A system and method. The system may include a display, a lens having distortion, an image generator, and a processor. The lens may be configured to focus light received from an environment. The image generator may be configured to receive the light from the lens and output a stream of images as image data, wherein each of the stream of images is distorted. The processor may be configured to: receive the image data from the image generator; detect a point source object in the stream of images of the image data; enhance the point source object in the stream of images of the image data; undistort the stream of images of the image data having an enhanced point source object; and output a stream of undistorted images as undistorted image data to the display.

A computer-readable, non-transitory recording medium contains therein an image processing program. The image processing program is for generating learning data of a character detection model that at least detects, to recognize a character in a document contained in an image, a position of the character in the image, and configured to cause a computer to generate a cropped image by cropping the image, and adopt the cropped image not containing an image representing a split character as the learning data, instead of adopting the cropped image containing the image representing the split character as the learning data.

The information processing apparatus according to the present disclosure synthesizes a handwriting image with a noise image to generate a synthesized image, generates a correct label indicative of handwriting pixels from the handwriting image, and applies the synthesized image and the correct label as learning data to generate a learning model.

The information processing apparatus according to the present disclosure synthesizes a handwriting image with a noise image to generate a synthesized image, generates a correct label indicative of handwriting pixels from the handwriting image, and applies the synthesized image and the correct label as learning data to generate a learning model.

A signal processing device includes a first frame buffer configured to store a first frame, a second frame buffer configured to store a second frame and a processor. The processor is coupled to the first frame buffer and the second frame buffer and is configured to perform a first image processing procedure according to the first frame and the second frame to obtain a super resolution difference value corresponding to each pixel of the first frame, perform a second image processing procedure according to the first frame and the second frame to obtain a noise reduction value corresponding to each pixel of the first frame, selectively add the super resolution difference value and the noise reduction value to the corresponding pixel of the first frame to generate an output frame and store the output frame in the second frame buffer as the second frame.

A method and system are provided for removing noise from document images using a neural network-based machine learning model. A dataset of original document images is used as an input source of images. Random noise is added to the original document images to generate noisy images, which are provided to a neural network-based denoising system that generates denoised images. Denoised images and original document images are evaluated by a neural network-based discriminator system, which generates a predictive output relating to authenticity of evaluated denoised images. Feedback is provided backpropagation updates to train both the denoising and discriminator systems. Training sequences are iteratively performed to provide the backpropagation updates, such that the denoising system is trained to generate denoised images that can pass as original document images while the discriminator system is trained to improve the accuracy in predicting the authenticity of the images presented.

A method and system are provided for removing noise from document images using a neural network-based machine learning model. A dataset of original document images is used as an input source of images. Random noise is added to the original document images to generate noisy images, which are provided to a neural network-based denoising system that generates denoised images. Denoised images and original document images are evaluated by a neural network-based discriminator system, which generates a predictive output relating to authenticity of evaluated denoised images. Feedback is provided backpropagation updates to train both the denoising and discriminator systems. Training sequences are iteratively performed to provide the backpropagation updates, such that the denoising system is trained to generate denoised images that can pass as original document images while the discriminator system is trained to improve the accuracy in predicting the authenticity of the images presented.

A display control integrated circuit (IC) applicable to performing real-time video content text detection and speech automatic generation in a display device may include a pre-processing circuit, a character recognition circuit and a post-processing circuit. The pre-processing circuit may input a video signal to obtain a real-time video content carried by the video signal, and perform preliminary text detection on the real-time video content to generate a series of segmented character images to indicate a subtitle. The character recognition circuit may perform character recognition on the series of segmented character images to generate a series of characters, respectively. The post-processing circuit may perform vocabulary correction on the series of characters to selectively replace any erroneous character with a correct character to generate one or more vocabularies, for performing speech automatic generation.

A display control integrated circuit (IC) applicable to performing real-time video content text detection and speech automatic generation in a display device may include a pre-processing circuit, a character recognition circuit and a post-processing circuit. The pre-processing circuit may input a video signal to obtain a real-time video content carried by the video signal, and perform preliminary text detection on the real-time video content to generate a series of segmented character images to indicate a subtitle. The character recognition circuit may perform character recognition on the series of segmented character images to generate a series of characters, respectively. The post-processing circuit may perform vocabulary correction on the series of characters to selectively replace any erroneous character with a correct character to generate one or more vocabularies, for performing speech automatic generation.

Described are techniques for image processing. For instance, a process can include obtaining a plurality of labeled input images and determining a threshold percentage associated with the plurality of labeled input images, indicative of a percentage of correctly labeled input images. The process can include determining a respective self-influence for each respective labeled input image included in the plurality of input images and generating a respective self-influence weight for each respective labeled input image, based on the respective self-influence and the threshold percentage associated with each respective labeled input image. The process can include determining one or more loss values using a loss function associated with training a machine learning network based on using the plurality of labeled input images as a training data set, wherein the loss function determines the one or more loss values based on weighting each respective labeled input image by its respective self-influence weight.