Abstract: Removing an embedded barcode in an image. A barcode-embedding area in an image is acquired. Pixels in the barcode-embedding area can be changed from a RGB color space to a color space with a luminance component. The luminance values of the pixels can be determined on a dark side or a light side. The luminance values of the pixels on the dark side or the light side can be mapped to luminance values falling into a whole range of a luminance interval. The pixels in the barcode-embedding area can be changed from the color space with the luminance component to the RGB color space.

Abstract: Removing an embedded barcode in an image. A barcode-embedding area in an image is acquired. Pixels in the barcode-embedding area can be changed from a RGB color space to a color space with a luminance component. The luminance values of the pixels can be determined on a dark side or a light side. The luminance values of the pixels on the dark side or the light side can be mapped to luminance values falling into a whole range of a luminance interval. The pixels in the barcode-embedding area can be changed from the color space with the luminance component to the RGB color space.

Abstract: A method, system, and computer product for driving a display system in a screen protection mode includes receiving one or more image data, generating a first image signal and a second image signal based on the one or more image data, responsive to receiving an input indicating a screen protection mode, displaying a first image on a display screen of the display system based on the first image signal, displaying a second image on the display screen based on the second image signal, and passing through the first image by a viewing device and blocking the second image by the viewing device, so that a viewer wearing the viewing device indentifies the first image. Left-eye and right-eye lenses of the viewing device are configured to operate in a same manner.

Abstract: A method for enhancing lower-quality visual data using hierarchical algorithms, the method comprising the steps of: receiving one or more sections of lower-quality visual data; applying a hierarchical algorithm to the one or more sections of lower-quality visual data to enhance the one or more sections of lower-quality visual data to one or more sections of higher-quality visual data, wherein the hierarchical algorithm was developed using a learned approach; and outputting the one or more sections of higher-quality visual data.

Abstract: A method for enhancing at least a section of lower-quality visual data using a hierarchical algorithm, the method comprises receiving at least one section of lower-quality visual data; and extracting a subset of features, from the at least one section of lower-quality visual data. A plurality of layers of reduced-dimension visual data from the extracted features are formed and enhanced to form at least one section of higher-quality visual data. The at least one section of higher-quality visual data corresponds to the at least one section of lower-quality visual data received.

Abstract: A method for training learned hierarchical algorithms, the method comprising the steps of receiving input data and generating metrics from the input data. At least one hierarchical algorithm is then selected from a plurality of predetermined hierarchical algorithms based on comparing the generated metrics from the input data and like metrics for each of the plurality of predetermined hierarchical algorithms. The selected hierarchical algorithm is developed based on the input data and the developed hierarchical algorithm is outputted.

Abstract: A method for enhancing at least a section of lower-quality visual data using a hierarchical algorithm, the method including receiving at least a plurality of neighbouring sections of lower-quality visual data. A plurality of input sections from the received plurality of neighbouring sections of lower quality visual data are selected and features are extracted from those plurality of input sections of lower-quality visual data. A target section based on the extracted features from the plurality of input sections of lower-quality visual data is then enhanced.

Abstract: A method for enhancing at least a section of lower-quality visual data, the method comprising at least a section of the lower-quality visual data being received. A hierarchical algorithm is then selected from a plurality of hierarchical algorithms, wherein the step of selection is based on a predetermined metric and wherein the hierarchical algorithms were developed using a learned approach and at least one of the hierarchical algorithms is operable to increase the quality of the lower-quality visual data. The selected hierarchical algorithm is then used to increase the quality of the lower-quality visual data to create a higher-quality visual data.

Abstract: A method for training an algorithm to process at least a section of received visual data using a training dataset and reference dataset. The method comprises an iterative method with iterations comprising: generating a set of training data using the algorithm; comparing one or more characteristics of the training data to one or more characteristics of at least a section of the reference dataset; and modifying one or more parameters of the algorithm to optimise processed visual data based on the comparison between the characteristic of the training data and the characteristic of the reference dataset. The algorithm may output the processed visual data with the same content as the at least a section of received visual data. Some aspects and/or implementations provide for improved super-resolution of lower quality images to produce super-resolution images with improved characteristics (e.g. less blur, less undesired smoothing) compared to other super-resolution techniques.

