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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.

A high beam quality diode-pumped Nd:YAG master oscillator power-amplifier (MOPA) laser with three end-pumped slab amplifiers is developed. The Q -swtiched side-pumped rod oscillator presented a pulse energy 3.1 mJ with beam quality factors of M x 2=1.17 and M y 2=1.15 at a repetition of 1 kHz. The MOPA system delivered a pulse energy of 1.36 J with a pulse width of 48.2 ns and an extraction efficiency of 44.8%. The beam quality factors of M x 2=1.72 and M y 2=3.85 are measured without any phase-conjugators or adaptive optics.

In this paper, we propose an asymmetric optical double-image cryptosystem based on generalized singular value decomposition (GSVD) and five-dimensional (5D) hyperchaotic maps. In the proposed cryptosystem, the two plain images are first decomposed into five components by the GSVD operation. The two unitary matrices obtained by GSVD are encoded as a complex function, which is then modulated by the chaotic random phase masks (CRPMs). The private key and the final encryption result are generated by phase-truncation and amplitude-truncation operations. The GSVD operation can decompose two images at the same time and is used to generate the private key that enables the encryption process to be asymmetric. Compared with the existing phase-truncated-based cryptosystems, our cryptosystem can improve security against a special attack. In addition, the CRPMs are generated by 5D hyperchaotic maps, which have a larger parameter space and better randomness. Numerical simulation results are shown to verify the feasibility and robustness of our cryptosystem. Furthermore, the proposed cryptosystem can be extended to encrypt multiple images conveniently.

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 M S _ S S I M + L 1 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.

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.

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.

An all-sapphire fiber external Fabry-Perot interferometer (EFPI) sensor for measuring gas pressure is proposed and investigated. The sensor head is manufactured from a sapphire fiber ferrule and sapphire tube, and the same material can ensure the stability of the sensor structure at a high temperature. The refractive index of the gas is linearly related to the gas pressure. Therefore, the gas pressure can be measured by studying the optical cavity length of the EFPI. A multi-stage coupled multimode fiber is used to pick up the interference signal of the fiber EFPI. The pressure response of the sensor at different temperatures was measured in the experiment. The experimental results show that the sapphire fiber EFPI can measure 0-5 MPa gas pressure in the environment of 17-1400°C. The sensitivity of the sensor decreases with the increasing temperature, and the maximum sensitivity is 1.1673 µm/MPa (20°C). The sensor is compact and suitable for gas pressure measurement at a high temperature.

A spectral splicing method is proposed for an optical frequency domain reflectometry (OFDR) sensing system using a distributed Bragg reflector (DBR) laser. An external cavity tunable laser (ECTL) is used in a conventional OFDR sensing system. There are no reports to our knowledge about using a DBR laser as the light source for an OFDR sensing system. A DBR laser has the advantages of small size, no mechanical scanning, easy integration, and low cost. However, due to the mode hopping, the wavelength can only be continuously tuned in the range of about 1 nm. The spectral splicing method was used to splice 39 partially overlapping Rayleigh scattering signals into a continuous signal with wavelength of 35.174 nm. Distributed optical fiber strain sensing with 5 mm spatial resolution is realized. The strain range is ±2500µε, and the maximum error of the measured strain is 9.91 µε.

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- L 2 , 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.

Diffraction beams produced by an acousto-optic modulator are widely used in various optical experiments, some of which need to modulate the radio-frequency drive signal to change the diffraction beams from continuous light to pulsed light. The generation of such pulsed light is open-loop, and long-term stability of the power is disregarded. In this paper, we introduce a method to suppress the pulsed light power drift of a semiconductor laser. By using the servo system, the low frequency power drift of 1-60 kHz pulsed light can be suppressed. This pulsed light power stabilization method can be applied to optical rotation detection and pulse pumping.

A location deviation compensation algorithm based on maximum cross correlation is proposed for optical frequency domain reflectometry. The characteristic change of fiber under test and the auxiliary interferometer induced by strain/temperature causes location deviation in the optical frequency domain reflectometry system, which in turn leads to demodulation errors. Compared to current available compensated methods, this algorithm simultaneously compensates the location deviation caused by the fiber under test and the auxiliary interferometer. The temperature variation experiments of the fiber under test and the auxiliary interferometer were carried out, respectively, and results show that the algorithm minimized the demodulation errors introduced by the location deviation.

