Dynamic simulation and projection of ESV changes in arid regions caused by urban growth under climate change scenarios.

Spatial simulation and projection of ecosystem services value (ESV) changes caused by urban growth are important for sustainable development in arid regions. We developed a new model of cellular automata based grasshopper optimization algorithm (named GOA-CA) for simulating urban growth patterns and assessing the impacts of urban growth on ESV changes under climate change scenarios. The results show that GOA-CA yielded overall accuracy exceeding 98%, and FOM for 2010 and 2020 were 43.2% and 38.1%, respectively, indicating the effectiveness of the model. The prairie lost the highest economic ESVs (192 million USD) and the coniferous yielded the largest economic ESV increase (292 million USD) during 2000-2020. Using climate change scenarios as urban future land use demands, we projected three scenarios of the urban growth of Urumqi for 2050 and their impacts on ESV. Our model can be easily applied to simulating urban development, analyzing its impact on ESV and projecting future scenarios in global arid regions.

Optical single-channel cryptosystem based on the non-negative matrix factorization and face biometric in cyan-magenta-yellow-black color space.

In this paper, an optical color single-channel asymmetric cryptosystem based on the non-negative matrix factorization (NMF) and a face biometric in cyan-magenta-yellow-black (CMYK) space is proposed. To the best of our knowledge, this is the first time that NMF has been introduced into optical color image encryption. In the proposed cryptosystem, the color image in CMYK space is first decomposed into four color channels: C, M, Y, and K. By performing NMF operations on the four color channels, the four basic and sparse matrices can be obtained, respectively, which achieves asymmetry and saves computational resources. The four basis matrices can be used as private keys, and the four coefficient matrices are synthesized by the inverse discrete wavelet transform for subsequent encryption. Finally, the synthesized image is encoded with double random phase encoding based on phase truncation (PT). Compared with the existing PT-based cryptosystems, our cryptosystem can improve security against a special attack. In addition, the chaotic random phase mask is generated by a face biometric, which is noncontact and unique. Numerical simulation results are shown to verify the feasibility and robustness of our cryptosystem. Further, the proposed cryptosystem can be extended to encrypt multiple images conveniently.

Performance of a U2-net model for phase unwrapping.

Phase unwrapping plays a pivotal role in optics and is a key step in obtaining phase information. Recently, owing to the rapid development of artificial intelligence, a series of deep-learning-based phase-unwrapping methods has garnered considerable attention. Among these, a representative deep-learning model called U 2-net has shown potential for various phase-unwrapping applications. This study proposes a U 2-net-based phase-unwrapping model to explore the performance differences between the U 2-net and U-net. To this end, first, the U-net, U 2-net, and U 2-net-lite models are trained simultaneously, then their prediction accuracy, noise resistance, generalization capability, and model weight size are compared. The results show that the U 2-net model outperformed the U-net model. In particular, the U 2-net-lite model achieved the same performance as that of the U 2-net model while reducing the model weight size to 6.8% of the original U 2-net model, thereby realizing a lightweight model.

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.

DIC measurement method for large rotation based on improved grid-based motion statistics.

As a noncontact optical measurement method, the digital image correlation (DIC) method can provide full-field displacement and strain measurement during object deformation. In the case of small rotation deformation, the traditional DIC method can obtain accurate deformation measurement results. However, when the object rotates at a large angle, the traditional DIC method cannot obtain the extreme value of the correlation function, resulting in the occurrence of decorrelation. In order to address the issue, a full-field deformation measurement DIC method based on improved grid-based motion statistics is proposed for large rotation angles. First, the speeded up robust features algorithm is applied to extract and match the feature point pairs between the reference image and the deformed image. Furthermore, an improved grid-based motion statistics algorithm is proposed to eliminate the wrong matching point pairs. Then, the deformation parameters of the feature point pairs obtained by the affine transformation are taken as the initial deformation value for DIC calculation. Finally, the intelligent gray-wolf optimization algorithm is used to obtain the accurate displacement field. The effectiveness of the proposed method is proved by simulation and practical experiments, and the comparative experiments show that the proposed method is faster and more robust.

Enhancement method with naturalness preservation and artifact suppression based on an improved Retinex variational model for color retinal images.

