An image processing device comprises an input unit, a characteristic amount calculation unit, a characteristic amount vector calculation unit, and a characteristic identification unit. The input unit receives an image. The characteristic amount calculation unit calculates an image characteristic amount characterizing a texture of a local area of the image received by the input unit. The characteristic amount vector calculation unit calculates a first characteristic amount vector corresponding to the local area from the image characteristic amount. The characteristic identification unit identifies a characteristic of the local area on the basis of the first characteristic amount vector and a second characteristic amount vector calculated by the same method as the first characteristic amount vector and calculated from an image whose characteristic has been determined in advance.

The present invention relates to a method capable of real-time implementable local tone mapping of high dynamic range images. In one embodiment, the present invention relates to a method capable of accomplishing real-time local tone mapping of high dynamic range images so that they have clear details without, for example, halo artifacts when viewed on standard display devices. In another embodiment, the present invention relates to a method capable of accomplishing real-time local tone mapping of high dynamic range images so that they have clear details without, for example, halo artifacts when viewed on standard display devices where such a method utilizes, in part, a modified Reinhard operator.

In order to reduce errors in assessment of a structure caused by noise and remove the noise superimposed in an image without causing artificiality in the result image after the removal while retaining significant information, a similarity is calculated by comparing a reference image generated from a plurality of original images with each original image and set as an index of noise determination. The respective original images are smoothed and synthesized using the said index to acquire a final image after the noise removal.

A method of extracting text from a digital image is provided. The method of extracting text includes receiving a digital image at an image processor where the digital image includes a textual object and a graphical object. A mask is generated based on the digital image. The mask includes a pattern having a first pattern area associated with the textual object and a second pattern area associated with the graphical object. The mask is applied to the digital image creating a transformed digital image. The transformed digital image includes a portion of the digital image associated with the textual object. Character recognition is performed on the portion of the digital image associated with the textual object of the transformed digital image to create a recognized text output.

Provided are a method and an apparatus for adjusting image brightness. The method includes: acquiring an image to be processed, and acquiring a single-channel brightness image based on grayscales of each channel of the image to be processed; performing Gaussian filtering on the single-channel brightness image to acquire a Gaussian filtered image; adjusting grayscales of the Gaussian filtered image based on the grayscales of the Gaussian filtered image and a preset proportion; comparing the Gaussian filtered image after adjustment with the Gaussian filtered image before adjustment to acquire a grayscale change rate of each pixel through adjustment; processing the image to be processed based on the grayscale change rate of each pixel to acquire a processed image; and outputting the processed image.

A system suitable for reducing static or slow moving echoes from acoustic boundaries in a system such as pipe walls, blood vessels, tissue structures so that an image from flowing or time varying media such as water, oil, blood etc may be imaged more clearly, the system including an analog-to-digital converter for digitizing a received analog signal from a detector, means for digitally separating static or slow moving components of the digital signal, a digital-to-analog converter to provide an analog version of the separated static or slow moving components of the signal, and a subtractor to subtract the analog version of the static or the slow moving components of the signal from an analog signal received from a detector to produce an analog signal corresponding to the remaining flow components of the signal.

A composition information obtaining unit calculates a mammary gland/fat ratio and a first information obtaining unit obtains imaged contrast information representing a contrast of the radiation image. A second information obtaining unit sets target application condition of X-ray, and obtains target contrast information representing an intended contrast for the radiation image based on the intended application condition. A contrast correction amount determination unit determines a contrast correction amount based on the imaged contrast information and the target contrast information. An image processing unit performs image processing, including gradation processing based on the determined contrast correction amount, on the radiation image, and obtains a processed radiation image.

Techniques for removing artifacts, such as shadows, from document images are described. A shadow map is generated for a digital image by first determining local background colors using clusters of local pixel intensities. Then, a global reference background color is selected from all pixel intensities of the digital image. Next, a per-pixel scaling factor is determined that maps the local background colors to the global reference background color, which applies localized adjustment to the digital image to remove local shadow.

Identifying objects in images is a difficult problem, particularly in cases an original image is noisy or has areas narrow in color or grayscale gradient. A technique employing a convolutional network has been identified to identify objects in such images in an automated and rapid manner. One example embodiment trains a convolutional network including multiple layers of filters. The filters are trained by learning and are arranged in successive layers and produce images having at least a same resolution as an original image. The filters are trained as a function of the original image or a desired image labeling; the image labels of objects identified in the original image are reported and may be used for segmentation. The technique can be applied to images of neural circuitry or electron microscopy, for example. The same technique can also be applied to correction of photographs or videos.

A restoration filter generation device that generates a restoration filter to perform restoration processing on luminance system image data that is image data related to luminance, which is generated based on image data of respective colors of multiple colors obtained by an imaging device having an optical system, includes an information acquisition device acquiring transfer function information corresponding to point image distribution in the optical system, for each color of the multiple colors, and a restoration filter generation device generating the restoration filter based on the transfer function information acquired by the information acquisition device and generating the restoration filter that performs phase correction of the luminance system image data according to the transfer function information on a single color of the multiple colors.