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 T V - H i l b e r t - L 2 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 T V - H i l b e r t - L 2 model to obtain the fringe part, then perform MSR enhancement on the fringe part, and finally decompose the enhanced fringe part with T V - H i l b e r t - L 2 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 T V - H i l b e r t - L 2 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.

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.

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.

In this paper, we demonstrate a high-power, narrow-linewidth, polarization-maintaining fiber amplifier with near-diffraction-limited beam quality. By optimizing the phase modulation signal, a nearly top-hat-shaped spectrum was generated for self-pulsing suppressing. That results in doubling the self-pulsing threshold we got from conventional white noise signal phase modulation with the same optical linewidth. Based on an optimized signal and a high power, polarization-maintaining, counter-pumped fiber amplifier, we obtain a 3.25 kW narrow-linewidth linearly polarized laser output with a linewidth of ∼20 G H z , the polarization extinction ratio is about 15 dB, and the M 2 is less than 1.22 at the maximum output power. To the best of our knowledge, this is the first demonstration of a narrow-linewidth, linear polarization, all-fiber amplifier with 3.25 kW laser output.

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.

We present a rigorous approach for measuring the throughput of an integrating sphere, from which the so-called sphere multiplier M can be derived. The critical ingredients of this approach are: (i) the transmitted power is measured at the base of an integrating port to avoid non-ideal port effects associated with reflections on the port wall; (ii) to implement this last point, optical fibers are used for light collection, providing a well-defined collection area and numerical aperture; (iii) the angular-dependent fiber throughput and detector sensitivity are determined experimentally and accounted for. We demonstrate in particular that a more realistic theory, accounting for the propagation of skew rays through the fiber, is needed to quantitatively model the fiber effect on the measured sphere throughput. We show experimentally that failure to fulfill these three points produces erroneous results, by as much as 50%. With an accurate experimentally derived sphere multiplier, agreement with theory is then obtained only if realistic ports (with non-zero reflectivity) are assumed. This provides experimental evidence for recent theoretical predictions of the importance of realistic ports [Tang et al., Appl. Opt.57, 1581 (2018)APOPAI0003-693510.1364/AO.57.001581]. Using the same experimental techniques, we also present clear experimental proof of two other predictions from that study: that the angular distribution exiting the port is strongly altered and that the overall port transmittivity is drastically reduced for high aspect ratio ports. This work will provide a solid basis for future quantitative measurements of absolute throughput and for further developments of the theory of integrating spheres.

Speckle noises widely exist in optical coherence tomography (OCT) images. We propose an improved double-path parallel convolutional neural network (called DPNet) to reduce speckles. We increase the network width to replace the network depth to extract deeper information from the original OCT images. In addition, we use dilated convolution and residual learning to increase the learning ability of our DPNet. We use 100 pairs of human retinal OCT images as the training dataset. Then we test the DPNet model for denoising speckles on four different types of OCT images, mainly including human retinal OCT images, skin OCT images, colon crypt OCT images, and quail embryo OCT images. We compare the DPNet model with the adaptive complex diffusion method, the curvelet shrinkage method, the shearlet-based total variation method, and the OCTNet method. We qualitatively and quantitatively evaluate these methods in terms of image smoothness, structural information protection, and edge clarity. Our experimental results prove the performance of the DPNet model, and it allows us to batch and quickly process different types of poor-quality OCT images without any parameter fine-tuning under a time-constrained situation.

The digital speckle correlation method (DSCM) aims to measure the displacement of the interesting points by matching the subset around the same point between the undeformed image and the deformed image. It is an effective and powerful optical metrology method for deformation measurement. Considering that the gray wolf optimizer (GWO) is one of the most popular metaheuristic algorithms to calculate the unknown search spaces in the field of optical engineering, a sub-pixel displacement measurement technique based on the GWO and gradient algorithm is proposed. First, the zero-mean normalized cross correlation function is applied to analyze the correlation between the reference image and deformed image subsets. Second, by exploiting the global searching ability of the GWO algorithm, the initial integer pixel value is obtained and further viewed as the initialization displacement. Finally, the final sub-pixel displacement is generated by using a Barron gradient algorithm. Compared with the state-of-the-art methods on synthetic speckle images, the proposed method can effectively measure the displacement and deformation of rigid bodies. Furthermore, the experiments on the real images demonstrate the effectiveness of our presented framework.

