Abstract: Image pair co-registration systems and methods include receiving a pair of multi-modal images, defining a parametric deformation model, defining a loss function that is minimized when the pair of images are aligned, and performing a multi-scale search to determine deformation parameters that minimize the loss function. The optimized deformation parameters define an alignment of the pair of images. The pair of images may include visible spectrum image and an infrared image. The method further includes resizing the visible spectrum image to match the infrared image, applying at least one lens distortion correction model, and normalizing a dynamic range of each of the pair of images. The multi-scale search may further include resizing the pair of images to a current processing scale, applying adaptive histogram equalization to the pair of images to generate equalized images, applying Gaussian Blur to the equalized images, and optimizing the deformation parameters.

Abstract: Techniques are disclosed for radar data denoising systems and methods. In one example, a method includes receiving radar data. The method further includes performing a first transform associated with the radar data to obtain transformed radar data. The transformed radar data is associated with a location parameter and a variance that is independent of the location parameter. The method further includes performing a second transform of the transformed radar data to obtain dimensionality-reduced radar data. The method further includes filtering the dimensionality-reduced radar data to obtain denoised dimensionality-reduced radar data. Related devices and systems are also provided.

Abstract: A system comprises a digital micromirror device (DMD), an image sensor comprising an array of sensors operable to capture an image of a scene, a readout integrated circuit (ROIC) operable to generate signals from the sensors corresponding to the captured image of the scene, and an image reconstruction module. The image sensor is operable to capture an image of a scene and comprises an array of photodetector sensors operable to capture an image of a scene at a first frame rate, and a read a readout integrated circuit (ROIC) operable to generate signals from the photodetector sensors corresponding to the captured image of the scene at a second frame rate.

Abstract: Techniques are disclosed for panoramic image construction based on images captured by rotating imagers. In one example, a method includes receiving a first sequence of images associated with a scene and captured during continuous rotation of an image sensor. Each image of the first sequence has a portion that overlaps with another image of the first sequence. The method further includes generating a first panoramic image. The generating includes processing a second sequence of images based on a point-spread function to mitigate blur associated with the continuous rotation to obtain a deblurred sequence of images, and processing the deblurred sequence based on a noise power spectral density to obtain a denoised sequence of images. The point-spread function is associated with the image sensor's rotation speed. The second sequence is based on the first sequence. The first panoramic image is based on the denoised sequence.

Abstract: Techniques are disclosed for point cloud denoising systems and methods. In one example, a method includes determining a respective local coordinate system for each point of a point cloud. The method further includes determining a respective first adaptive-shape neighborhood for each point of the point cloud based on each respective local coordinate system. The method further includes performing filtering associated with each respective first adaptive-shape neighborhood to obtain a respective second adaptive-shape neighborhood for each point of the point cloud. The method further includes determining local estimates for points inside each of the second adaptive-shape neighborhoods. The method further includes aggregating the local estimates for each point of the point cloud to obtain a denoised point cloud. Related devices and systems are also provided.

Abstract: Systems and methods for registration of image pairs, including camera response function normalization are disclosed and include selecting a pair of images from a set of images, each of the pair of images having associated metadata, determining a camera response function for each of the images in the pair of images using the associated metadata, normalizing each camera response function for across the set of images, and applying the normalized camera response function to the pair of images. A deformation map is generated in a multi-scale process using registration parameters, to deform one of the pair of images to align with another of the pair of images. The image pairs may be selected by identifying image pairs having an overlap that exceeds an overlap threshold, having a sequential proximity in an image series satisfying a proximity threshold, and/or having estimated image capture attitudes within an attitude threshold.

Abstract: Image pair co-registration systems and methods include receiving a pair of multi-modal images, defining a parametric deformation model, defining a loss function that is minimized when the pair of images are aligned, and performing a multi-scale search to determine deformation parameters that minimize the loss function. The optimized deformation parameters define an alignment of the pair of images. The pair of images may include visible spectrum image and an infrared image. The method further includes resizing the visible spectrum image to match the infrared image, applying at least one lens distortion correction model, and normalizing a dynamic range of each of the pair of images. The multi-scale search may further include resizing the pair of images to a current processing scale, applying adaptive histogram equalization to the pair of images to generate equalized images, applying Gaussian Blur to the equalized images, and optimizing the deformation parameters.

