Degree Of Linear Polarization Images Research Articles

Polarization image fusion method with image enhancement

Because of their complementary characteristics, intensity images and polarization images are often fused to produce information-rich images. However, the polarization characteristics are easily affected by the object’s environment, and the image fusion process may lose important information. In this paper, we propose an unsupervised end-to-end network framework based on a CNN for intensity images and degree of linear polarization images. First, we construct our own polarization dataset to solve the limitations of the training dataset; a hybrid loss function is designed to form an unsupervised learning process; and a Laplace operator enhancement layer is introduced into the network to further improve the quality of the fused images. Subjective and objective comparison experiments prove that the proposed fusion network is visually superior to several classical fusion methods.

Joint polarization detection and degradation mechanisms for underwater image enhancement.

Light absorption and scattering exist in the underwater environment, which can lead to blurring, reduced brightness, and color distortion in underwater images. Polarized images have the advantages of eliminating underwater scattering interference, enhancing contrast, and detecting material information of the object in underwater detection. In this paper, from the perspective of polarization imaging, different concentrations (0.15g/ml, 0.30g/ml, and 0.50g/ml), different wave bands (red, green, and blue), different materials (copper, wood, high-density PVC, aluminum, cloth, foam, cloth sheet, low-density PVC, rubber, and porcelain tile), and different depths (10cm, 20cm, 30cm, and 40cm) are set up in a chamber for the experimental environment. By combining the degradation mechanism of underwater images and the analysis of polarization detection results, it is proved that the degree of polarization images have greater advantages than degree of linear polarization images, degree of circular polarization images, S1, S2, and S3 images, and visible images underwater. Finally, a fusion algorithm of underwater visible images and polarization images based on compressed sensing is proposed to enhance underwater degraded images. To improve the quality of fused images, we introduce orthogonal matching pursuit (OMP) in the high-frequency part to improve image sparsity and consistency detection in the low-frequency part to improve the image mutation phenomenon. The fusion results show that the peak SNR values of the fusion result maps using OMP in this paper are improved by 32.19% and 22.14% on average over those using backpropagation and subspace pursuit methods. With different materials and concentrations, the underwater image enhancement algorithm proposed in this paper improves information entropy, average gradient, and standard deviation by 7.76%, 18.12%, and 40.8%, respectively, on average over previous algorithms. The image NIQE value shows that the image quality obtained by this paper's algorithm is improved by about 69.26% over the original S0 image.

Enhancement method for high colour polarization region under low irradiance condition

Due to the significant impact of low illumination environments on polarization imaging, polarization information is heavily influenced by noise interference. To address this issue, we propose a fusion method to enhance the high colour polarization area in low irradiance conditions. Firstly, we obtain the colour degree of linear polarization (DoLP) image, the colour angle of polarization (AoP) image, and the colour intensity (S0) image by the colour polarization camera. We normalize the S0 image. Subsequently, we map the colour AoP information in combination with the colour DoLP threshold to the H channel. The visual saliency information and brightness information of the colour DoLP image and the colour S0 image are mapped to the S channel and V channel, respectively. Finally, the data is transferred to the RGB space. Experiments show that our method surpasses several existing methods in both visual quality and quantitative measurement.

Tri-zone flame spatial structure imaging combined with endogenic polarized scattering.

We propose a multi-mode optical imaging method to retrieve the 2D and 3D spatial structures of the preheating, reaction, and recombination zones of an axisymmetric steady flame. In the proposed method, an infrared camera, a visible light monochromatic camera, and a polarization camera are triggered synchronously to capture 2D flame images, and their corresponding 3D images are reconstructed by combining different projection position images. The results of the experiments conducted indicate that the infrared and visible light images represent the flame preheating and flame reaction zones, respectively. The polarized image can be obtained by computing the degree of linear polarization (DOLP) of raw images captured by the polarization camera. We discover that the highlighted regions in the DOLP images lie outside the infrared and visible light zones; they are insensitive to the flame reaction and have different spatial structures for different fuels. We deduce that the combustion product particles cause endogenic polarized scattering, and that the DOLP images represent the flame recombination zone. This study focuses on the combustion mechanisms, such as the formation of combustion products and quantitative flame composition and structure.