The segmentation of blood vessels in retinal images is crucial to the diagnosis of many diseases. We propose a deep learning method for vessel segmentation based on an encoder-decoder network combined with squeeze-and-excitation connection and atrous spatial pyramid pooling. In our implementation, the atrous spatial pyramid pooling allows the network to capture features at multiple scales, and the high-level semantic information is combined with low-level features through the encoder-decoder architecture to generate segmentations. Meanwhile, the squeeze-and-excitation connections in the proposed network can adaptively recalibrate features according to the relationship between different channels of features. The proposed network can achieve precise segmentation of retinal vessels without hand-crafted features or specific post-processing. The performance of our model is evaluated in terms of visual effects and quantitative evaluation metrics on two publicly available datasets of retinal images, the Digital Retinal Images for Vessel Extraction and Structured Analysis of the Retina datasets, with comparison to 12 representative methods. Furthermore, the proposed network is applied to vessel segmentation on local retinal images, which demonstrates promising application prospect in medical practices.

An observation of prior-damage behavior inside a high-finesse optical resonator is reported. Intra-cavity average power drops appeared with magnitude and time scale depending on the power level. Increasing further the incident laser beam power led to irreversible damage of the cavity coupling mirror surface. The origin of this phenomenon is investigated with post mortem mirror surface imaging and analysis of the signals reflected and transmitted by the enhancement cavity. Scattering losses induced by surface deformation due to a hot-spot surface contaminant is found to be most likely the dominant physics process behind this phenomenon.

Optical coherence tomography (OCT) image enhancement is a challenging task because speckle reduction and contrast enhancement need to be addressed simultaneously and effectively. We present a refined Retinex model for guidance in improving the performance of enhancing OCT images accompanied by speckle noise; a physical explanation is provided. Based on this model, we establish two sequential optimization functions in the logarithmic domain for speckle reduction and contrast enhancement, respectively. More specifically, we obtain the despeckled image of an entire OCT image by solving the first optimization function. Incidentally, we can recover the speckle noise map through removing the despeckle component directly. Then, we estimate the illumination and reflectance by solving the second optimization function. Further, we apply the contrast-limited adaptive histogram equalization algorithm to adjust the illumination, and project it back to the reflectance for achieving contrast enhancement. Experimental results demonstrate the robustness and effectiveness of our proposed method. It performs well in both speckle reduction and contrast enhancement and is superior to the other two methods both in terms of qualitative analysis and quantitative assessment. Our method has the practical potential to improve the accuracy of manual screening and computer-aided diagnosis for retinal diseases.

The fringe skeleton method is the most straightforward method to estimate phase terms in electronic speckle pattern interferometry (ESPI). It usually needs to binarize the fringe patterns. However, the massive inherent speckle noise and intensity inhomogeneity in ESPI fringe patterns make it difficult to binarize the ESPI fringe patterns. In this paper, we propose a binarization method for ESPI fringe patterns based on a modified M-net convolutional neural network. Our method regards the binarization of fringe patterns as a segmentation problem. The M-net is an excellent network for segmentation and has proven to be a useful tool for skeleton extraction in our previous work. Here we further modify the structure of the previous network a bit to suit our task. We train the network by pairs of ESPI fringe patterns and corresponding binary images. After training, we test our method on 20 computer-simulated and three groups of experimentally obtained ESPI fringe patterns. The results show that even for fringe patterns with high noise and intensity inhomogeneity, our method can obtain good binarization results without image preprocessing. We also compare the modified M-net with a classic segmentation network, the U-net, and a residual encoder-decoder network (RED-net). The RED-net was used for binarization of document images. The experimental results prove the effectiveness of our method.

In this paper, an optical single-channel asymmetric cryptosystem based on the inverse discrete wavelet transform (IDWT) and chaotic standard map for multi-image in cyan-magenta-yellow-black (CMYK) mode is proposed. To the best of our knowledge, this is the first time that the color image in CMYK format is encoded into a real-valued two-dimensional (2D) format by the IDWT; thus, our scheme can be implemented in a single-channel step. In addition, we propose to generate the random phase mask based on the chaotic standard map. Due to the large key space and high efficiency of the chaotic standard map, the security level of the proposed scheme can be improved. The chaotic standard map is employed to generate the chaotic standard phase mask (CSPM). Then the encoded 2D image can be encrypted in the linear canonical transform domain based on the CSPM. Numerical simulation results are shown to verify the feasibility and effectiveness of our cryptosystem. In addition, our approach outperforms other relevant cryptosystems and can be extended to encrypt multiple color images directly.

