A method for calibrating a photodetector array supplying a video stream includes: a determination step, wherein an offset table is determined for each current image of the video stream based on at least two corrections from among the following: a first correction from a comparison of the current image to a corresponding predetermined reference table; a second correction from a calculation of a column error of the current image; and a third correction from a high-pass temporal filtering of the video stream; and a calculation step, wherein a current value of an offset table, equal to a sum between a previous value of the offset table and a weighted sum of at least two corrections, is calculated, with each coefficient of the offset table being associated with a respective photodetector of the array.

A method for calibrating a photodetector array supplying a video stream includes: a determination step, wherein an offset table is determined for each current image of the video stream based on at least two corrections from among the following: a first correction from a comparison of the current image to a corresponding predetermined reference table; a second correction from a calculation of a column error of the current image; and a third correction from a high-pass temporal filtering of the video stream; and a calculation step, wherein a current value of an offset table, equal to a sum between a previous value of the offset table and a weighted sum of at least two corrections, is calculated, with each coefficient of the offset table being associated with a respective photodetector of the array.

The apparatus for an X-ray data generation according to an embodiment of the inventive concept includes a processor that receives 3D data to generate output data and a buffer, and the processor includes an extraction unit that extracts raw object data from the 3D data and projects the raw object data onto a 2D plane to generate first object data, an augmentation unit that performs data augmentation on the first object data to generate second object data, a composition unit that synthesizes the second object data and background data to generate composite data, and a post-processing unit that performs post-processing on the composite data to generate the output data, and the buffer stores a plurality of parameters related to generation of the first object data, the second object data, the composite data, and the output data.

The apparatus for an X-ray data generation according to an embodiment of the inventive concept includes a processor that receives 3D data to generate output data and a buffer, and the processor includes an extraction unit that extracts raw object data from the 3D data and projects the raw object data onto a 2D plane to generate first object data, an augmentation unit that performs data augmentation on the first object data to generate second object data, a composition unit that synthesizes the second object data and background data to generate composite data, and a post-processing unit that performs post-processing on the composite data to generate the output data, and the buffer stores a plurality of parameters related to generation of the first object data, the second object data, the composite data, and the output data.

A character segmentation method and apparatus, and a computer-readable storage medium. The character segmentation method comprises: converting a character area image into a grayscale image (step 101); converting the grayscale image into an edge binary image by using an edge detection algorithm (step 102); acquiring character box segmentation blocks from the edge binary image by using a projection method (step 103); and determining a target character area from the character box segmentation blocks by using a contour detection algorithm, and performing character segmentation on the character area image according to the target character area (step 104). Another character segmentation method comprises: converting a character area image into a grayscale image (step 701); performing clustering analysis on the grayscale image by using a fuzzy C-means clustering algorithm, and executing binarization processing on the grayscale image according to a clustering analysis result (step 702); acquiring character positioning blocks from a binary image by using a projection method (step 703); and performing character segmentation on the character area image according to position information of the character positioning blocks (step 704). By using the methods and apparatuses, character segmentation can be performed on a relatively low quality image.

A character segmentation method and apparatus, and a computer-readable storage medium. The character segmentation method comprises: converting a character area image into a grayscale image (step 101); converting the grayscale image into an edge binary image by using an edge detection algorithm (step 102); acquiring character box segmentation blocks from the edge binary image by using a projection method (step 103); and determining a target character area from the character box segmentation blocks by using a contour detection algorithm, and performing character segmentation on the character area image according to the target character area (step 104). Another character segmentation method comprises: converting a character area image into a grayscale image (step 701); performing clustering analysis on the grayscale image by using a fuzzy C-means clustering algorithm, and executing binarization processing on the grayscale image according to a clustering analysis result (step 702); acquiring character positioning blocks from a binary image by using a projection method (step 703); and performing character segmentation on the character area image according to position information of the character positioning blocks (step 704). By using the methods and apparatuses, character segmentation can be performed on a relatively low quality image.

An electronic device may include enhancement circuitry to enhance high resolution image data to improve perceived quality of an image corresponding to the high resolution image data. The enhancement circuitry may include tone detection circuitry to determine one or more tones within the image and apply changes to the high resolution image data based on the one or more tones. The enhancement circuitry may also include example-based improvement circuitry to compare the high resolution image data to low resolution image data and apply changes to the high resolution image data based on differences between sections of the high resolution image data and sections of the low resolution image data. The enhancement circuitry may also include channel processing circuitry to apply the first and second changes to one or more channels of the high resolution image data.

An electronic device may include enhancement circuitry to enhance high resolution image data to improve perceived quality of an image corresponding to the high resolution image data. The enhancement circuitry may include tone detection circuitry to determine one or more tones within the image and apply changes to the high resolution image data based on the one or more tones. The enhancement circuitry may also include example-based improvement circuitry to compare the high resolution image data to low resolution image data and apply changes to the high resolution image data based on differences between sections of the high resolution image data and sections of the low resolution image data. The enhancement circuitry may also include channel processing circuitry to apply the first and second changes to one or more channels of the high resolution image data.

A method is described. The method includes receiving oversampled infrared data provided from an infrared pixel array. The method also includes performing at least one of selective median filtering, spatial-temporal filtering, or resolution enhancement for the oversampled infrared data.

A method is described. The method includes receiving oversampled infrared data provided from an infrared pixel array. The method also includes performing at least one of selective median filtering, spatial-temporal filtering, or resolution enhancement for the oversampled infrared data.