Polarization image fusion with self-learned fusion strategy

Polarization image fusion aims to integrate intensity and degree of linear polarization images into one with more details, which is beneficial to improve the ability of targets detection under complex background. The fusion strategies in conventional methods are designed in a hand-crafted way and not robust to different fusion tasks. In this paper, we propose a novel and deep network to address the polarization image fusion issue with self-learned strategy. The network consists of Encoder, Fusion, and Decoder layers. Feature maps extracted by Encoder are fused, then fed into Decoder to generate fused images. Besides, a novel loss function is adopted to train the network in an unsupervised way, without ground truth of fused images. To verify the advantage, the network trained on polarization images is also used to infrared and visible images fusion, and multi-focus image fusion. Experimental results showed that our method outperforms several state-of-the-art methods in terms of visual quality and quantitative measurement. The proposed fused method can be applied in the military and civilian fields such as camouflage and hidden targets detection, medical diagnosis, and environmental monitoring.

Analysis of signal-to-noise ratio of angle of polarization and degree of polarization

Recent advancements in nanofabrication technology has led to commercialization of single-chip polarization and color-polarization imaging sensors in the visible spectrum. Novel applications have arisen with the emergence of these sensors leading to questions about noise in the reconstructed polarization images. In this paper, we provide theoretical analysis for the input and output referred noise for the angle and degree of linear polarization information. We validated our theoretical model with experimental data collected from a division of focal plane polarization sensor. Our data indicates that the noise in the angle of polarization images depends on both incident light intensity and degree of linear polarization and is independent of the incident angle of polarization. However, noise in degree of linear polarization images depends on all three parameters: incident light intensity, angle and degree of linear polarization. This theoretical model can help guide the development of imaging setups to record optimal polarization information.

PFNet: an unsupervised deep network for polarization image fusion.

Image fusion is the key step to improve the performance of object detection in polarization images. We propose an unsupervised deep network to address the polarization image fusion issue. The network learns end-to-end mapping for fused images from intensity and degree of linear polarization images, without the ground truth of fused images. Customized architecture and loss function are designed to boost performance. Experimental results show that our proposed network outperforms other state-of-the-art methods in terms of visual quality and quantitative measurement.

A New Polarization Image Demosaicking Algorithm by Exploiting Inter-Channel Correlations With Guided Filtering

This paper presents a fast and effective polarization image demosaicking algorithm, which explores inter-channel dependency of Stokes parameters for the minimization of residual aliasing artifacts after cubic spline interpolation. A guided filtering approach is used for denoising. An optimization based on the confidence level of the aforementioned guided filtering, the correlations between the demosaicked image and input, as well as the total intensity, angle and degree of linear polarization, is constructed and solved with Newton’s method. Experimental results demonstrate that the proposed algorithm can surpass the existing methods in terms of both objective root mean squared error and structural similarity index by at least 36.0% and 3.4%, respectively, and by close visual inspection of the clarity of objects in the angle and degree of linear polarization images. The proposed algorithm consists of only convolutions and element-wise operations, making it fast and parallelizable for efficient GPU acceleration. An image of size $512\times 612\times 4$ can be processed within 10 s on i7-6700k CPU, and gains further 5 times speedup with M4000M GPU.

Snapshot multispectral polarization imaging using a photonic crystal filter array.

A new filter array and a demosaicking method for snapshot multispectral polarization imaging are proposed in this paper. The proposed filter array is a thin-film wavy multilayer structure regarded as a photonic crystal that can be fabricated using the autocloning method. The multispectral polarization filter array is developed by altering the wave structure of the photonic crystal at each pixel. In addition, we propose a demosaicking method for multispectral polarization images by considering snapshot imaging as a linear model. In the experiments, we evaluated the recovered spectrum error in some color charts and showed various demosaicked images such as multispectral polarization images, specific-band degree of linear polarization images, polarized RGB images, and non-polarized RGB images.

Polarized-pixel performance model for DoFP polarimeter

A division of a focal plane (DoFP) polarimeter is manufactured by placing a micropolarizer array directly onto the focal plane array (FPA) of a detector. Each element of the DoFP polarimeter is a polarized pixel. This paper proposes a performance model for a polarized pixel. The proposed model characterizes the optical and electronic performance of a polarized pixel by three parameters. They are respectively major polarization responsivity, minor polarization responsivity and polarization orientation. Each parameter corresponds to an intuitive physical feature of a polarized pixel. This paper further extends this model to calibrate polarization images from a DoFP (division of focal plane) polarimeter. This calibration work is evaluated quantitatively by a developed DoFP polarimeter under varying illumination intensity and angle of linear polarization. The experiment proves that our model reduces nonuniformity to 6.79% of uncalibrated DoLP (degree of linear polarization) images, and significantly improves the visual effect of DoLP images.