Abstract: Various techniques are disclosed for separating and removing low-frequency shadow or shading (also referred to herein as “non-uniformity”) from images that have been corrupted by the non-uniformity. A non-uniformity estimate that approximates the non-uniformity effect on the corrupted image may be generated by iteratively adding new blotches of non-uniformity data represented by two-dimensional (2D) functions, such as 2D Gaussian functions, to the non-uniformity estimate and applying filters to smoothen the 2D functions. In each iteration of the non-uniformity estimate generation process, a new non-uniformity update candidate that minimizes a cost function is identified. The corrupted image is processed based on the non-uniformity estimate to generate a corrected image.

Abstract: Techniques are disclosed for panoramic image construction based on images captured by rotating imagers. In one example, a method includes receiving a first sequence of images associated with a scene and captured during continuous rotation of an image sensor. Each image of the first sequence has a portion that overlaps with another image of the first sequence. The method further includes generating a first panoramic image. The generating includes processing a second sequence of images based on a point-spread function to mitigate blur associated with the continuous rotation to obtain a deblurred sequence of images, and processing the deblurred sequence based on a noise power spectral density to obtain a denoised sequence of images. The point-spread function is associated with the image sensor's rotation speed. The second sequence is based on the first sequence. The first panoramic image is based on the denoised sequence.

Abstract: Various techniques are disclosed for reducing noise and enhancing sharpness of an input image. For example, a method includes performing an initial collaborative filtering and sharpening on the input image to generate a pilot image, using the pilot image to derive coefficients that are used to perform a second collaborative filtering on the input image to generate a filtered image. In some embodiments, the collaborative filtering and sharpening is performed using parameters that boost or enhance the differences in pixel values for the same spatial locations of the matched image blocks extracted during the collaborative filtering and sharpening process. Accordingly, the method according to various embodiments performs especially well for images that have weak spatial correlations among mutually similar blocks.

Abstract: Techniques are disclosed for point cloud denoising systems and methods. In one example, a method includes determining a respective local coordinate system for each point of a point cloud. The method further includes determining a respective first adaptive-shape neighborhood for each point of the point cloud based on each respective local coordinate system. The method further includes performing filtering associated with each respective first adaptive-shape neighborhood to obtain a respective second adaptive-shape neighborhood for each point of the point cloud. The method further includes determining local estimates for points inside each of the second adaptive-shape neighborhoods. The method further includes aggregating the local estimates for each point of the point cloud to obtain a denoised point cloud. Related devices and systems are also provided.

Abstract: Various techniques are disclosed for systems and methods to provide image resolution enhancement. For example, a method includes: receiving a reference image (e.g., a visible light image) of a scene comprising image pixels identified by pixel coordinates; receiving a lower-resolution target image (e.g., an infrared image) of the scene; resizing the target image to a larger size; determining an adaptive-shape neighborhood for each pixel coordinate, wherein the adaptive-shape neighborhood extends from the each pixel coordinate such that those reference image pixels that are within the shape-adaptive neighborhood meet a regularity condition; determining, for each adaptive-shape neighborhood, a local estimate based on those target image pixels that are within the adaptive-shape neighborhood; and aggregating the local estimates associated with the adaptive-shape neighborhoods to provide a global estimate that corresponds to the target image with an improved resolution.

Abstract: A system comprises a digital micromirror device (DMD), an image sensor comprising an array of sensors operable to capture an image of a scene, a readout integrated circuit (ROIC) operable to generate signals from the sensors corresponding to the captured image of the scene, and an image reconstruction module. The image sensor is operable to capture an image of a scene and comprises an array of photodetector sensors operable to capture an image of a scene at a first frame rate, and a read a readout integrated circuit (ROIC) operable to generate signals from the photodetector sensors corresponding to the captured image of the scene at a second frame rate.

