Abstract: A super-resolution image reconstruction method includes acquiring a plurality of raw images, positioning the plurality of raw images, acquiring a plurality of motion vectors, setting image coordinates of a super-resolution image, adjusting image coordinates of the super-resolution image according to the plurality of motion vectors for generating a plurality of updated image coordinates, setting a first image range, adjusting coordinates of a first image range according to the plurality of motion vectors for generating a plurality of updated first image ranges, generating a plurality of second image ranges according to each updated first image range, and generating a reconstructed pixel of the super-resolution image at the image coordinates according to a plurality of weightings, the plurality of updated first image ranges, and the plurality of second image ranges.

Abstract: An auto white balance adjusting method includes determining a local white pixel area and a global white pixel area, selecting a plurality of pixels of an image according to the local white pixel area for generating a local average color value in a first color space, selecting a plurality of pixels of the image according to the global white pixel area for generating a global average color value in the first color space, converting the local average color value into three primary color gains in a second color space, generating three primary color target gains according to the three primary color gains and a color temperature curve in the second color space, and adjusting a white balance of the image frame by frame to meet the three primary color target gains according to the local average color value and the three primary color gains.

Abstract: Image dewarping includes capturing a source image from a camera, selecting an input row of pixels from the source image, if the input row of pixels comprises a plurality of input pixels in a region of interest in the source image, storing the plurality of input pixels to a memory to generate an input segment of pixels, when a plurality of pixels required to generate an output row of pixels are all stored in the memory, reading from the memory the plurality of pixels corresponding to the output row of pixels, and performing coordinate transformation on the plurality of pixels to generate the output row of pixels, and when coordinate transformation has been completed on the plurality of pixels, releasing from the memory an input segment of pixels of the plurality of input segments of pixels that does not correspond to other output rows of pixels.

Abstract: An auto white balance adjusting method includes determining a white pixel area according to a standard of a first color space, selecting a plurality of pixels of an image according to the white pixel area, generating an average color value of the plurality of pixels in the first color space, converting the average color value in the first color space to three primary color gains in a second color space, generating three primary color target gains according to the three primary color gains and a color temperature curve, and gradually adjusting a white balance of the image to meet the three primary color target gains according to the average color value in the first color space and the three primary color gains in the second color space. The first color space and the second color space are different.

Abstract: An image color brightness compensation method includes generating image data of a plurality of pixels by using a Bayer color filter of an image sensing unit, generating demosaic image data after the image data of the plurality of pixels is processed by a demosaic process, executing a linear combination process of the demosaic image data by using at least one matrix for generating output image data, processing the output image data by using a blending unit for generating blended image data, generating a brightness compensation gain of the blended image data, and compensating image brightness of the blending image data for generating brightness compensated image data according to the brightness compensation gain.

Abstract: A method of image conversion includes selecting a set of pixels from a source image, storing the set of pixels in a memory, determining whether the set of pixels includes at least one pixel requiring coordinate conversion by querying a predetermined mapping table, if the set of pixels includes the at least one pixel requiring coordinate conversion, converting the coordinates of the at least one pixel, and after converting the coordinates of the at least one pixel, removing the set of pixels from the memory.

Abstract: An auto white balance adjusting method includes determining a white pixel area according to a standard of a first color space, selecting a plurality of pixels of an image according to the white pixel area, generating an average color value of the plurality of pixels in the first color space, converting the average color value in the first color space to three primary color gains in a second color space, generating three primary color target gains according to the three primary color gains and a color temperature curve, and gradually adjusting a white balance of the image to meet the three primary color target gains according to the average color value in the first color space and the three primary color gains in the second color space. The first color space and the second color space are different.

Abstract: An image color brightness compensation method includes generating image data of a plurality of pixels by using a Bayer color filter of an image sensing unit, generating demosaic image data after the image data of the plurality of pixels is processedby a demosaic process, executing a linear combination process of the demosaic image data by using at least one matrix for generating output image data, processing the output image data by using a blending unit for generating blended image data, generating a brightness compensation gain of the blended image data, and compensating image brightness of the blending image data for generating brightness compensated image data according to the brightness compensation gain.

Abstract: A method of image conversion includes selecting a set of pixels from a source image, storing the set of pixels in a memory, determining whether the set of pixels includes at least one pixel requiring coordinate conversion by querying a predetermined mapping table, if the set of pixels includes the at least one pixel requiring coordinate conversion, converting the coordinates of the at least one pixel, and after converting the coordinates of the at least one pixel, removing the set of pixels from the memory.

Abstract: A barcode detection method includes obtaining a gradient of each pixel in an image, generating a gradient phase and a gradient magnitude of each pixel according to the gradient, and binarizing the gradient magnitude of each pixel to generate a binary image, generating a sliding window on the image, sampling the binary image vertically and horizontally within the sliding window to generate the numbers of grayscale value variations in the vertical and horizontal directions, locating the most intensive flip region according to the grayscale variations in the vertical and horizontal directions, locating a core barcode region according to the most intensive flip region, capturing the gradient phase of the pixels in the core barcode region to generate a gradient phase distribution, generating a barcode format detection result according to the gradient phase distribution, and locating the barcode region according to the barcode format detection result.