Retinal images are widely used for the diagnosis of various diseases. However, low-quality retinal images with uneven illumination, low contrast, or blurring may seriously interfere with diagnosis by ophthalmologists. This study proposes an enhancement method for low-quality retinal color images. In this paper, an improved variational Retinex model for color retinal images is first proposed and applied to each channel of the RGB color space to obtain the illuminance and reflectance layers. Subsequently, the Naka-Rushton equation is introduced to correct the illumination layer, and an enhancement operator is constructed to improve the clarity of the reflectance layer. Finally, the corrected illuminance and enhanced reflectance are recombined. Contrast-limited adaptive histogram equalization is introduced to further improve the clarity and contrast. To demonstrate the effectiveness of the proposed method, this method is tested on 527 images from four publicly available datasets and 40 local clinical images from Tianjin Eye Hospital (China). Experimental results show that the proposed method outperforms the other four enhancement methods and has obvious advantages in naturalness preservation and artifact suppression.

A Retinex-based variational model for noise suppression and nonuniform illumination correction in corneal confocal microscopy images.

Objective.Corneal confocal microscopy (CCM) image analysis is a non-invasivein vivoclinical technique that can quantify corneal nerve fiber damage. However, the acquired CCM images are often accompanied by speckle noise and nonuniform illumination, which seriously affects the analysis and diagnosis of the diseases.Approach.In this paper, first we propose a variational Retinex model for the inhomogeneity correction and noise removal of CCM images. In this model, the Beppo Levi space is introduced to constrain the smoothness of the illumination layer for the first time, and the fractional order differential is adopted as the regularization term to constrain reflectance layer. Then, a denoising regularization term is also constructed with Block Matching 3D (BM3D) to suppress noise. Finally, by adjusting the uneven illumination layer, we obtain the final results. Second, an image quality evaluation metric is proposed to evaluate the illumination uniformity of images objectively.Main results.To demonstrate the effectiveness of our method, the proposed method is tested on 628 low-quality CCM images from the CORN-2 dataset. Extensive experiments show the proposed method outperforms the other four related methods in terms of noise removal and uneven illumination suppression.SignificanceThis demonstrates that the proposed method may be helpful for the diagnostics and analysis of eye diseases.

Fusion network based on the dual attention mechanism and atrous spatial pyramid pooling for automatic segmentation in retinal vessel images.

Accurate segmentation of retinal blood vessels from retinal images is crucial to aid in the detection and diagnosis of many eye diseases. In this paper, a fusion network based on the dual attention mechanism and atrous spatial pyramid pooling (DAANet) is proposed for vessel segmentation. First, we propose a dual attention module consisting of a position attention module and a channel attention module, which aims to adaptively recalibrate features to extract effective features. And full-scale skip connections are used in the encoder to provide multi-scale feature maps for the dual attention modules. Then, atrous spatial pyramid pooling (ASPP) allows the network to capture features at multiple scales and combine high-level semantic information with low-level features through the encoder-decoder architecture. We qualitatively evaluate the model using five metrics: sensitivity, specificity, accuracy, AUC, and F1 score on DRIVE, CHASED_B1, and STARE datasets. The DAANet outperforms the work of 10 state-of-the-art predecessors in these three datasets. Furthermore, we apply the trained model to clinical retinal images. The model obtains gratifying accurate and detailed segmentation results, which demonstrates a promising application prospect in medical practices.

Phase retrieval for objects in rain based on a combination of variational image decomposition and variational mode decomposition in FPP.

As far as we know, there is no paper reported to retrieve the phase of an object in rain by the fringe projection profilometry (FPP) method. The fringe projection pattern taken in rain contains much rain noise, which makes it difficult to accurately retrieve the phase of the object. In this paper, we focus on the phase retrieval of the object in rain by the FPP method. We first decompose the original fringe projection pattern into a series of band-limited intrinsic mode functions by the two-dimensional variational mode decomposition (2D-VMD) method. Then we screen out fringe-associated modes adaptively based on mutual information and reconstruct the fringe projection pattern. Next, we decompose the reconstructed fringe projection pattern by the TGV-Hilbert-BM3D variational model to obtain the de-rained fringe component. Finally, we use the Fourier transform method, phase unwrapping method, and carrier-removal method to obtain the unwrapped phase. We test the proposed method on three fringe projection patterns taken in simulated rain weather, and we compare our proposed method with the phase-shifting method, windowed Fourier method, morphological operation-based bidimensional empirical mode decomposition method, 2D-VMD method, and the TGV-Hilbert-BM3D method. The experimental results demonstrate that, for the first time to our knowledge, our method can effectively retrieve the phase of an object in rain from a single fringe projection pattern.