Poor visual quality of color retinal images greatly interferes with the analysis and diagnosis of the ophthalmologist. In this paper, we propose an enhancement method for low-quality color retinal images based on the combination of the Retinex-based enhancement method and the contrast limited adaptive histogram equalization (CLAHE) algorithm. More specifically, we first estimate the illumination map of the entire image by constructing a Retinex-based variational model. Then, we restore the reflectance map by removing the illumination modified by Gamma correction and directly enable the reflectance as the initial enhancement. To further enhance the clarity and contrast of blood vessels while avoiding color distortion, we apply CLAHE on the luminance channel in CIELUV color space. We collect 60 low-quality color retinal images as our test dataset to verify the reliability of our proposed method. Experimental results show that the proposed method is superior to the other three related methods, both in terms of visual analysis and quantitative evaluation while testing on our dataset. Additionally, we apply the proposed method to four publicly available datasets, and the results show that our methods may be helpful for the detection and analysis of retinopathy.

Extracting skeletons from fringe patterns is the key to the fringe skeleton method, which is used to extract phase terms in electronic speckle pattern interferometry (ESPI). Because of massive inherent speckle noise, extracting skeletons from poor, broken ESPI fringe patterns is challenging. In this paper, we propose a method based on a modified M-net convolutional neural network for skeleton extraction from poor, broken ESPI fringe patterns. In our method, we pose the problem as a segmentation task. The M-net performs excellent segmentation, and we modify its loss function to suit our task. The broken ESPI fringe patterns and corresponding complete skeleton images are used to train the modified M-net. The trained network can extract and inpaint the skeletons simultaneously. We evaluate the performance of the network on two groups of computer-simulated ESPI fringe patterns and two groups of experimentally obtained ESPI fringe patterns. Two related recent methods, the gradient vector fields based on variational image decomposition and the U-net based method, are compared with our method. The results demonstrate that our method can obtain accurate, complete, and smooth skeletons in all cases, even where fringes are broken. It outperforms the two compared methods quantitatively and qualitatively.

We propose an asymmetric optical image cryptosystem based on biometric keys and singular value decomposition (SVD) in the Fresnel transform domain. In the proposed cryptosystem, the biometric keys are palmprint phase mask generated by a palmprint, a chaotic phase mask, and an amplitude truncated Fourier transform, which can provide the cryptosystem with more data security due to the uniqueness of the palmprint. Two images are first encoded into a complex function, which then is modulated by the palmprint phase mask. A Fresnel transform and then an SVD operation are performed on the modulated result. The SVD operation is used to generate private secret keys, which makes the encryption secret keys and decryption secret keys different, and thus the encryption process and decryption process are different. In addition, multiple images are encrypted into a real-valued ciphertext, making it convenient to transport and record. Numerical simulation results have demonstrated that our proposed encryption system has robustness against statistical, occlusion, noise, and chosen-plaintext attacks.

In this paper, we propose an optical single-channel asymmetric cryptosystem for multi-image in cyan-magenta-yellow-black (CMYK) color space. To the best of our knowledge, this is the first time that multiple images in CMYK color space have been directly encrypted. The proposed optical asymmetric cryptosystem is based on the quick response (QR) encoding process and the designed Fresnel-linear canonical-fractional Fourier transform (FLFT) encryption process. Each FLFT encryption process consists of phase-truncated FLFT and random amplitude phase masks. The proposed cryptosystem without color space conversion can improve the quality of the decrypted images and avoid the loss of information. In addition, by utilizing the QR codes, the cross talk and quality-loss problems can be reduced efficiently. Numerical simulation results demonstrate that the proposed cryptosystem possesses high robustness against various types of attacks, high security for encrypting multiple color images, and fast encryption efficiency. Furthermore, the proposed cryptosystem outperforms the other relevant cryptosystems and can be extended to encrypt multiple color images in a straightforward way.

