Implementations relate to detecting/replacing transient obstructions from high-elevation digital images. A digital image of a geographic area includes pixels that align spatially with respective geographic units of the geographic area. Analysis of the digital image may uncover obscured pixel(s) that align spatially with geographic unit(s) of the geographic area that are obscured by transient obstruction(s). Domain fingerprint(s) of the obscured geographic unit(s) may be determined across pixels of a corpus of digital images that align spatially with the one or more obscured geographic units. Unobscured pixel(s) of the same/different digital image may be identified that align spatially with unobscured geographic unit(s) of the geographic area. The unobscured geographic unit(s) also may have domain fingerprint(s) that match the domain fingerprint(s) of the obscured geographic unit(s). Replacement pixel data may be calculated based on the unobscured pixels and used to generate a transient-obstruction-free version of the digital image.

Certain aspects of the technology disclosed herein involve combining images to generate a wide view image of a surrounding environment. Images can be recorded using an stand-alone imaging device having wide angle lenses and/or normal lenses. Images from the imaging device can be combined using methods described herein. In an embodiment, a pixel correspondence between a first image and a second image can be determined, based on a corresponding overlap area associated with the first image and the second image. Corresponding pixels in the corresponding overlap area associated with the first image and the second image can be merged based on a weight assigned to each of the corresponding pixels.

An image processing system for verifying that embedded digital content satisfies a predetermined criterion associated with display of the content, the image processing system a content embedding engine that embeds content in a resource provided by a content provider and that configures the resource for rendering, a rendering engine that renders the content embedded in the resource; an application interface engine that interfaces with the rendering engine and that generates a visualization of the resource and of the embedded content rendered in the resource; and an image processing engine that processes one or more pixels of the generated visualization of the resource and of the embedded content and the resource to verify that the specified visual element satisfies the predetermined criterion; and transmits verification data comprising an indication of whether the predetermined criterion is satisfied.

Images may be captured by a moving image capture device. A reference image and a background image may be selected from the images. The reference image may include depiction of an object, with the object blocking view of the background. The background image may include depiction of the background blocked by the object in the reference image. An object layer may be generated by segmenting the depiction of the object from the reference image. A background layer may be generated by combining the depiction of the background in the background image with the reference image. The background layer may be blurred and combined with the object layer to generate a panning image.

Various examples of a system for peanut sorting and grading are disclosed herein. The system for grading peanut maturity, can include: a sample feeder configured to supply individual peanuts to an imaging area; a sorting board comprising a plurality of chutes and a plurality of gates, each chute of the plurality of chutes designated for a grade of peanut; and program instructions to obtain the digital image of the individual peanut; determine the grade of the individual peanut; and sort the individual peanut based on the grade of the individual peanut. A method for grading peanut maturity, can include feeding an individual peanut to an imaging area; obtaining a digital image of the individual peanut; determining a grade of the individual peanut based on an average color; and sorting the individual peanut in a chute of a sorting board based on the grade of the individual peanut.

An apparatus and method for successive multi-frame image denoising are herein disclosed. The apparatus includes a first subtractor including a first input to receive a frame of the image, a second input to receive a reference frame, and an output; an absolute value function block including an input connected to the output of the first subtractor and an output; a second subtractor including a first input connected to the output of the absolute value function block, a second input for receiving a first predetermined value, and an output; and a maximum value divider function block including an input connected to the output of the second subtractor and an output for outputting filter weights.

A sample observation device includes: an emission optical system that emits planar light to a sample on an XZ plane; a scanning unit that scans the sample in a Y-axis direction so as to pass through an emission surface of the planar light; an imaging optical system that has an observation axis inclined with respect to the emission surface and forms an image of observation light generated in the sample; an image acquisition unit that acquires a plurality of pieces of XZ image data corresponding to an optical image of the observation light; and an image generation unit 8 that generates XY image data based on the plurality of pieces of XZ image data. The image generation unit extracts an analysis region of the plurality of pieces of XZ image data acquired in the Y-axis direction, integrates brightness values of at least the analysis region in a Z-axis direction to generate X image data, and combines the X image data in the Y-axis direction to generate the XY image data.

A method for spatially-adaptive tone mapping in an image having high dynamic range includes using a computing device to receive an input image from an image sensor comprising a plurality of pixels having pixel locations and determine within the input image a plurality of local size scales, each comprising a neighborhood having substantially constant illumination. The variation in reflectance within each neighborhood is estimated and local contrast within each neighborhood is enhanced. Using the illumination and variation within the contrast-enhanced neighborhoods, the image is remapped to a reduced dynamic range to generate an output image.

Aspects of the present disclosure involve a system and a method for performing operations comprising: accessing a source image depicting a target structure; accessing one or more target images depicting at least a portion of the target structure; computing correspondence between a first set of pixels in the source image of a first portion of the target structure and a second set of pixels in the one or more target images of the first portion of the target structure, the correspondence being computed as a function of camera parameters that vary between the source image and the one or more target images; and generating a three-dimensional (3D) model of the target structure based on the correspondence between the first set of pixels in the source image and the second set of pixels in the one or more target images based on a joint optimization of target structure and camera parameters.

A computer-implemented method for image processing is provided. The method includes obtaining data acquired by a medical imaging system. The method also includes normalizing the data. The method further includes de-noising the normalized data utilizing a deep learning-based denoising network. The method even further includes de-normalizing the de-noised data. The method yet further includes generating blended data based on both the data and the de-normalized de-noised data.