The present invention discloses a multi-frame image super resolution system that utilizes both deep learning models and traditional models of enhancing the resolution of an image so that minimal computational resources are used. A frame alignment module of the invention aligns the frames of the image after which a processing module configured within the system process the Y and the UV channels of the image by using multiple deep and traditional resolution enhancement models. A merging unit merges the output of the processors to produce a super resolution image incorporating the advantages of both of the image enhancement methods.

A method for detecting defects in a formed metal part includes locating one or more regions of interest in a synthetic image of a part manufactured by a forming process. The synthetic image is informed based on a physics-based simulation of the forming process. The regions of interest indicate a high risk of having a defect from the forming process. A set of training images including real images of actual manufactured parts are registered with the synthetic image. The regions of interest are overlaid on each training image, to extract patches from the training images that correspond to high-risk regions. An anomaly detection model is trained on the patches extracted from the training images to detect a defect in a formed metal part from an acquired image of the formed metal part, by detecting an anomaly in a patch extracted from the acquired image that corresponds to a high-risk region.

A method for generating a 2D orthomosaic map including obtaining a series of images of a field from a camera located on a ground based vehicle, processing the series of images to mark pixels of the ground based vehicle and optionally an implement, identifying, marking, and removing pixels containing plants, stitching together the series of images into a single map, and reintroducing pixels containing plants into the single map.

An image processing method and a device configured to implement the same are disclosed. The method comprises: obtaining optical input from a hybrid imaging device, wherein an obtained optical input comprises a first component and a second component that temporally corresponds to the first component; wherein the first component of the obtained optical input corresponds to a first temporal resolution, while the second component of the obtained optical input corresponds to a second temporal resolution higher than that of the first component; performing image restoration operation on a first subset of the first component of the obtained optical input in accordance with data from the second component of the obtained optical input; and performing image fusion operation to generate fused image data from an output of the image restoration operation and a second subset of the first component of the obtained optical input.

Systems, methods, and non-transitory computer-readable media can perform operations comprising determining visual geometry detections (e.g., lane line detections) associated with geometry corresponding with a map; aligning the visual geometry detections with the geometry based on transformations associated with selected degrees of freedom; and determining a pose of a vehicle based on alignment of the visual geometry detections with the geometry.

To improve user convenience as to adjustment for vibration correction of a captured image captured by an image capturing apparatus, a feature portion evaluation unit refers to feature portions selected by a feature portion selection unit and determines whether feature portions necessary for vibration isolation have been acquired from a reference image. The feature portion evaluation unit has the function of notifying information about the feature portions of the reference image in a case where the acquired feature portions do not satisfy a predetermined condition, that is, in a case where the reliability of the acquired feature portions does not reach a predetermined level.

Techniques for image processing and transformation are provided. A plurality of images and a plurality of maps are received, and a system of neural networks is trained based on the plurality of images and the plurality of maps. A first image is received, and a first map is generated by processing the first image using the system of neural networks.

In an approach for image registration performance assurance by optimizing system configurations, a processor evaluates alignment of a registered image and a fixed image using a pre-trained learning model. The registered image is generated with a first registration method. A processor provides a reward score to the alignment, the reward score being defined as a higher score indicating a better alignment. A processor generates a registration status represented as a feature vector that contains information about how the registered and fixed images are aligned. A processor determines a second registration method based on the reward score, the feature vector, and the first registration method.

An apparatus accesses label data and training images corresponding to a geographic area; and provides the label data and training images to a training model. The training model comprises of at least a predictor model and an alignment model. The predictor model is configured to receive an image and provide a prediction corresponding to the image. The alignment model is configured to generate a transformed prediction based on aligning the label data and the prediction. The apparatus executes a loss engine to iteratively receive the label data and the transformed prediction, evaluate a loss function based on the label data and the transformed prediction, and cause weights of the predictor model and the alignment model to be updated based on the evaluated loss function to cause the predictor and alignment models to be trained.

The subject matter discloses a method of asset change detection using images, the method comprising steps executed by processing circuitry, the steps comprising: receiving at least one image of an asset captured by an image capturing device; receiving at least one attribute of a task of detecting a change in the asset using the received at least one image, at least one of the at least one attribute being one of the group consisting of: an attribute measured by a sensor, an attribute extracted from a website, an attribute retrieved from a database, an attribute input by a user, and an attribute encoded in computer code; selecting a reference image among a plurality of reference images of the asset according to at least one criterion based on the received at least one attribute of the task of detecting the change in the asset; computing an asset-difference pixel map, using the selected reference image and the image captured by the image capturing device; and detecting the change in the asset, using the computed asset-difference map.