Abstract: A peony PoWOX11 gene and an application of a coded protein thereof are provided in the present disclosure, belonging to the field of biotechnology. The peony PoWOX11 gene is introduced into Arabidopsis thaliana to obtain a transgenic Arabidopsis thaliana plant stably expressing peony PoWOX11 gene, with bolting and flowering of the transgenic Arabidopsis thaliana plant delayed; where the peony PoWOX11 gene has a nucleotide sequence as shown in SEQ ID NO. 7, and a protein coded by the peony PoWOX11 gene has an amino acid sequence as shown in SEQ ID NO. 8.

Abstract: Disclosed are a peony PoWOX4 gene and applications of a coded protein thereof, belonging to the field of biotechnology. According to the present disclosure, Arabidopsis thaliana is taken as a model plant, and the peony PoWOX4 gene is transformed into Arabidopsis thaliana to promote the early bolting, flowering and vegetative growth of Arabidopsis thaliana.

Abstract: A tomographic imaging system is provided. The system receives measurements at frequencies of a wave-field scattered by an internal structure of an object, recursively reconstructs an image of the internal structure of the object until a termination condition is met, and renders the reconstructed image. For a current iteration, a frequency is added to previous frequencies used during a previous iteration to produce current frequencies, such that the added frequency is higher than frequencies present in the previous frequencies, and reconstructs a current image of the internal structure of the object that minimizes a difference between the scattered wave-field measured at the current frequencies and a wave-field synthetized from the current image. The wave-field synthesized from the current image is generated by a neural network operator. A previous image determined during the previous iteration initializes the reconstruction of the current image.

Abstract: Embodiment of the present disclosure disclose a tomographic imaging system for reconstructing an image of an internal structure of an object. An incident wavefield is transmitted into the object occupying a background domain embedding the object. The incident wavefield is scattered into multiple scattered wavefield by the object. The incident and scattered wavefields are measured as a total wavefield. The total wavefield propagates through a computational domain and a residual domain in the background domain that are defined by cross-domain and residual measurement operators. The total wavefield is used for the image reconstruction. The image is reconstructed by solving an optimization problem corresponding to the computational domain. The optimization problem is solved iteratively to minimize a difference between the total wavefield and a wavefield synthesized using a measurement operator and a Green's function operator from the reconstructed image.

Abstract: Systems and methods for image processing for determining a registration map between a first image of a scene with a second image of the scene, include solving an optimal transport (OT) problem to produce the registration map by optimizing a cost function that determines a minimum of a ground cost distance between the first and the second images modified with an epipolar geometry-based regularizer including a distance that quantifies the violation of an epipolar geometry constraint between corresponding points defined by the registration map. The ground cost compares a ground cost distance of features extracted within the first image with a ground cost distance of features extracted from the second image.

Abstract: Embodiment of the present disclosure disclose a tomographic imaging system for reconstructing an image of an internal structure of an object. An incident wavefield is transmitted into the object occupying a background domain embedding the object. The incident wavefield is scattered into multiple scattered wavefield by the object. The incident and scattered wavefields are measured as a total wavefield. The total wavefield propagates through a computational domain and a residual domain in the background domain that are defined by cross-domain and residual measurement operators. The total wavefield is used for the image reconstruction. The image is reconstructed by solving an optimization problem corresponding to the computational domain. The optimization problem is solved iteratively to minimize a difference between the total wavefield and a wavefield synthesized using a measurement operator and a Green's function operator from the reconstructed image.

Abstract: A permittivity sensor, for determining an image of a distribution of permittivity of a material of an object in a scene, comprising an input interface, a hardware processor, and an output interface is provided. The input interface is configured to accept phaseless measurements of propagation of a known incident field through the scene and scattered by the material of the object in the scene. The hardware processor is configured to solve a multi-variable minimization problem over unknown phases of the phaseless measurements and unknown image of the permittivity of the material of the object by minimizing a difference of a nonlinear function of the known incident field and the unknown image with a product of known magnitudes of the phaseless measurements and the unknown phases. Further, the output interface is configured to render the permittivity of the material of the object provided by the solution of the multi-variable minimization problem.

