Abstract: A system and method of removing turbulence from an image of a time ordered sequence of image frames. The method comprises removing effects of turbulence from a first image of the sequence to create an initial corrected image frame; determining a number of iterations required to achieve a desired turbulence removal for a subsequent image in the sequence and satisfy a latency constraint and an available memory capacity; and determining, based on the number of required iterations, a minimum set of image frames required to remove turbulence from the subsequent image. The minimum set of image frames comprises: a number of image frames of the sequence, a number of image frames generated in an intermediate iteration of turbulence removal and the initial corrected image frame. The method further comprises using the minimum set of image frames to remove turbulence from the subsequent image of the sequence.

Abstract: A method of training a generator G of a Generative Adversarial Network (GAN) includes generating a real contextual data set {x1, . . . , xN} for a high resolution image Y; generating a generated contextual data set {g1, . . . , gN} for a generated high resolution image G(Z); calculating a perceptual loss Lpcept value using the real contextual data set {x1, . . . , xN} and the generated contextual data set {g1, . . . , gN}; and training the generator G using the perceptual loss Lpcept value. The generated high resolution image G(Z) is generated by the generator G of the GAN in response to receiving an input Z, where the input Z is a random sample that corresponds to the high resolution image Y.

Abstract: An image tracking method of the present invention includes the following steps: (A) obtaining a plurality of original images by using an image capturing device; (B) transmitting the plurality of original images to a computing device, and generating a position box based on a preset image set; (C) obtaining an initial foreground image including a target object, and an identified foreground image is determined based on a pixel ratio and a first threshold; (D) obtaining a feature and obtaining a first feature score based on the feature of the identified foreground images; and (E) generating a target object matching result based on the first feature score and a second threshold, and recording a moving trajectory of the target object based on the target object matching result.

Abstract: An image processing apparatus includes a low-resolution image generating circuit configured to generate a low-resolution image including a second pixel corresponding to first pixels based on an input image including the first pixels, and an edge preserving smoothing circuit configured to generate a reliability of the second pixel based on characteristics of values of the first pixels and perform edge preserving smoothing on the input image using a value of the second pixel of which a reflection ratio is adjusted, based on the reliability of the second pixel.

Abstract: An information processing device includes: a processor configured to: obtain sensor data; determine partial data; input the sensor data to an input layer of a first neural network recognition model; input the partial data to an input layer of a second neural network recognition model; align second intermediate output data from a second intermediate layer of the second neural network recognition model, relative to first intermediate output data from a first intermediate layer of the first neural network recognition model; generate intermediate input data from the first intermediate output data and the second intermediate output data aligned; and input the intermediate input data to a third intermediate layer of the first neural network recognition model subsequent to the first intermediate layer.