforms the compared methods, eliminating the need for pre-processing, post-processing procedures, and parameter fine-tuning. Moreover, our method effectively solves the phase unwrapping problem while preserving the structure and shape, eliminating speckle noise, and addressing uneven grayscale.