Abstract: A neural network is trained to process received visual data to estimate a high-resolution version of the visual data using a training dataset and reference dataset. A set of training data is generated and a generator convolutional neural network parameterized by first weights and biases is trained by comparing characteristics of the training data to characteristics of the reference dataset. The first network is trained to generate super-resolved image data from low-resolution image data and the training includes modifying first weights and biases to optimize processed visual data based on the comparison between the characteristics of the training data and the characteristics of the reference dataset.

Abstract: A method for increasing the quality of a section of visual data communicated over a network from a first node to a second node, the method at the second node including receiving a lower-quality visual data via a network, receiving a corresponding reference to an algorithm operable to increase a quality of the lower-quality visual data, the algorithm selected based on a higher-quality visual data from which the lower-quality visual data was generated, and using the algorithm to increase the quality of the lower-quality visual data to recreate the higher-quality visual data.

Abstract: A method for developing an enhancement model for low-quality visual data, the method comprising the steps of receiving one or more sections of higher-quality visual data; and training a hierarchical algorithm. The hierarchical algorithm is operable to increase the quality of one or more sections of lower-quality visual data so as to substantially reproduce the one or more sections of higher-quality visual data. The hierarchical algorithm is then outputted.

Abstract: A method for enhancing at least a section of lower-quality visual data using a hierarchical algorithm, the method comprising receiving at least a plurality of neighbouring sections of lower-quality visual data. A plurality of input sections from the received plurality of neighbouring sections of lower quality visual data are selected and features are extracted from those plurality of input sections of lower-quality visual data. A target section based on the extracted features from the plurality of input sections of lower-quality visual data is then enhanced.

Abstract: A method for enhancing visual data when communicating visual data over a network from a first node to a second node. The method at the first node comprises developing at least one modified hierarchical algorithm from a known hierarchical algorithm operable to substantially recreate at least one section of higher-quality visual data. References to one or more known hierarchical algorithms from which the modified hierarchical algorithms were developed are transmitted to the second node along with one or more modifications to the one or more known hierarchical algorithms operable to reproduce the one or more modified hierarchical algorithms from the known hierarchical algorithms. The second node is able to recreate substantially the higher-quality video using the modified hierarchical algorithm.

Abstract: A method for enhancing one or more sections of lower-quality visual data using a hierarchical algorithm, the method comprising receiving one or more sections of lower-quality visual data. The one or more sections of lower-quality visual data are enhanced to one or more sections of higher-quality visual data using the hierarchical algorithm. Additionally, at least the first step of the hierarchical algorithm is performed in a lower-quality domain; and wherein the hierarchical algorithm operates in both a higher-quality domain and the lower-quality domain.

Abstract: Embodiments of the present invention provide for hiding information in an image. A first pixel point and a second pixel point that are adjacent in an image are extracted. A first sub-pixel of the first pixel point and a second sub-pixel of the second pixel point that are to be combined during display on a display device are determined, wherein the display device determines a combined pixel value according to pixel values of the first sub-pixel and the second sub-pixel in a predefined manner. Information is hidden using parity properties of a sum of pixel values of the first sub-pixel and the second sub-pixel while the combined pixel value determined according to pixel values of the first sub-pixel and the second sub-pixel in the predefined manner is kept unchanged. By using unique display characteristics of a display device, information is hidden in an image without changing display effect of the image on the display device.

Abstract: A computing device configured for installing an operating system is described. The computing device includes a storage device, a processor and instructions stored in memory. The computing device begins writing an operating system image to a storage device. One or more current storage device driver files are downloaded. The computing device injects one or more current storage device drivers obtained from the one or more current storage device driver files without first booting an operating system obtained from the operating system image.

Abstract: Disclosed are a method and apparatus for embedding or detecting watermarks in a text, which belong to the field of document protection. The method and apparatus overlay an additional layer of shade in a document for recording a large amount of information from watermarks. The shade comprises the dots arranged under certain rules. Shifts of the dots record each bit string within the watermark. According to the method and apparatus, a large amount of information can be embedded and the watermarks embedded in the shade are dispersed together with the document which can be digitized by a scanner for being detected.

Abstract: Disclosed are a method and apparatus for embedding or detecting watermarks in a text, which belong to the field of document protection. The method and apparatus overlay an additional layer of shade in a document for recording a large amount of information from watermarks. The shade comprises the dots arranged under certain rules. Shifts of the dots record each bit string within the watermark. According to the method and apparatus, a large amount of information can be embedded and the watermarks embedded in the shade are dispersed together with the document which can be digitized by a scanner for being detected.