An image processing apparatus includes a filtering processing unit that executes a filtering process to suppress background noise of input image data; a threshold setting unit that sets a binarization threshold for the input image data; a background noise determination threshold setting unit that determines that a tone value at which the number of pixels of background noise of the input image data reaches a peak is a background peak tone value and that sets, as a background noise determination threshold, a tone value having a density higher than a density of the background peak tone value and having a number of pixels within a specific range of percentages, of the number of pixels at the peak; a background noise reduction processing unit that executes a background noise reduction process on the input image data and a binarization processing unit that executes a binarization process on the input image data.

An image processing apparatus includes a filtering processing unit that executes a filtering process to suppress background noise of input image data; a threshold setting unit that sets a binarization threshold for the input image data; a background noise determination threshold setting unit that determines that a tone value at which the number of pixels of background noise of the input image data reaches a peak is a background peak tone value and that sets, as a background noise determination threshold, a tone value having a density higher than a density of the background peak tone value and having a number of pixels within a specific range of percentages, of the number of pixels at the peak; a background noise reduction processing unit that executes a background noise reduction process on the input image data and a binarization processing unit that executes a binarization process on the input image data.

An image forming apparatus includes a gradation correction unit configured to perform a correction process on first image data expressing first gradation values, and output second image data expressing second gradation values. The gradation correction unit is configured to determine the second gradation values based on the first gradation values of a plurality of colors of a plurality of pixels in an area including a process target pixel and having a size corresponding to a first color misregistration amount. The gradation correction unit is further configured to determine the second gradation values such that there is no pixel in which a sum of the second gradation values of the plurality of colors exceeds a first threshold value, even when color misregistration within the first color misregistration amount occurs.

An image forming apparatus includes a gradation correction unit configured to perform a correction process on first image data expressing first gradation values, and output second image data expressing second gradation values. The gradation correction unit is configured to determine the second gradation values based on the first gradation values of a plurality of colors of a plurality of pixels in an area including a process target pixel and having a size corresponding to a first color misregistration amount. The gradation correction unit is further configured to determine the second gradation values such that there is no pixel in which a sum of the second gradation values of the plurality of colors exceeds a first threshold value, even when color misregistration within the first color misregistration amount occurs.

An image processing device includes a neutral edge detection and enhancement component which, for pixels of an input image, computes an edge strength for the pixel as a function of differences between the input luminance value of the pixel and input luminance values of neighboring pixels, computes a neutral adjustment factor for the pixel as a function of the input luminance value of the pixel, and computes a chroma adjustment factor for the pixel as a function of the input chrominance values of the pixel. An adjusted luminance value for the pixel is computed as a function of the input luminance value, the edge strength, and the neutral adjustment factor. Adjusted chrominance values for the pixel may be computed as a function of the input chrominance value, the edge strength, and the chroma adjustment factor. The system allows detection and enhancement of neutral edges in monochrome and color images.

An image processing device includes a neutral edge detection and enhancement component which, for pixels of an input image, computes an edge strength for the pixel as a function of differences between the input luminance value of the pixel and input luminance values of neighboring pixels, computes a neutral adjustment factor for the pixel as a function of the input luminance value of the pixel, and computes a chroma adjustment factor for the pixel as a function of the input chrominance values of the pixel. An adjusted luminance value for the pixel is computed as a function of the input luminance value, the edge strength, and the neutral adjustment factor. Adjusted chrominance values for the pixel may be computed as a function of the input chrominance value, the edge strength, and the chroma adjustment factor. The system allows detection and enhancement of neutral edges in monochrome and color images.

A method for generating an image processing filter includes: adjusting; and extracting. The adjusting inputs first training image data into a neural network to generate output image data, calculates an evaluation value based on a loss function using the output image data and second training image data, and adjusts a convolution filter so as to reduce the evaluation value. The extracting extracts data from the adjusted convolution filter as data for the image processing filter. A first training image includes noise and reproduces a test pattern. A second training image includes reduced noise and reproduces the test pattern. The loss function includes a first term and a second term. The first term specifies a magnitude of a difference between the output image data and the second training image data. The second term grows smaller as symmetry of the convolution filter relative to a filter axis of symmetry increases.

A method for generating an image processing filter includes: adjusting; and extracting. The adjusting inputs first training image data into a neural network to generate output image data, calculates an evaluation value based on a loss function using the output image data and second training image data, and adjusts a convolution filter so as to reduce the evaluation value. The extracting extracts data from the adjusted convolution filter as data for the image processing filter. A first training image includes noise and reproduces a test pattern. A second training image includes reduced noise and reproduces the test pattern. The loss function includes a first term and a second term. The first term specifies a magnitude of a difference between the output image data and the second training image data. The second term grows smaller as symmetry of the convolution filter relative to a filter axis of symmetry increases.

In the case where both thickening processing and UCR processing are performed for an object, appropriate effects are obtained for both pieces of the processing. An image processing apparatus including an image processing unit configured to perform thickening processing for an object included in an input image and to perform saturation suppression processing for an edge portion of the object in the input image for which the thickening processing has been performed.

In the case where both thickening processing and UCR processing are performed for an object, appropriate effects are obtained for both pieces of the processing. An image processing apparatus including an image processing unit configured to perform thickening processing for an object included in an input image and to perform saturation suppression processing for an edge portion of the object in the input image for which the thickening processing has been performed.