It is important to enhance the contrast and remove the speckle noise for optical coherence tomography (OCT) images. In this paper, we propose a selective retinex enhancement method based on the fuzzy c-means (FCM) clustering algorithm to enhance only the structure part in OCT images and combines with the block-matching 3D (BM3D) algorithm for filtering. In the proposed selective retinex enhancement method, we first calculate the feature image of the original image, which includes the mean value and standard deviation of each pixel in the original image and its correlation image. Second, by applying the FCM clustering algorithm to the feature image, a mask is generated that can distinguish the structure part from the background part in the OCT image. Then, the mask is applied to the multi-scale retinex algorithm, and only the structure part in the OCT image is enhanced. Moreover, the BM3D method is applied to filter the enhanced image. Experimental results demonstrate that the proposed method performs impressively in improving the contrast and removing the speckle noise of OCT images, and it provides better quantitative performance in terms of signal-to-noise ratio, contrast-to-noise ratio, equivalent number of looks, and the edge preservation parameter $ \beta $β.

The fringe orientation is an important feature of the electronic speckle interferometry (ESPI) fringe pattern. Accurate and efficient calculation of the fringe orientation is very important for subsequent electronic speckle processing such as skeleton extraction and image filtering. To accurately and efficiently estimate fringe orientation, we propose an effective method based on a convolutional neural network. In the proposed method, the network needs clean-noisy image pairs to train and noisy images with theoretical value to test. The aligned noise-free ESPI fringe pattern orientation fields are fairly good estimations for the corresponding noise ones. After the model training is done, the other multiple ESPI fringe patterns are fed to the trained network simultaneously; the corresponding orientation results can be obtained accurately and efficiently. The advantage of using this method to extract the orientation is that the fringe orientation information can be extracted accurately and efficiently without complicated parameter adjustment. We evaluate the performance of our method via applying our method to the computer-simulated and experimentally acquired ESPI fringe patterns and comparing the results with those of three extensively used methods.

In this work, we study the characteristics of self-pulsing in a polarization-maintained fiber amplifier operated with different linewidths based on white noise source phase modulation. It indicates that the self-pulsing is almost simultaneous with the stimulated Brillouin scattering process, and its threshold is increasing near-linearly with the linewidth. By optimizing the laser structure, the threshold of self-pulsing increases by a factor of 1.5. We demonstrate a high-power linear polarization and all-fiberized amplifier with narrow linewidth and near-diffraction-limited beam quality. The output power scales to 1.5 kW with the pumping efficiency of 83%. The full width at half-maximum linewidth was measured to be 13 GHz. The polarization extinction ratio was larger than 13 dB. The beam quality M 2 was about 1.14 at the maximum laser power.

In this paper, we propose a total variation based on block matching 3D (BM3D-TV method), which includes the total variation regular term, the data fidelity term, and the block matching term. In addition, we also propose a fast numerical algorithm based on the split Bregman iteration for the proposed method. By assigning suitable weights to the data fidelity term and block matching term, the image noise reduction and the image structural characteristics can be matched optimally. We test the proposed method on six human retinal and one mouse skin optical coherence tomography (OCT) images respectively, and also compare it with total variation (TV) and BM3D, which were proved to be effective in denoising. The performances of these methods are quantitatively evaluated in terms of the signal-to-noise ratio, the contrast-to-noise ratio, and the averaged equivalent number of homogeneous areas at the aspects of speckle reduction and structure protection. Vast experiments show that the BM3D-TV method can effectively reduce speckle noise in OCT images, protect important structural information and improve image quality, compared with the BM3D and TV methods.

A terahertz metamaterial resonator consisting of two rectangular strips embedded in a closed-ring resonator is designed and investigated in this paper that can simultaneously generate the classical electromagnetically induced transparency effect and two Fano resonance modes. The formation of the transparent window and the Fano dips can be attributed to the coupling of the localized resonance modes between the closed-ring resonator and the two embedded rectangular strips. The effect of embedded rectangular strip sizes on the transmitted performance is discussed, and it is found that the resonant performance of the transparent window and Fano effect depends mainly on the length of the rectangular strips in the longitudinal direction. Based on this, two different device applications related to the embedded rectangular strips are discussed. The metamaterial proposed here could open up new avenues toward the control of terahertz waves in many technology-related areas.

