A computer implemented method can decode a frame of video data comprising an array of pixels to obtain decoded luma values and decoded chroma values corresponding to the array of pixels, and extract a frame mask based on the decoded luma values. The frame mask can include an array of mask values respectively corresponding to the array of pixels. A mask value indicates whether a corresponding pixel is in foreground or background of the frame. The method can perform a morphological operation to the frame mask to change one or more mask values to indicate their corresponding pixels are removed from the foreground and added to the background of the frame. The method can also identify foreground pixels after performing the morphological operation to the frame mask, and render a foreground image for display based on the decoded luma values and decoded chroma values of the foreground pixels.

A computer implemented method can decode a frame of video data comprising an array of pixels to obtain decoded luma values and decoded chroma values corresponding to the array of pixels, and extract a frame mask based on the decoded luma values. The frame mask can include an array of mask values respectively corresponding to the array of pixels. A mask value indicates whether a corresponding pixel is in foreground or background of the frame. The method can perform a morphological operation to the frame mask to change one or more mask values to indicate their corresponding pixels are removed from the foreground and added to the background of the frame. The method can also identify foreground pixels after performing the morphological operation to the frame mask, and render a foreground image for display based on the decoded luma values and decoded chroma values of the foreground pixels.

A system for image inpainting is provided, including an encoder, a decoder, and a sketch tensor space of a third-order tensor; wherein the encoder includes an improved wireframe parser and a canny detector, and a pyramid structure sub-encoder; the improved wireframe parser is used to extract line maps from an original image input to the encoder, the canny detector is used to extract edge maps from the original image, and the pyramid structure sub-encoder is used to generate the sketch tensor space based on the original image, the line maps and the edge maps; and the decoder outputs an inpainted image from the sketch tensor space. A method thereof is also provided.

A system for image inpainting is provided, including an encoder, a decoder, and a sketch tensor space of a third-order tensor; wherein the encoder includes an improved wireframe parser and a canny detector, and a pyramid structure sub-encoder; the improved wireframe parser is used to extract line maps from an original image input to the encoder, the canny detector is used to extract edge maps from the original image, and the pyramid structure sub-encoder is used to generate the sketch tensor space based on the original image, the line maps and the edge maps; and the decoder outputs an inpainted image from the sketch tensor space. A method thereof is also provided.

Methods, systems, and apparatus, including computer programs encoded on a storage device, for electric grid asset detection are enclosed. An electric grid asset detection method includes: obtaining overhead imagery of a geographic region that includes electric grid wires; identifying the electric grid wires within the overhead imagery; and generating a polyline graph of the identified electric grid wires. The method includes replacing curves in polylines within the polyline graph with a series of fixed lines and endpoints; identifying, based on characteristics of the fixed lines and endpoints, a location of a utility pole that supports the electric grid wires; detecting an electric grid asset from street level imagery at the location of the utility pole; and generating a representation of the electric grid asset for use in a model of the electric grid.

Methods, systems, and apparatus, including computer programs encoded on a storage device, for electric grid asset detection are enclosed. An electric grid asset detection method includes: obtaining overhead imagery of a geographic region that includes electric grid wires; identifying the electric grid wires within the overhead imagery; and generating a polyline graph of the identified electric grid wires. The method includes replacing curves in polylines within the polyline graph with a series of fixed lines and endpoints; identifying, based on characteristics of the fixed lines and endpoints, a location of a utility pole that supports the electric grid wires; detecting an electric grid asset from street level imagery at the location of the utility pole; and generating a representation of the electric grid asset for use in a model of the electric grid.

A method includes: computing noise data by subtracting, by a processing circuit, a noisy image from a corresponding ground truth image; clustering, by the processing circuit, a plurality of noise values of the noise data based on intensity values of the corresponding ground truth image; permuting, by the processing circuit, a plurality of locations of the noise values of the noise data within each cluster; generating, by the processing circuit, a synthetic noise image based on the permuted locations of the noise values; adding, by the processing circuit, the synthetic noise image to the corresponding ground truth image to generate a synthetic noisy image; and augmenting an image dataset for training a neural network to perform image denoising with the synthetic noisy image.

A method includes: computing noise data by subtracting, by a processing circuit, a noisy image from a corresponding ground truth image; clustering, by the processing circuit, a plurality of noise values of the noise data based on intensity values of the corresponding ground truth image; permuting, by the processing circuit, a plurality of locations of the noise values of the noise data within each cluster; generating, by the processing circuit, a synthetic noise image based on the permuted locations of the noise values; adding, by the processing circuit, the synthetic noise image to the corresponding ground truth image to generate a synthetic noisy image; and augmenting an image dataset for training a neural network to perform image denoising with the synthetic noisy image.

An image processing method according to the invention is for dilating an object in a binary image represented by run-length code. The image processing method includes obtaining a target dilation amount representing how much the object is to be dilated, determining a first dilation amount decreasing as a density of runs in the image increases, comparing the target dilation amount and the first dilation amount, and performing an dilation processing based on the comparison result. If the target dilation amount is larger than the first dilation amount, a first dilation processing by the first dilation amount and a second dilation processing after the first dilation processing by a second dilation amount are performed. The second dilation amount is a difference between the target dilation amount and the first dilation amount. Otherwise, the dilation processing by the target dilation amount is directly performed.

A method for detecting a display panel defect, including: collecting a panel image of a to-be-detected display panel, a plurality of first pixels of the display panel corresponding to a plurality of second pixels in the panel image; converting the panel image into a binary image; dilating each bright spot region in the binary image such that adjacent bright spot regions communicate with each other to form at least one closed communication region in the binary image; determining a region of interest mask image in the binary image in accordance with the at least one closed communication region; determining a region of interest in accordance with the region of interest mask image and the panel image; and performing feature identification on the region of interest to determine a defect of the display panel.