DIC measurement for large-scale structures based on adaptive warping image stitching.

As a representative method of optical non-interference measurement, digital image correlation (DIC) technology is a non-contact optical mechanics method that can measure the displacement and deformation of the whole field. However, when the measurement range of the field is too large, the existing DIC method cannot measure the full-field strain accurately, which limits the application of the DIC measurement in the case of a large size and wide-field view. To address this issue, a DIC measurement method for large-scale structures based on adaptive warping image stitching is proposed in this paper. First, multiple adjacent high-resolution images are collected at different locations of large-scale structures. Secondly, the collected images are stitched by applying the adaptive warping image stitching algorithm to obtain a panoramic image. Finally, the DIC algorithm is applied to solve the whole deformation field. In the experiments, we first verify the feasibility of the proposed method for image matching and fusion through the numerical simulation of a rigid body translation experiment. Then the accuracy and robustness of the proposed method in practical application are verified by rigid body translation and a three-point bending experiment. The experimental results demonstrate that the measurement range of DIC is improved significantly with the adaptive warping image stitching algorithm.

Uneven wrapped phase pattern denoising using a deep neural network.

The wrapped phase patterns obtained from an object composed of different materials have uneven gray values. In this paper, we improve the dilated-blocks-based deep convolution neural network (DBDNet) and build a new dataset for restoring the uneven gray values of uneven wrapped phase patterns as well as eliminating the speckle noise. In our method, we improve the structure of dilated blocks in DBDNet to enhance the ability of obtaining full scales of gray values and speckle noise information in the uneven phase patterns. We use the combined MS_SSIM+L1 loss function to improve the denoising and restoration performance of our method. We compare three representative networks ResNet-based, ADNet, and BRDNet in denoising with our proposed method. We test the three compared methods and our method on one group of computer-simulated and one group of experimentally obtained uneven noisy wrapped phase patterns from a dynamic measurement. We also conduct the ablation experiments on the improved model structure and the combined loss function used in our method. The denoising performance has been evaluated quantitatively and qualitatively. The denoising results demonstrate that our proposed method can reduce high speckle noise, restore the uneven gray values of wrapped phase patterns, and get better results than the compared methods.

Variational image decomposition model TGV-Hilbert-BM3D for phase retrieval in FPP and its application for an on-site wall surface bulge.

Mass loss from wall surface bulge deformation can be used to estimate the strength loss of reinforcement, bond reduction, and ductility degradation, so it is very important to accurately measure the three-dimensional (3D) shape of on-site wall surface bulge. In this paper, we try to solve the problem by use of fringe projection profilometry. In the fringe projection patterns of wall surface bulge, the contrast of the fringes is very weak, and there are sometimes cracks in patterns. We first present a preprocessing method to inpaint fringes if there are damaged fringes caused by cracks. Then we propose a new, to the best of our knowledge, image decomposition model, total generalized variation (TGV)-Hilbert-block-matching (BM)3D, to effectively extract the fringe component. Finally, we use Fourier transform, phase unwrapping, and carrier-removal methods to obtain the unwrapped phase. We test the proposed method on a simulated fringe projection pattern and two real fringe projection patterns of wall surface bulge. We compare our method with the advanced total variation space-generalized functions space-BM3D, TV-Hilbert-L2, and Beppo-Levi-space-Hilbert-BM3D methods. In addition, we perform ablation experiments to prove that our preprocessing method is necessary. The experimental results demonstrate that our method can effectively measure the 3D shape of wall surface bulge from a single fringe projection pattern for the first time, to our knowledge.

Joint enhancement and denoising in electronic speckle pattern interferometry fringe patterns with low contrast or uneven illumination via an oriented variational Retinex model.

Simultaneous speckle reduction and contrast enhancement for electronic speckle pattern interferometry (ESPI) fringe patterns is a challenging task. In this paper, we propose a joint enhancement and denoising method based on the oriented variational Retinex model for ESPI fringe patterns with low contrast or uneven illumination. In our model, we use the structure prior to constrain the illumination and introduce a fractional-order differential to constrain the reflectance for enhancement, then use the second-order partial derivative of the reflectance as the denoising term to reduce noise. The proposed model is solved using the sequential method to obtain piecewise smoothed illumination and noise-suppressed reflectance sequentially, which avoids remaining noise in the illumination and reflectance map. After obtaining the refined illuminance and reflectance, we substitute the gamma-corrected illuminance into the camera response function to further adjust the reflectance as the final enhancement result. We test our proposed method on two non-uniform illumination computer-simulated and two low-contrast experimentally obtained ESPI fringe patterns. Finally, we compare our method with three other joint enhancement and denoising variational Retinex methods.