The denoising of electronic speckle pattern interferometry (ESPI) fringe patterns is a key step in the application of ESPI. In this paper, we propose a method for batch denoising of ESPI fringe patterns based on a convolution neural network (CNN). In the proposed method, the network is first trained by our training dataset, which consists of the noisy ESPI fringe patterns and the corresponding noise-free images. We propose a new computer-simulated method of ESPI fringe patterns to create our training dataset. After training, the other multi-frame ESPI fringe patterns to be processed are fed to the trained network simultaneously, and the corresponding denoising images can be obtained in batches. We demonstrate the performance of the proposed method via application to 50 computer-simulated ESPI fringe patterns and three groups of experimentally obtained ESPI fringe patterns. The experimental results show that our method can obtain desired results even when the quality of ESPI fringe images is considerably low because of variable density, high noise, and low contrast, and our method can denoise multi-frame fringe patterns simultaneously. Moreover, we use the computer-simulated ESPI fringe patterns to train the network; after training, the trained network can be used to denoise either computer-simulated ESPI fringe patterns or the experimentally obtained ESPI fringe patterns. The proposed method is especially suitable for processing a large number of ESPI fringe patterns.

The filtering of ESPI fringe patterns with both noise and discontinuity is a challenging problem raised in recent years. Discontinuity-detectable and discontinuity-aware processing techniques are demanded. In this paper, a fuzzy c-means (FCM) clustering based fringe segmentation method is proposed. By applying the FCM clustering method to the estimated fringe orientation, we segment the discontinuous ESPI fringe patterns into continuous segments, thus the discontinuous region is identified and a discontinuous region mask is generated. Then, the discontinuous region mask is introduced into the controlling speed function, and an adaptive shape-preserving oriented partial differential equation (OPDE) model is proposed for discontinuous ESPI fringe patterns denoising. According to our method, the discontinuous regions are effectively found and with the proposed adaptive shape-preserving OPDE, the noise is well eliminated, the shape of fringes and the discontinuity are well kept. The performance of our method is illustrated with three computer-simulated and one experimentally obtained discontinuous ESPI fringe patterns and comparison with related segmentation methods and OPDEs.

Phase unwrapping is one of the key steps of optical interferogram analysis, among which phase discontinuity is still a challenge. In this paper, we propose a new weighted least-squares phase-unwrapping algorithm for discontinuous optical phase patterns. In the proposed algorithm, the orientation coherence is introduced to define the new weighting coefficient, which can accurately show the wrapped phase quality. According to our proposed algorithm, the new weighting coefficient has a good performance on distinguishing the continuous regions and the discontinuous regions in wrapped phase patterns. This advantage of our algorithm can ensure a more reliable unwrapped result for discontinuous optical phase patterns. We test the proposed algorithm on the computer-simulated speckle phase images and two experimentally obtained phase images, respectively, and compare them with the other five widely used methods. The experimental results demonstrate the performance of our new weighted least-squares phase-unwrapping algorithm.

We propose an optical single-channel color image cryptosystem based on vector decomposition and three-dimensional (3D) chaotic maps. In the proposed cryptosystem, the color image is first decomposed into R, G, and B channels, and then the three channels are individually scrambled by a generalized Arnold transform. Subsequently, the scrambled channels are phase encoded and then synthesized into one vector. The synthesized vector is used as the input image of the double random phase encoding (DRPE) in the gyrator domain. The introduction of the vector decomposition enables the cryptosystem to realize single-channel color image encryption, and the first phase mask of DRPE acts as the main secret key when the input image is a non-negative amplitude-only map. In addition, the phase masks of DRPE are generated by a 3D logistic map, which can facilitate the update, management, and transmission of the phase keys. Numerical simulation results demonstrate that the proposed cryptosystem is robust against various attacks and outperforms the other relevant cryptosystems. Furthermore, the proposed cryptosystem can be directly extended to encrypt multiple color images.

kW-level 1030 nm polarization-maintained fiber laser with narrow linewidth and near-diffraction-limited beam quality is demonstrated. Theoretical simulations based on the power balance equation are first performed to optimize the system parameters of the 1030 nm ytterbium-doped fiber laser for the maximum suppression of amplified spontaneous emission (ASE). With the optimized parameters, both the copumped and counterpumped MOPA lasers are implemented to obtain an output power of >1  kW. In both cases, the ASE suppression ratio reaches 40 dB with a 3 dB linewidth of about 0.14 nm, and the polarization extinction ratio is about 12 dB at 1 kW of output power. The beam quality starts degrading at 900 W of output power in the copumped structure, but maintains nearly single mode (Mx2,My2)=(1.07,1.12) until power is over 1 kW in the counterpumped structure.

