An abnormality detection unit detects an abnormal object in target images repeatedly acquired. An abnormality type selection unit selects, for each of the target images, an abnormality type of the abnormal object from a plurality of specific abnormality types based on values of at least two basic feature amounts. A feature amount monitoring unit monitors the values of the basic feature amounts and a value of an auxiliary feature amount corresponding to the abnormality type currently selected by the abnormality type selection unit. An adjustment processing unit executes an adjustment process corresponding to the auxiliary feature amount being monitored by the feature amount monitoring unit. The abnormality type selection unit changes the abnormality type to be selected, in accordance with the change in the values of the basic feature amounts mentioned above.

A method for preparing digital image data from an analog image input by scanning, and reducing visibility of the scanning noise, may include estimating a visibility of scanning noise, and a number of scanning samples needed to reduce scanning noise to below a visible threshold. Related methods include scanning, by an analog-to-digital image scanner, an analog image for multiple iterations, resulting in digital image data for each of the iterations; calculating a noise statistic for individual pixels of digital image data across the iterations; determining true values of individual pixels of the digital image data based on the noise statistic for each of the individual pixels and generating scanner noise reduced digital image data wherein pixels are assigned their respective ones of the true values; and saving the scanner noise reduced digital image data in a computer memory.

An image processing apparatus includes an acquisition unit configured to acquire a reference image serving as a reference printing result and a target image serving as a printing result to be inspected, a correction unit configured to correct the target image based on a paper white area of each of the reference image and the target image so that a paper white color of the paper white area of the target image matches a paper white color of the paper white area of the reference image, wherein the paper white area is determined based on a positional displacement between the reference image and the target image, and an inspection unit configured to inspect the printing result by comparing the corrected target image with the reference image.

A computing system receives, from a client device, an image of a content item uploaded by a user of the client devices. The computing system divides the image into one or more overlapping patches. The computing system identifies, via a first machine learning model, one or more distortions present in the image based on the image and the one or more overlapping patches. The computing system determines that the image meets a threshold level of quality. Responsive to the determining, the computing system corrects, via a second machine learning model, the one or more distortions present in the image based on the image and the one or more overlapping patches. Each patch of the one or more overlapping patches are corrected. The computing system reconstructs the image of the content item based on the one or more corrected overlapping patches.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for implementing content-aware filters to autonomously remove scan marks from digital documents. In particular implementations, the disclosed systems utilize a set of targeted scan mark models in a scan mark removal pipeline. For example, each scan mark model includes a corresponding content-aware filter configured to identify document regions that match a designated class of scan marks to filter. Examples of scan mark models include staple scan mark models, punch hole scan mark models, and page turn scan mark models. In certain embodiments, the disclosed systems then use the scan mark models to generate mark-specific masks based on document input features. Additionally, in some embodiments, the disclosed systems combine the mark-specific masks into a final segmentation mask and apply the final segmentation mask to the digital document for correcting the identified regions with scan marks.

An inspection apparatus includes processing circuitry. The processing circuitry acquires read image data obtained by reading an image printed on both sides of a conveyance medium. The processing circuitry searches for marks printed on both sides of the conveyance medium in the read image data. The processing circuitry outputs information indicating a misalignment amount of both sides of the conveyance medium based on positions where the marks are printed.

Exemplary quantitative susceptibility mapping methods, systems and computer-accessible medium can be provided to generate images of tissue magnetism property from complex magnetic resonance imaging data using the Bayesian inference approach, which minimizes a cost function consisting of a data fidelity term and two regularization terms. The data fidelity term is constructed directly from the complex magnetic resonance imaging data. The first prior is constructed from matching structures or information content in known morphology. The second prior is constructed from a region having an approximately homogenous and known susceptibility value and a characteristic feature on anatomic images. The quantitative susceptibility map can be determined by minimizing the cost function. Thus, according to the exemplary embodiment, system, method and computer-accessible medium can be provided for determining magnetic susceptibility information associated with at least one structure.

An inspection apparatus performs dropout color processing on a first inspection area set for an image generated by reading a print product and then performs first recognition processing on the first inspection area, and further performs second recognition processing on a second inspection area set for the image and then performs an inspection of whether sufficient margin areas are allocated on the second inspection area, without performing dropout color processing.

An inspection apparatus obtains a scanned image by reading a printed product, calculates a weighting factor of a weighting filter based on a difference between a value of a pixel of interest in a reference image used in creation of the printed product and values of peripheral pixels of the pixel of interest, performs filter processing by using the weighting filter having the weighting factors, with respect to a pixel of interest in the scanned image, which corresponds to the pixel of interest in the reference image, thereby shifting the pixel of interest in the scanned image, and create an inspection target image by calculating the weighting factors and performing the filter processing with respect to the shifted pixel of interest, and inspects quality of the printed product by collating the inspection target image with the reference image.

Exemplary quantitative susceptibility mapping methods, systems and computer-accessible medium can be provided to generate images of tissue magnetism property from complex magnetic resonance imaging data using the Bayesian inference approach, which minimizes a cost function consisting of a data fidelity term and two regularization terms. The data fidelity term is constructed directly from the complex magnetic resonance imaging data. The first prior is constructed from matching structures or information content in known morphology. The second prior is constructed from a region having an approximately homogenous and known susceptibility value and a characteristic feature on anatomic images. The quantitative susceptibility map can be determined by minimizing the cost function. Thus, according to the exemplary embodiment, system, method and computer-accessible medium can be provided for determining magnetic susceptibility information associated with at least one structure.