Various methods and systems for improved anterior segment optical coherence tomography (OCT) imaging are described. One example method includes collecting a set of B-scans over a range of different transverse locations on the cornea, segmenting each B-scan to identify an anterior corneal layer and an outer edge of Bowman's layer, calculating thickness values for each B-scan by computing the distance from the anterior corneal layer to the outer edge of the Bowman's layer, combining the thickness values from the B-scans to create a polar epithelial thickness map, converting the polar epithelial thickness map to a Cartesian epithelial thickness map using a fitting method, and storing or displaying the Cartesian epithelial thickness map or information derived from the Cartesian epithelial thickness map.

A method of de-smearing an image includes capturing image data from an imaging sensor and collecting motion data indicative of motion of the sensor while capturing the image data. The motion data is collected at a higher frequency than an exposure frequency at which the image data is captured. The method includes modeling motion of the sensor based on the motion data, wherein motion is modeled at the higher frequency than the exposure frequency. The method also includes modeling optical blur for the image data, modeling noise for the image data, and forming a de-smeared image as a function of the modeled motion, the modeled blur, and the modeled noise, and the image data captured from the imaging sensor.

An image processing device includes a three-dimensional noise reduction (3D NR) circuit, an artificial intelligence noise reduction (AI NR) circuit, a weight determination circuit and an image blending circuit. The 3D NR circuit performs a 3D NR operation on input image data to generate first image data. The AI NR circuit performs an AI NR operation on the input image data to generate second image data. The weight determination circuit outputs a blending weight according to a motion index. The image blending circuit blends the first image data and the second image data according to the blending weight to generate output image data.

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 computation device, a sensing device and a processing method based on time-of-flight (ToF) ranging are provided. In the method, intensity information of at least two phases corresponding to at least one pixel is obtained. The intensity information is generated by sensing a modulation light with time delays using these phases. Whether to abandon the intensity information of the at least two phases corresponding to the pixel is determined according to the difference between the intensity information of the at least two phases. Accordingly, the influence caused by motion blur would be reduced on depth information estimation.

An image processing device comprises a specification unit configured to specify, among a plurality of frames constituting a moving image, a section in the moving image of frames corresponding to a scene whose shake amount is larger, based on the plurality of frames, and an output unit configured to output information representing the section.

A display device includes a display configured to output image data, and a controller configured to extract a contour noise removal region from the image data, acquire motion information of the image data, and remove contour noise based on a de-contour gain value corresponding to the motion information.

A system and method for detecting, quantifying, and/or measuring motion of an object and correcting for exposure includes providing a processor and at least one video sensor; determining video recording parameters of the at least one video sensor; recording, by the at least one video sensor, video of the object; extracting a data set from the video wherein the data set describes the motion of the object; calculating a frequency transform of the data set for at least one frequency; and performing exposure correction at the at least one frequency based on the recording parameters.

A signal processing device according to the present technology includes a feature quantity extraction unit including a neural network and trained to extract a feature quantity for a specific event with respect to an input signal from a sensor. and a correction unit that performs correction of the input signal on the basis of the feature quantity extracted by the feature quantity extraction unit.

Systems and methods are disclosed for image signal processing. For example, methods may include receiving a current image of a sequence of images from an image sensor; combining the current image with a recirculated image to obtain a noise reduced image, where the recirculated image is based on one or more previous images of the sequence of images from the image sensor; determining a noise map for the noise reduced image, where the noise map is determined based on estimates of noise levels for pixels in the current image, a noise map for the recirculated image, and a set of mixing weights; recirculating the noise map with the noise reduced image to combine the noise reduced image with a next image of the sequence of images from the image sensor; and storing, displaying, or transmitting an output image that is based on the noise reduced image.