Nano-fabricated pixelated micropolarizer array for visible imaging polarimetry.

Pixelated micropolarizer array (PMA) is a novel concept for real-time visible imaging polarimetry. A 320 × 240 aluminum PMA fabricated by electron beam lithography is described in this paper. The period, duty ratio, and depth of the grating are 140 nm, 0.5, and 100 nm, respectively. The units are standard square structures and the metal nanowires of the grating are collimating and uniformly thick. The extinction ratio of 75 and the maximum polarization transmittance of 78.8% demonstrate that the PMA is suitable for polarization imaging. When the PMA is applied to real-time polarization imaging, the degree of linear polarization image and the angle of linear polarization image are calculated from a single frame image. The polarized target object is highlighted from the unpolarized background, and the surface contour of the target object can be reflected by the polarization angle.

In vivo characterization of human pigmented lesions by degree of linear polarization image maps using incident linearly polarized light

Melanoma is the most serious form of skin cancer and often appears as an evolving multicolored skin growth. It is well documented that pre-existing atypical or dysplastic nevi can evolve into a melanoma. The development of an in vivo imaging system to characterize benign and malignant nevi has been emphasized to aid in early detection of melanoma. The goal of this study is to utilize a novel Stokes polarimetry imaging (SPI) system for the characterization of pigmented lesions, and to evaluate the SPI system in comparison to dermoscopy and histology images. Linearly polarized light with varying incident polarization angles (IPA) illuminated various types of pigmented lesions. The melanocytic nesting patterns of pigmented lesions were characterized by constructing the degree-of-linear-polarization (DOLP) image map with comparison to dermoscopy and histology. The incident polarized light was filtered by visible filters for spectral imaging and incident deeply penetrating red light was used to correlate the SPI image with histopathological examination. The DOLP images with varying IPA at different visible wavelengths were used to characterize various kinds of pigmented lesions by showing subsurface melanocytic nesting distribution as well as morphological information with better resolution and contrast. In correlation with dermoscopy and histology, various defining features such as compound, junctional, lentiginous, reticular, globular patterns of melanocytic nests were identified. When imaging pigmented melanocytic lesions, the SPI system with varying IPA at the red light wavelength can better define the melanocytic nesting patterns in both the dermal epidermal junction and the dermis. The SPI system has the potential to be an effective in vivo method of detecting pre-malignant nevi and melanoma.

Detection of Tendon Tears by Degree of Linear Polarization Imaging

A Stokes polarimetry imaging (SPI) system was developed and utilized to detect tendon tears by constructing the degree of linear polarization (DOLP) image maps after linearly polarized light illumination. The micro and partial-thickness tears of turkey tendons were made and imaged by the SPI system at different incident polarization angles (IPA) with different mechanical loads on the tendon. The micro and partial-thickness tendon tears were detected by the DOLP images due to weak birefringence around the tears. The tendon tears were detected by a highest DOLP contrast at longest visible wavelength (Red, 650 ± 50 nm). All polarized images showed modulated DOLP as the incident polarization angle (IPA) was varied. The varying DOLP allowed the optimal detection of the micro and partial-thickness tendon tears at a certain IPA. The SPI system with variable IPA and spectral information can improve the detection of the tendon tears by higher visibility of fiber orientations, and thereby improve diagnosis and treatment of the tendon related injuries.

Stokes polarimetry imaging of rat tail tissue in a turbid medium: degree of linear polarization image maps using incident linearly polarized light

Illumination with incident linearly polarized light on tissue and polarization state measurements of the remitted light provide a means by which various tissue structures can be differentiated. A rat tail is embedded within a turbid gelatin such that there is a variable depth of medium above it. By varying the incident polarization angle (IPA) of the illuminating linearly polarized light, the geometry, and the orientation angle of the tissue, a series of 2-D degree of linear polarization image maps are created using our Stokes polarimetry imaging technique. The image maps show locations of the polarization-sensitive structures in the rat tail, including soft tissue, intervertebral disks, and tendons. The observed morphologies in the image maps indicate locations where the depolarization of light differs according to the tissue type and underlying layers. The data indicate the importance of varying the IPA, and that tissue dichroism and birefringence affect the degree of linear polarization image maps. Diagnostic information regarding subsurface tissue structures is obtained.