We use Monte Carlo ray-tracing modeling to follow the stochastic trajectories of rays entering a cylindrical port from inside an integrating sphere. This allows us to study and quantify properties of realistic ports of non-negligible length, as opposed to the common thin-port assumption used in most theoretical treatments, where the port is simply considered as a hole in the spherical wall. We show that most practical ports encountered in integrating sphere applications cannot be modeled as thin ports. Indeed, a substantial proportion of rays entering the port can be reflected back into the sphere, with port reflectances as high as 80% demonstrated on realistic examples. This can have significant consequences on estimates of the sphere multiplier and therefore pathlength inside the sphere, a critical parameter in many applications. Moreover, a nonzero port reflectance is inevitably associated with reduced transmittance through the port, with implications in terms of overall throughput. We also discuss angular redistribution effects in a realistic port and the consequences in terms of detected throughput within a fixed numerical aperture. Those results highlight the importance of real port effects for any quantitative predictions of optical systems using integrating spheres. We believe that those effects can be exploited to engineer ports for specific applications and improve the overall sphere performance in terms of pathlength or throughput. This work carries important implications in our theoretical understanding of integrating spheres and on the practical design of optical systems using them.

In this paper, we propose an image decomposition model Shearlet-Hilbert-L 2 with better performance for denoising in electronic speckle pattern interferometry (ESPI) fringe patterns. In our model, the low-density fringes, high-density fringes, and noise are, respectively, described by shearlet smoothness spaces, adaptive Hilbert space, and L 2 space and processed individually. Because the shearlet transform has superior directional sensitivity, our proposed Shearlet-Hilbert-L 2 model achieves commendable filtering results for various types of ESPI fringe patterns, including uniform density fringe patterns, moderately variable density fringe patterns, and greatly variable density fringe patterns. We evaluate the performance of our proposed Shearlet-Hilbert-L 2 model via application to two computer-simulated and nine experimentally obtained ESPI fringe patterns with various densities and poor quality. Furthermore, we compare our proposed model with windowed Fourier filtering and coherence-enhancing diffusion, both of which are the state-of-the-art methods for ESPI fringe patterns denoising in transform domain and spatial domain, respectively. We also compare our proposed model with the previous image decomposition model BL-Hilbert-L 2 .

This paper presents an adaptive reconstruction method for coded aperture temporal compressive imaging. A pixel-wise equal-exposure coding strategy is first implemented to induce speckle-like features in the moving area of the measured image. A moving area detection method is then proposed to reconstruct the adaptively segmented moving areas into a series of video frames, which are filled back into the stationary clear background. Both simulation and experimental results demonstrate that the proposed method significantly reduces the time consumption of video reconstruction without degradation of image quality.

Filtering off speckle noise from a fringe image is one of the key tasks in electronic speckle pattern interferometry (ESPI). In general, ESPI fringe images can be divided into three categories: low-density fringe images, high-density fringe images, and variable-density fringe images. In this paper, we first present a general filtering method based on variational image decomposition that can filter speckle noise for ESPI fringe images with various densities. In our method, a variable-density ESPI fringe image is decomposed into low-density fringes, high-density fringes, and noise. A low-density fringe image is decomposed into low-density fringes and noise. A high-density fringe image is decomposed into high-density fringes and noise. We give some suitable function spaces to describe low-density fringes, high-density fringes, and noise, respectively. Then we construct several models and numerical algorithms for ESPI fringe images with various densities. And we investigate the performance of these models via our extensive experiments. Finally, we compare our proposed models with the windowed Fourier transform method and coherence enhancing diffusion partial differential equation filter. These two methods may be the most effective filtering methods at present. Furthermore, we use the proposed method to filter a collection of the experimentally obtained ESPI fringe images with poor quality. The experimental results demonstrate the performance of our proposed method.

It is a key step to remove the massive speckle noise in electronic speckle pattern interferometry (ESPI) fringe patterns. In the spatial-domain filtering methods, oriented partial differential equations have been demonstrated to be a powerful tool. In the transform-domain filtering methods, the shearlet transform is a state-of-the-art method. In this paper, we propose a filtering method for ESPI fringe patterns denoising, which is a combination of second-order oriented partial differential equation (SOOPDE) and the shearlet transform, named SOOPDE-Shearlet. Here, the shearlet transform is introduced into the ESPI fringe patterns denoising for the first time. This combination takes advantage of the fact that the spatial-domain filtering method SOOPDE and the transform-domain filtering method shearlet transform benefit from each other. We test the proposed SOOPDE-Shearlet on five experimentally obtained ESPI fringe patterns with poor quality and compare our method with SOOPDE, shearlet transform, windowed Fourier filtering (WFF), and coherence-enhancing diffusion (CEDPDE). Among them, WFF and CEDPDE are the state-of-the-art methods for ESPI fringe patterns denoising in transform domain and spatial domain, respectively. The experimental results have demonstrated the good performance of the proposed SOOPDE-Shearlet.

