A method for adjusting uniformity of image color tones includes setting brightness of a darkest display region as target brightness and setting color temperature coordinates of a designated display region as target color temperature coordinates of at least one part of display regions of a display, comparing the brightness and the color temperature coordinates of each display region of the at least one part of display regions with the target brightness and the target color temperature coordinates for generating a first calibrated color tone, generating a second calibrated color tone of the each display region of the at least one part of display regions according to an Alpha channel parameter and the first calibrated color tone, generating a uniformity compensated image layer according to all second calibrated color tones, and virtually overlaying the uniformity compensated image layer on the at least one part of display regions.

An image inspection device detects an abnormality of a formed image by comparing imaged data obtained by imaging the formed image with original image data of the formed image. The image inspection device includes a hardware processor that: adjusts the original image data to reduce sharpness of the original image data; and detects the abnormality by comparing the adjusted original image data with the obtained imaged data. The hardware processor partly restricts the reduction of the sharpness.

An image inspection device detects an abnormality of a formed image by comparing imaged data obtained by imaging the formed image with original image data of the formed image. The image inspection device includes a hardware processor that: adjusts the original image data to reduce sharpness of the original image data; and detects the abnormality by comparing the adjusted original image data with the obtained imaged data. The hardware processor partly restricts the reduction of the sharpness.

Techniques related to removing haze from video are discussed. Such techniques include color converting a video frame from an input color space to a haze color space using a haze color detected in a previous frame, estimating pixel-wise haze amounts, de-hazing the video frame in the haze color space using the pixel-wise haze amounts, and color converting the de-hazed frame to the input color space.

Techniques related to removing haze from video are discussed. Such techniques include color converting a video frame from an input color space to a haze color space using a haze color detected in a previous frame, estimating pixel-wise haze amounts, de-hazing the video frame in the haze color space using the pixel-wise haze amounts, and color converting the de-hazed frame to the input color space.

Embodiments relate to a pixel defect detection circuit for detecting and correcting defective pixels in captured image frames. The pixel defect detection circuit includes a defect pixel location table that maps pixel locations in an image frame to respective confidence values, each confidence value indicating a likelihood that a corresponding pixel is defective. The pixel defect detection circuit further includes a dynamic defect processing circuit configured to determine whether a first pixel of an image frame is defective, and a flatness detection circuit configured to determine whether the first pixel is in a flat region of the image frame. The confidence value corresponding to the location of the first pixel is updated based upon whether the first pixel is determined be defective if the first pixel is determined to be in a flat region, and not updated if the first pixel is determined to not be in a flat region.

An image forming apparatus performs a halftone process using plural dither matrices. Generated is calibration data to calculate a color registration error amount corresponding to a primary scanning directional position of an image forming available width. An image area dividing unit determines an image boundary position that is identical to any of matrix boundaries of plural dither matrices arranged in an image forming target area, and divides the image forming target area into plural image adjustment areas using the image boundary position. A correction processing unit corrects secondary scanning directional positions of the plural image adjustment areas, using respective correction amounts calculated with the calibration data. Further, the image area dividing unit determines the image boundary position using a remainder left by dividing in the primary scanning direction the number of pixels of the image adjustment area by the number of pixels of the dither matrix.

According to one embodiment, an image processing apparatus includes a scanner to generate image data by scanning a document and a control unit. The control is configured to set, for each pixel in the image data, a second pixel value based on a first pixel value and a difference between a first base value corresponding to a base color of the document and a second base value that is based on neighboring pixel values around the pixel. A correction table is used to obtain a corrected second pixel value for each pixel. A third pixel value for each pixel is set based on the corrected second pixel value and the difference between the first and second base values.

According to one embodiment, an image processing apparatus includes a scanner to generate image data by scanning a document and a control unit. The control is configured to set, for each pixel in the image data, a second pixel value based on a first pixel value and a difference between a first base value corresponding to a base color of the document and a second base value that is based on neighboring pixel values around the pixel. A correction table is used to obtain a corrected second pixel value for each pixel. A third pixel value for each pixel is set based on the corrected second pixel value and the difference between the first and second base values.

A method for correcting in-track position errors in a digital printing system having a linear printhead includes printing a test target including a plurality of alignment marks. A data processing system is used to automatically analyze a captured image of the printed test target to determine a measured in-track position for each of the alignment marks. The measured in-track positions for the alignment marks are compared to reference positions to determine measured in-track position errors. An in-track position correction function is determined responsive to the measured in-track position errors, wherein the in-track position correction function specifies in-track position corrections to be applied as a function of cross-track position. A corrected digital image is determined by resampling an input digital image responsive to the in-track position correction function.