Distortion generated in an image is effectively corrected in imaging using an EPI sequence such as DWI without extending an imaging time. After one excitation RF pulse of EPI is applied, a navigator scan in which the polarity of the phase encoding is opposite to that of the main scan is performed continuously to the main scan, and the distortion of the image by using the navigator scan data obtained by the navigator scan is corrected. In a case of multi-shot, phase information obtained from the navigator scan data for each shot is used to perform phase correction and multi-shot reconstruction on the main scan data of each shot.

Images of a hand may be used to identify users. Quality, detail, and so forth of these images may vary. An image is processed to determine a first spatial mask. A first neural network comprising many layers uses the first spatial mask at a first layer and a second spatial mask at a second layer to process images and produce an embedding vector representative of features in the image. The first spatial mask provides information about particular portions of the input image, and is determined by processing the image with an algorithm such as an orientation certainty level (OCL) algorithm. The second spatial mask is determined using unsupervised training and represents weights of particular portions of the input image as represented at the second layer. The use of the masks allows the first neural network to learn to use or disregard particular portions of the image, improving overall accuracy.

The present disclosure relates to a method of recovering an image from which a motion blur is removed and an image recovering apparatus and the image recovering apparatus according to an exemplary embodiment of the present disclosure includes a signal generator which receives at least one of illuminance information and speed information from a sensor to determine a random flickering pattern and a triggering signal based on the received signal, output the determined random flickering pattern to a lighting unit, and output the triggering signal to a camera; and an image processor which receives an image including a motion blur from the camera to recover the received image based on the random flickering pattern determined by the signal generator.

A method for mitigating motion blur in a visual-inertial tracking system is described. In one aspect, the method includes accessing a first image generated by an optical sensor of the visual tracking system, accessing a second image generated by the optical sensor of the visual tracking system, the second image following the first image, determining a first motion blur level of the first image, determining a second motion blur level of the second image, identifying a scale change between the first image and the second image, determining a first optimal scale level for the first image based on the first motion blur level and the scale change, and determining a second optimal scale level for the second image based on the second motion blur level and the scale change.

A method for mitigating motion blur in a visual tracking system is described. In one aspect, a method for selective motion blur mitigation in a visual tracking system includes accessing a first image generated by an optical sensor of the visual tracking system, identifying camera operating parameters of the optical sensor during the optical sensor generating the first image, determining a motion of the optical sensor during the optical sensor generating the first image, determining a motion blur level of the first image based on the camera operating parameters of the optical sensor and the motion of the optical sensor, and determining whether to downscale the first image using a pyramid computation algorithm based on the motion blur level.

An ophthalmological image processing apparatus acquires a plurality of images of a subject eye photographed in a scanning-type imaging optical system, sets any one of the plurality of images as a template, sets corresponding points or corresponding regions between an image of the subject eye and the template at a plurality of positions of each of the image of the subject eye and the template, calculates a movement amount of each of the corresponding points or each of the corresponding regions, and corrects a distortion of the image of the subject eye with respect to the template based on the movement amount of each of the corresponding points or each of the corresponding regions.

Techniques are provided to identify, correct, and/or replace anomalous pixels. In one example, a method includes receiving an image frame comprising a plurality of pixels arranged in a plurality of rows and columns. The pixels comprise image data associated with a scene and fixed pattern noise introduced by an imaging device. The method also includes performing a first process on a first set of the pixels to determine associated correction terms configured to reduce the fixed pattern noise, and applying the correction terms to the first set of the pixels in response to the first process. The method also includes performing a second process on a second set of the pixels to determine whether to replace the second set of the pixels to reduce the fixed pattern noise, and replacing at least a subset of the second set of the pixels in response to the second process. Additional methods and systems are also provided.

A method and apparatus for capturing digital video includes displaying a preview of a field of view of the imaging device in a user interface of the imaging device. A sequence of images is captured. A main subject and a background in the sequence of images is determined, wherein the main subject is different than the background. A sequence of modified images for use in a final video is obtained, wherein each modified image is obtained by combining two or more images of the sequence of images such that the main subject in the modified image is blur free and the background is blurred. The sequence of modified images is combined to obtain the final video, which is stored in a memory of the imaging device, and displayed in the user interface.

A method includes obtaining, using at least one image sensor of an electronic device, a first image frame and multiple second image frames of a scene. Each of the second image frames has an exposure time different from an exposure time of the first image frame. The method also includes encoding, using at least one processor, each of the first image frame and the second image frames using a convolutional neural network to generate a corresponding feature map. The method further includes aligning, using the at least one processor, encoded features of the feature map corresponding to the first image frame with encoded features of the feature maps corresponding to the second image frames.

An apparatus includes a subject detection unit configured to detect a specific subject in an input image, an acquisition unit configured to acquire camera information, an estimation unit configured to estimate a target of interest in an image using the subject information and the camera information, a first motion detection unit configured to detect background motion and subject motion, a conversion unit configured to convert the detected background motion and the detected subject motion to a first blur correction amount for correcting background blur and a second blur correction amount for correcting subject blur, respectively, and a correction amount calculation unit configured to, based on the target of interest, combine the first blur correction amount and the second blur correction amount and generate a final blur correction amount.