Abstract: A barcode detection method includes obtaining a gradient of each pixel in an image, generating a gradient phase and a gradient magnitude of each pixel according to the gradient, and binarizing the gradient magnitude of each pixel to generate a binary image, generating a sliding window on the image, sampling the binary image vertically and horizontally within the sliding window to generate the numbers of grayscale value variations in the vertical and horizontal directions, locating the most intensive flip region according to the grayscale variations in the vertical and horizontal directions, locating a core barcode region according to the most intensive flip region, capturing the gradient phase of the pixels in the core barcode region to generate a gradient phase distribution, generating a barcode format detection result according to the gradient phase distribution, and locating the barcode region according to the barcode format detection result.

Abstract: A barcode detection method includes acquiring an image by a camera, acquiring a horizontal gradient and a vertical gradient of each pixel of the image within a region, generating a gradient phase and gradient magnitude of each pixel according to the horizontal gradient and the vertical gradient, performing a binarization process to the gradient magnitude of each pixel of the image within the region for generating a binarized image, vertically and horizontally sampling the binarized image for generating the gray level flip count in a vertical direction and a horizontal direction, locating an image region of a barcode according to the gray level flip count in the vertical direction and the horizontal direction, acquiring a plurality of gradient phases of all pixels within the image region for generating a gradient phase distribution, and generating a barcode format detection result according to the gradient phase distribution.

Abstract: A barcode detection method includes acquiring an image by a camera, acquiring a horizontal gradient and a vertical gradient of each pixel of the image within a region, generating a gradient phase and gradient magnitude of each pixel according to the horizontal gradient and the vertical gradient, performing a binarization process to the gradient magnitude of each pixel of the image within the region for generating a binarized image, vertically and horizontally sampling the binarized image for generating the gray level flip count in a vertical direction and a horizontal direction, locating an image region of a barcode according to the gray level flip count in the vertical direction and the horizontal direction, acquiring a plurality of gradient phases of all pixels within the image region for generating a gradient phase distribution, and generating a barcode format detection result according to the gradient phase distribution.

Abstract: A panoramic image stitching method includes acquiring a plurality of first images, converting camera image plane coordinates of each first image of the plurality of first images into virtual image plane coordinates for generating a second image according a world coordinate system, identifying a plurality of feature points on the second image, calibrating coordinates of the plurality of feature points on the second image, generating a calibrated second image according to calibrated coordinates of the plurality of feature points on the second image, and stitching a plurality of calibrated second images for generating a panoramic image.

Abstract: An image perspective conversion method includes acquiring a first image, partitioning a predetermined region on the first image into a plurality of polygonal sub-regions, acquiring a plurality of first coordinates corresponding to the plurality of polygonal sub-regions, converting the plurality of first coordinates into a plurality of second coordinates according to a world coordinate system, and interpolating a plurality of pixels among the plurality of second coordinates for generating a second image.

Abstract: An image perspective conversion method includes acquiring a first image, partitioning a predetermined region on the first image into a plurality of polygonal sub-regions, acquiring a plurality of first coordinates corresponding to the plurality of polygonal sub-regions, converting the plurality of first coordinates into a plurality of second coordinates according to a world coordinate system, and interpolating a plurality of pixels among the plurality of second coordinates for generating a second image.

Abstract: A panoramic image stitching method includes acquiring a plurality of first images, converting camera image plane coordinates of each first image of the plurality of first images into virtual image plane coordinates for generating a second image according a world coordinate system, identifying a plurality of feature points on the second image, calibrating coordinates of the plurality of feature points on the second image, generating a calibrated second image according to calibrated coordinates of the plurality of feature points on the second image, and stitching a plurality of calibrated second images for generating a panoramic image.

Abstract: A polyesterification method comprises steps of mixing, by combining N,N-dialkylformamide dialkyl acetal with a tin catalyst, so as to transfer an alkoxy group from the N,N-dialkylformamide dialkyl acetal to the tin catalyst to obtain a Sn coordination complex; and polymerization, by conducting a ring-opening polymerization of a ester under the catalysis of the Sn coordination complex, and finally to obtain a polyester compound; wherein, the chemical formula of the N,N-dialkylformamide dialkyl acetal is (RO)2CHNR2, with the R being an alkyl group.

Abstract: A polyesterification method comprises steps of mixing, by combining N,N-dialkylformamide dialkyl acetal with a tin catalyst, so as to transfer an alkoxy group from the N,N-dialkylformamide dialkyl acetal to the tin catalyst to obtain a Sn coordination complex; and polymerization, by conducting a ring-opening polymerization of a ester under the catalysis of the Sn coordination complex, and finally to obtain a polyester compound; wherein, the chemical formula of the N,N-dialkylformamide dialkyl acetal is (RO)2CHNR2, with the R being an alkyl group.