Abstract: Various techniques are disclosed for separating and removing low-frequency shadow or shading (also referred to herein as “non-uniformity”) from images that have been corrupted by the non-uniformity. A non-uniformity estimate that approximates the non-uniformity effect on the corrupted image may be generated by iteratively adding new blotches of non-uniformity data represented by two-dimensional (2D) functions, such as 2D Gaussian functions, to the non-uniformity estimate and applying filters to smoothen the 2D functions. In each iteration of the non-uniformity estimate generation process, a new non-uniformity update candidate that minimizes a cost function is identified. The corrupted image is processed based on the non-uniformity estimate to generate a corrected image.

Abstract: Various techniques are disclosed for reducing noise and enhancing sharpness of an input image. For example, a method includes performing an initial collaborative filtering and sharpening on the input image to generate a pilot image, using the pilot image to derive coefficients that are used to perform a second collaborative filtering on the input image to generate a filtered image. In some embodiments, the collaborative filtering and sharpening is performed using parameters that boost or enhance the differences in pixel values for the same spatial locations of the matched image blocks extracted during the collaborative filtering and sharpening process. Accordingly, the method according to various embodiments performs especially well for images that have weak spatial correlations among mutually similar blocks.

Abstract: Various techniques are disclosed for systems and methods to provide image resolution enhancement. For example, a method includes: receiving a reference image (e.g., a visible light image) of a scene comprising image pixels identified by pixel coordinates; receiving a lower-resolution target image (e.g., an infrared image) of the scene; resizing the target image to a larger size; determining an adaptive-shape neighborhood for each pixel coordinate, wherein the adaptive-shape neighborhood extends from the each pixel coordinate such that those reference image pixels that are within the shape-adaptive neighborhood meet a regularity condition; determining, for each adaptive-shape neighborhood, a local estimate based on those target image pixels that are within the adaptive-shape neighborhood; and aggregating the local estimates associated with the adaptive-shape neighborhoods to provide a global estimate that corresponds to the target image with an improved resolution.

Abstract: Various techniques are disclosed to suppress distortion in images (e.g., video or still images), such as distortion caused by atmospheric turbulence. For example, similar image blocks from a sequence of images may be identified and tracked along motion trajectories to construct spatiotemporal volumes. The motion trajectories are smoothed to estimate the true positions of the image blocks without random displacements/shifts due to the distortion, and the smoothed trajectories are used to aggregate the image blocks in their new estimated positions to reconstruct the sequence of images with the random displacements/shifts suppressed. Blurring that may remain within each image block of the spatiotemporal volumes may be suppressed by modifying the spatiotemporal volumes in a collaborative fashion.

Abstract: Various techniques are disclosed to effectively suppress noise in images (e.g., video or still images). For example, noise in images may be more accurately modeled as having a structured random noise component and a structured fixed pattern noise (FPN) component. Various parameters of noise may be estimated robustly and efficiently in real time and in offline processing. Noise in images may be filtered adaptively, based on various noise parameters and motion parameters. Such filtering techniques may effectively suppress noise even in images that have a prominent FPN component, and may also improve effectiveness of other operations that may be affected by noise.

Abstract: Various techniques are disclosed to suppress distortion in images (e.g., video or still images), such as distortion caused by atmospheric turbulence. For example, similar image blocks from a sequence of images may be identified and tracked along motion trajectories to construct spatiotemporal volumes. The motion trajectories are smoothed to estimate the true positions of the image blocks without random displacements/shifts due to the distortion, and the smoothed trajectories are used to aggregate the image blocks in their new estimated positions to reconstruct the sequence of images with the random displacements/shifts suppressed. Blurring that may remain within each image block of the spatiotemporal volumes may be suppressed by modifying the spatiotemporal volumes in a collaborative fashion.

Abstract: Various techniques are disclosed to effectively suppress noise in images (e.g., video or still images). For example, noise in images may be more accurately modeled as having a structured random noise component and a structured fixed pattern noise (FPN) component. Various parameters of noise may be estimated robustly and efficiently in real time and in offline processing. Noise in images may be filtered adaptively, based on various noise parameters and motion parameters. Such filtering techniques may effectively suppress noise even in images that have a prominent FPN component, and may also improve effectiveness of other operations that may be affected by noise.