Abstract: Systems and methods for image processing for determining a registration map between a first image of a scene with a second image of the scene, include solving an optimal transport (OT) problem to produce the registration map by optimizing a cost function that determines a minimum of a ground cost distance between the first and the second images modified with an epipolar geometry-based regularizer including a distance that quantifies the violation of an epipolar geometry constraint between corresponding points defined by the registration map. The ground cost compares a ground cost distance of features extracted within the first image with a ground cost distance of features extracted from the second image.

Abstract: An imaging system to reconstruct a reflectivity image of a scene including an object moving with the scene. A tracking system to track a deforming object to estimate an object deformation for each time step. Sensors acquire snapshots of the scene, each acquired snapshot of the object includes measurements in the object deformation for that time step, to produce a set of object measurements with deformed shapes over the time steps. Compute a correction to estimates of object deformation for each time step, with matching measurements of the corrected object deformation for each time step to measurements in the acquired snapshot of object for that time step. Select a corrected deformation over other corrected deformations for each time step, according to a distance between the corrected deformation and the estimate of the deformation, to obtain a final estimate of the deformation of the deformable object moving in the scene.

Abstract: Systems, methods and apparatus for image processing for reconstructing a super resolution image from multispectral (MS) images. Receive image data and initialize a fused image using a panchromatic (PAN) image, and estimate a blur kernel between the PAN image and the MS images as an initialization function. Iteratively, fuse a MS image with an associated PAN image of a scene using a fusing algorithm. Each iteration includes: update the blur kernel based on a Second-Order Total Generalized Variation function to regularize a kernel shape; fuse the PAN image and MS images with the updated blur kernel based on a local Laplacian prior function to regularize the high-resolution information to obtain an estimated fused image; compute a relative error between the estimated fused image of the current iteration and a previous estimated fused image from a previous iteration, to a predetermined threshold, to stop iterations stop, to obtain a PAN-sharpened image.

Abstract: An imaging system to reconstruct a reflectivity image of a scene including an object moving with the scene. A tracking system to track a deforming object to estimate an object deformation for each time step. Sensors acquire snapshots of the scene, each acquired snapshot of the object includes measurements in the object deformation for that time step, to produce a set of object measurements with deformed shapes over the time steps. Compute a correction to estimates of object deformation for each time step, with matching measurements of the corrected object deformation for each time step to measurements in the acquired snapshot of object for that time step. Select a corrected deformation over other corrected deformations for each time step, according to a distance between the corrected deformation and the estimate of the deformation, to obtain a final estimate of the deformation of the deformable object moving in the scene.

Abstract: Systems, methods and apparatus for image processing for reconstructing a super resolution image from multispectral (MS) images. Receive image data and initialize a fused image using a panchromatic (PAN) image, and estimate a blur kernel between the PAN image and the MS images as an initialization function. Iteratively, fuse a MS image with an associated PAN image of a scene using a fusing algorithm. Each iteration includes: update the blur kernel based on a Second-Order Total Generalized Variation function to regularize a kernel shape; fuse the PAN image and MS images with the updated blur kernel based on a local Laplacian prior function to regularize the high-resolution information to obtain an estimated fused image; compute a relative error between the estimated fused image of the current iteration and a previous estimated fused image from a previous iteration, to a predetermined threshold, to stop iterations stop, to obtain a PAN-sharpened image.