The polarization-dependent transverse mode properties in a few-mode nonpolarization-maintaining (non-PM) fiber amplifier with PM fiber laser seeding are investigated. In addition, the effect of spectral broadening on degeneration of the degree of polarization (DOP) is studied. The experimental results show that a high DOP can be achieved if the output laser of the non-PM fiber amplifier has good beam quality and a narrow linewidth without any broadening. A kilowatt (kW) fiber laser system with PM fiber seed and a non-PM fiber amplifier is demonstrated with the DOP at around 90% by controlling the beam quality and spectrum broadening. With the active polarization control technique, the system can achieve a kW linearly polarized output with 6.5 GHz linewidth. The M 2 is around 1.1, and the polarization extinction ratio is about 14.5 dB. Unfortunately, the random self-pulsing issue arose when the output power exceeded 1093 W. We believe that a higher power of linearly polarized laser output based on adaptive polarization control in a non-PM fiber amplifier will be obtained if the self-pulsing issue is solved.

We propose an optical color image encryption system based on the single-lens Fourier transform, the Fresnel transform, and the chaotic random phase masks (CRPMs). The proposed encryption system contains only one optical lens, which makes it more efficient and concise to implement. The introduction of the Fresnel transform makes the first phase mask of the proposed system also act as the main secret key when the input image is a non-negative amplitude-only map. The two CRPMs generated by dual two-dimensional chaotic maps can provide more security to the proposed system. In the proposed system, the key management is more convenient and the transmission volume is reduced greatly. In addition, the secret keys can be updated conveniently in each encryption process to invalidate the chosen plaintext attack and the known plaintext attack. Numerical simulation results have demonstrated the feasibility and security of the proposed encryption system.

The skeletonization methods based on gradient vector fields (GVFs) have been a powerful tool for electronic speckle pattern interferometry (ESPI) fringe patterns. However, the skeletonization of ESPI fringe patterns with variable density has been an open problem in this area. In this paper, we propose a novel method for calculating GVFs based on the variational image decomposition of ESPI fringe patterns with variable density. In the proposed method, the GVFs of low-density regions are described in Beppo-Levi space, the high-density regions in Hilbert space and the noise regions in curvelet space. The GVFs of a whole image are the sum of the decomposed GVFs of low-density regions and high-density regions. The skeletons of ESPI fringe patterns with variable density can be obtained based on the topological analysis of the GVFs of a whole image. We apply the proposed method to a computer-simulated and two experimentally obtained ESPI fringe patterns with variable density and compare them with the related skeleton methods based on GVFs. The experimental results have demonstrated that the proposed method outperforms the other methods, even when the quality of the ESPI fringe patterns is considerably low.

In this paper, we first present a general method for the construction of transform-domain filters for the removal of speckle noise in electronic speckle pattern interferometry (ESPI) wrapped phase patterns. Then we review the construction of previously published representative transform-domain filters, including the Fourier transform (FT), the wavelet transform, windowed Fourier filtering, and the localized FT filter. Furthermore, we fully evaluate the performance of the four filters via a variety of test cases and a comparable mean square error. Lastly, we also propose three new transform-domain filters for the removal of speckle noise in ESPI based on the general method. The three new methods are developed by selecting the input image function and filter function with better performance. The filtering results of the three proposed methods have been demonstrated via comparisons with four previously published filters.

Oriented partial differential equation (OPDE)-based filtering methods have been demonstrated to be a powerful tool for denoising while preserving all fringes. In this paper we propose new OPDE-filtering models, named parabolic-hyperbolic oriented partial differential equations (PH-OPDEs), based on variational methods. We test the proposed PH-OPDEs on two computer-simulated and two experimentally obtained ESPI fringe patterns with poor quality, and compare our models with related OPDE models. The experimental results have demonstrated that the new models have significantly better performance in numerical stability and computational efficiency as compared with the previous OPDE models.