Dynamic shape measurement for objects with patterns by Fourier fringe projection profilometry based on variational decomposition and multi-scale Retinex.

In practical measurement, we often need to measure the shape of objects with patterns or letters. As far as we know, no paper has ever reported the shape measurement for objects with patterns or letters by Fourier fringe projection profilometry (FPP). In this paper, we propose a method based on the variational decomposition TV-Hilbert-L2 model and multi-scale Retinex (MSR) to measure the shape of objects with patterns and letters by Fourier FPP. In this method, we first use the TV-Hilbert-L2 model to obtain the fringe part, then perform MSR enhancement on the fringe part, and finally decompose the enhanced fringe part with TV-Hilbert-L2 again. We evaluate the performance of this method via application to one computer-simulated noisy fringe projection pattern and two experimental fringe projection patterns with different types of patterns or letters, and comparison with the Fourier transform method, the variational image decomposition TV-Hilbert-L2 model. Furthermore, we apply the proposed method to the dynamic three-dimensional shape measurement of hand posture with pattern. The experimental results show that our method can effectively measure the dynamic shape of objects with patterns or letters from a single-frame fringe projection pattern.

DBDNet for denoising in ESPI wrapped phase patterns with high density and high speckle noise.

In this paper, we propose a dilated-blocks-based deep convolution neural network, named DBDNet, for denoising in electronic speckle pattern interferometry (ESPI) wrapped phase patterns with high density and high speckle noise. In our method, the proposed dilated blocks have a specific sequence of dilation rate and a multilayer cascading fusion structure, which can better improve the effect of speckle noise reduction, especially for phase patterns with high noise and high density. Furthermore, we have built an abundant training dataset with varieties of densities and noise levels to train our network; thus, the trained model has a good generalization and can denoise ESPI wrapped phase in various circumstances. The network can get denoised results directly and does not need any pre-process or post-process. We test our method on one group of computer-simulated ESPI phase patterns and one group of experimentally obtained ESPI phase patterns. The test images have a high degree of speckle noise and different densities. We compare our method with two representative methods in the spatial domain and frequency domain, named oriented-couple partial differential equation and windowed Fourier low pass filter (LPF), and a method based on deep learning, named fast and flexible denoising convolutional neural network (FFDNet). The denoising performance is evaluated quantitatively and qualitatively. The results demonstrate that our method can reduce high speckle noise and restore the dense areas of ESPI phase patterns, and get better results than the compared methods. We also apply our method to a series of phase patterns from a dynamic measurement and get successful results.

Binarization for low-quality ESPI fringe patterns based on preprocessing and clustering.

Massive inherent speckle noise and extremely low contrast make it difficult to binarize electronic speckle pattern interferometry (ESPI) fringe patterns. In this paper, we present a binarization based on preprocessing and fuzzy C-means (FCM) clustering for low-quality ESPI fringe patterns. First, we use the multiscale retinex (MSR) algorithm to enhance the original fringe pattern to improve the contrast between the bright and dark fringes. Then, the local entropy of the enhanced fringe pattern is calculated and the second-order oriented partial differential equation algorithm is introduced to filter the local entropy map. Finally, the FCM is applied to cluster the local entropy filtering map, and the pixels of the fringe pattern are classified into two categories: bright fringes and dark fringes. To verify the reliability and universality of the proposed method, we provide a qualitative evaluation of six experimental ESPI subtraction fringe patterns and two computer-simulated ESPI addition fringe patterns. Experimental results exhibit that the proposed method can provide good binarization performances.

Enhancement and denoising method for low-quality MRI, CT images via the sequence decomposition Retinex model, and haze removal algorithm.

