The present disclosure relates to systems, methods, and non-transitory computer readable media for generating deterministic enhanced digital images based on parallel determinations of pixel group offsets arranged in pixel waves. For example, the disclosed systems can utilize a parallel wave analysis to propagate through pixel groups in a pixel wave of a target region within a digital image to determine matching patch offsets for the pixel groups. The disclosed systems can further utilize the matching patch offsets to generate a deterministic enhanced digital image by filling or replacing pixels of the target region with matching pixels indicated by the matching patch offsets.

Various approaches related to capturing content of erasable boards are discussed herein.

In implementations of transferring image style to content of a digital image, an image editing system includes an encoder that extracts features from a content image and features from a style image. A whitening and color transform generates coarse features from the content and style features extracted by the encoder for one pass of encoding and decoding. Hence, the processing delay and memory requirements are low. A feature transfer module iteratively transfers style features to the coarse feature map and generates a fine feature map. The image editing system fuses the fine features with the coarse features, and a decoder generates an output image with content of the content image in a style of the style image from the fused features. Accordingly, the image editing system efficiently transfers an image style to image content in real-time, without undesirable artifacts in the output image.

Providing high-quality images of members of a group moving within a coverage area that includes non-group members is provided. A current location of each member of the group is tracked within the coverage area. Images are obtained of the coverage area. Based on the tracked current location of each member of the group within the coverage area, at least one of the non-group members are selectively redacted from the images of the coverage area such that the members of the group are clearly shown in redacted images and privacy rights of the at least one of the non-group members are protected. The selectively redacted images are provided to at least one of the members of the group.

Certain embodiments involve using an image completion neural network to perform user-guided image completion. For example, an image editing application accesses an input image having a completion region to be replaced with new image content. The image editing application also receives a guidance input that is applied to a portion of a completion region. The image editing application provides the input image and the guidance input to an image completion neural network that is trained to perform image-completion operations using guidance input. The image editing application produces a modified image by replacing the completion region of the input image with the new image content generated with the image completion network. The image editing application outputs the modified image having the new image content.

The present disclosure relates to a method and a system for computed tomography imaging. The method may comprise obtaining original data; obtaining a preprocessing result by preprocessing the original data; obtaining intensity of the artifact based on the preprocessing result; and updating a damaged channel or the air correction table based on the intensity of the artifact. Updating the air correction table may comprise: obtaining a first air correction table corresponding to a first temperature of detector; obtaining real-time temperature of detector; and obtaining a second air correction table corresponding to the real-time temperature based on the real-time temperature and the first air correction table.

An image processing apparatus includes an acquisition unit which acquires a parallax image generated based on a signal of a photoelectric converter among a plurality of photoelectric converters which receive light beams passing through partial pupil regions of an imaging optical system different from each other, and acquires a captured image generated by combining signals of the plurality of photoelectric converters, and an image processing unit which performs correction process so as to reduce a defect included in the parallax image based on the captured image.

Techniques of inpainting video content include training a neural network to perform an inpainting operation on a video using only content from that video. For example, upon receiving video content including a sequence of initial frames, a computer generates a sequence of inputs corresponding to at least some of the sequence of initial frames and each input including, for example, a uniform noise map. The computer then generates a convolutional neural network (CNN) using the sequence of input as the initial layer. The parameters of the CNN are adjusted according to a cost function, which has components including a flow generation loss component and a consistency loss component. The CNN then outputs, on a final layer, estimated image values in a sequence of final frames.

A system and method are described for enhancing readability of document images by operating on each document individually. Monochromatic light sources operating at different wavelengths of light can be used to obtain greyscale images. The greyscale images can then be used in any desired image enhancement algorithm. In one example algorithm, an automated method removes image background noise and improves sharpness of the scripts and characters using edge detection and local color contrast computation.

A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions executed by the processor to specifically configure the processor to implement a statistical model tool for providing insight into decision making. The statistical model tool applies the statistical model to an input image to generate an original classification probability. An image modification component executing within the statistical model tool iterative modifies each portion of the input image to generate a modified image. The statistical model tool applies the statistical model to the modified image to generate a new classification probability for each portion of the input image. A compare component executing in the statistical model tool compares each new classification probability to the original classification probability to generate a respective probability distance. A distance map generator executing within the statistical model tool generates a distance map data structure based on the probability distances. The distance map data structure represents an impact each portion of the input image has on determining classification probability by the statistical model.