Abstract: A permittivity sensor, for determining an image of a distribution of permittivity of a material of an object in a scene, comprising an input interface, a hardware processor, and an output interface is provided. The input interface is configured to accept phaseless measurements of propagation of a known incident field through the scene and scattered by the material of the object in the scene. The hardware processor is configured to solve a multi-variable minimization problem over unknown phases of the phaseless measurements and unknown image of the permittivity of the material of the object by minimizing a difference of a nonlinear function of the known incident field and the unknown image with a product of known magnitudes of the phaseless measurements and the unknown phases. Further, the output interface is configured to render the permittivity of the material of the object provided by the solution of the multi-variable minimization problem.

Abstract: Systems and methods for image processing including a memory having stored multi-angled view images of a scene. Each multi-angled view image includes pixels, and at least one multi-angled view image includes a clouded area in the scene, resulting in missing pixels. A processor configured to align three multi-angled view images in the multi-angled view images to a target view angle of the scene, to form a set of aligned multi-angled view images representing a target point of view of the scene, at least one aligned multi-angled view image of the at least three multi-angled view images, has missing pixels due to the clouded area. Form a matrix using vectorized aligned multi-angled view images, wherein the matrix is incomplete due to the missing pixels. Complete the matrix using a matrix completion to combine the aligned multi-angled view images to produce a fused image of the scene without the clouded area.

Abstract: Various examples of methods and systems are provided for approximate message passing with universal denoising. In one example, a method includes applying an approximate message passing (AMP) conversion framework to a plurality of substantially linear measurements to produce a plurality of scalar measurements; applying a denoiser algorithm to the plurality of scalar measurements to generate a plurality of denoised scalar measurements; and applying a conversion term to the plurality of denoised scalar measurements to convert the plurality of denoised scalar measurements to a plurality of denoised substantially linear measurements.

Abstract: Systems and methods for image processing including a memory having stored multi-angled view images of a scene. Each multi-angled view image includes pixels, and at least one multi-angled view image includes a clouded area in the scene, resulting in missing pixels. A processor configured to align three multi-angled view images in the multi-angled view images to a target view angle of the scene, to form a set of aligned multi-angled view images representing a target point of view of the scene, at least one aligned multi-angled view image of the at least three multi-angled view images, has missing pixels due to the clouded area. Form a matrix using vectorized aligned multi-angled view images, wherein the matrix is incomplete due to the missing pixels. Complete the matrix using a matrix completion to combine the aligned multi-angled view images to produce a fused image of the scene without the clouded area.

Abstract: Various examples of methods and systems are provided for compressive imaging using approximate message passing with denoising. According to an aspect, a method includes applying an approximate message passing (AMP) conversion framework to a plurality of substantially linear measurements for conversion into a plurality of scalar measurements. A denoiser algorithm can be applied to the plurality of scalar measurements to generate a plurality of denoised scalar measurements. Further, a conversion term can be applied to the plurality of denoised scalar measurements for converting the plurality of denoised scalar measurements to a plurality of denoised substantially linear measurements. The plurality of substantially linear measurements can represent two-dimensional or three-dimensional signals.

Abstract: Various examples of methods and systems are provided for approximate message passing with universal denoising. In one example, a method includes applying an approximate message passing (AMP) conversion framework to a plurality of substantially linear measurements to produce a plurality of scalar measurements; applying a denoiser algorithm to the plurality of scalar measurements to generate a plurality of denoised scalar measurements; and applying a conversion term to the plurality of denoised scalar measurements to convert the plurality of denoised scalar measurements to a plurality of denoised substantially linear measurements.

Abstract: Various examples of methods and systems are provided for compressive imaging using approximate message passing with denoising. According to an aspect, a method includes applying an approximate message passing (AMP) conversion framework to a plurality of substantially linear measurements for conversion into a plurality of scalar measurements. A denoiser algorithm can be applied to the plurality of scalar measurements to generate a plurality of denoised scalar measurements. Further, a conversion term can be applied to the plurality of denoised scalar measurements for converting the plurality of denoised scalar measurements to a plurality of denoised substantially linear measurements. The plurality of substantially linear measurements can represent two-dimensional or three-dimensional signals.