The visibility and analyzability of MRI and CT images have a great impact on the diagnosis of medical diseases. Therefore, for low-quality MRI and CT images, it is necessary to effectively improve the contrast while suppressing the noise. In this paper, we propose an enhancement and denoising strategy for low-quality medical images based on the sequence decomposition Retinex model and the inverse haze removal approach. To be specific, we first estimate the smoothed illumination and de-noised reflectance in a successive sequence. Then, we apply a color inversion from 0-255 to the estimated illumination, and introduce a haze removal approach based on the dark channel prior to adjust the inverted illumination. Finally, the enhanced image is generated by combining the adjusted illumination and the de-noised reflectance. As a result, improved visibility is obtained from the processed images and inefficient or excessive enhancement is avoided. To verify the reliability of the proposed method, we perform qualitative and quantitative evaluation on five MRI datasets and one CT dataset. Experimental results demonstrate that the proposed method strikes a splendid balance between enhancement and denoising, providing performance superior to that of several state-of-the-art methods.

ULNet for the detection of coronavirus (COVID-19) from chest X-ray images.

Novel coronavirus disease 2019 (COVID-19) is an infectious disease that spreads very rapidly and threatens the health of billions of people worldwide. With the number of cases increasing rapidly, most countries are facing the problem of a shortage of testing kits and resources, and it is necessary to use other diagnostic methods as an alternative to these test kits. In this paper, we propose a convolutional neural network (CNN) model (ULNet) to detect COVID-19 using chest X-ray images. The proposed architecture is constructed by adding a new downsampling side, skip connections and fully connected layers on the basis of U-net. Because the shape of the network is similar to UL, it is named ULNet. This model is trained and tested on a publicly available Kaggle dataset (consisting of a combination of 219 COVID-19, 1314 normal and 1345 viral pneumonia chest X-ray images), including binary classification (COVID-19 vs. Normal) and multiclass classification (COVID-19 vs. Normal vs. Viral Pneumonia). The accuracy of the proposed model in the detection of COVID-19 in the binary-class and multiclass tasks is 99.53% and 95.35%, respectively. Based on these promising results, this method is expected to help doctors diagnose and detect COVID-19. Overall, our ULNet provides a quick method for identifying patients with COVID-19, which is conducive to the control of the COVID-19 pandemic.

Contrast enhancement and speckle suppression in OCT images based on a selective weighted variational enhancement model and an SP-FOOPDE algorithm.

Simultaneous contrast enhancement and speckle suppression in optical coherence tomography (OCT) are of great significance to medical diagnosis. In this paper, we propose a selective weighted variational enhancement (SWVE) model to enhance the structural parts of OCT images, and then present a shape-preserving fourth-order-oriented partial differential equations (SP-FOOPDE) algorithm to suppress speckle noise. To be specific, in the SWVE model, we first introduce the fast and robust fuzzy c-means clustering (FRFCM) algorithm to generate masks based on the gray-level histograms of the reconstructed OCT images and utilize the masks to distinguish the structural parts from the background. Then the retinex-based weighted variational model, combined with gamma correction, is adopted to enhance the structural parts by multiplying the estimated reflectance with the adjusted illumination. In the despeckling process, we present an SP-FOOPDE algorithm with the fidelity term modified by the shearlet transform to strike a splendid balance between noise suppression and structural preservation. Experimental results show that the proposed method performs well in contrast enhancement and speckle suppression, with better quality metrics of the MSE, PSNR, CNR, ENL, EKI, and ν and better noise immunity than the related method. Moreover, the application to the segmentation preprocessing exhibits that the retinal structure of the OCT images processed by the proposed method can be completely segmented.

Non-uniform illumination correction based on multi-scale Retinex in digital image correlation.

Digital image correlation (DIC) is an effective optical measurement method. It aims to obtain the displacement field and strain field of the measured object by correlating two digital speckle images before and after deformation. In the actual acquisition of speckle images, due to the large volume of the measured object, the light source cannot cover all areas evenly or has some random change. These issues may easily lead to a non-uniform distribution of light intensity speckle images and reduce the quality of speckle images, which affects the accuracy of DIC measurement to a certain extent. To solve this problem, a non-uniform illumination correction algorithm based on multi-scale Retinex is introduced. First, to analyze the influence of non-uniform illumination on DIC measurement accuracy, the displacement comparison experiment of the numerical simulation speckle images with different non-uniform illumination is conducted. Then, a non-uniform illumination correction algorithm based on multi-scale Retinex is applied to reduce or eliminate the effects of non-uniform illumination by the simulation experiment. Finally, the quantitative measurement of rigid body rotation and uniaxial tensile experiment in plane is studied to verify the feasibility of the correction method for the speckle images. The experimental results show that the measurement accuracy of DIC is improved significantly with the aid of non-uniform illumination variation correction.