This system is designed to color correct a targeted color or colors in a video stream comprising: a computer device having a computer readable medium; and, a set of computer readable instructions embodied on the computer readable medium that are configured to: receive a dataset that includes one or more pairs of video frames where a first frame of the pair of an incorrect color frame and the second frame of the pair is a color correct frame, using a machine learning module to create a mask according to the dataset, receiving an image, correcting the image, and, transmitting the corrected image to a display.

An image processing apparatus including a determining unit configured to determine, based on at least one of a first conversion characteristic or a second conversion characteristic, a conversion characteristic of each of a plurality of regions in a first image having a first dynamic range, and a conversion unit configured to convert an image of each of the plurality of regions into a second image having a second dynamic range less than the first dynamic range by using the conversion characteristic determined by the determining unit. In relation to input luminance and output luminance, the first conversion characteristic has a higher characteristic of maintaining tone than the second conversion characteristic, and has a lower characteristic of maintaining contrast than the second conversion characteristic.

Flare compensation includes obtaining a dark corner intensity differences profile between a first and a second image based on a relative illumination of an area outside a first image circle of the first image and a second image circle of the second image. The dark corner intensity differences profile is obtained for a luminance (Y) component. A flare profile is obtained using an intensity differences profile and the dark corner intensity differences profile. The intensity differences profile is obtained for the Y component along a stitch line between the first image and the second image. The flare profile of the Y component is converted to an RGB flare profile. The first image is modified based on the RGB flare profile to obtain a processed first image.

Smart metrology methods and apparatuses disclosed herein process images for automatic metrology of desired features. An example method at least includes extracting a region of interest from an image, the region including one or more boundaries between different sections, enhancing at least the extracted region of interest based on one or more filters, generating a multi-scale data set of the region of interest based on the enhanced region of interest, initializing a model of the region of interest; optimizing a plurality of active contours within the enhanced region of interest based on the model of the region of interest and further based on the multi-scale data set, the optimized plurality of active contours identifying the one or more boundaries within the region of interest, and performing metrology on the region of interest based on the identified boundaries.

An information processing system acquires, using a reading device, a read image from an original on which a handwritten character is written; acquires, based on the read image, a partial image that is a partial region of the read image and a binarized image that expresses the partial image by two tones; performs learning of a learning model based on learning data that uses the partial image as a correct answer image and the binarized image as an input image; acquires print data including a font character; generates conversion image data including a gradation character obtained by inputting the font character to the learning model; and causes an image forming device to form an image based on the generated conversion image data.

A collation device includes a processor configured to, by executing a program: (a) acquire a photographed image including a collation area provided on a printing substrate having unevenness, (b) simultaneously execute smoothing processing and shading difference enhancement processing on the photographed image, and (c) detect the collation area based on an image obtained by simultaneously executing the smoothing processing and the shading difference enhancement processing.

Embodiments disclose systems and methods that aid in screening, diagnosis and/or monitoring of medical conditions. The systems and methods may allow, for example, for automated identification and localization of lesions and other anatomical structures from medical data obtained from medical imaging devices, computation of image-based biomarkers including quantification of dynamics of lesions, and/or integration with telemedicine services, programs, or software.

An information processing apparatus includes an identification unit configured to identify a partial region of an input image, and a processing unit configured to perform image processing for reducing degradation of the input image on the input image by inference using a neural network. The processing unit is configured to change the image processing between the partial region and another region.

An image processing method is performed by a processor. The image processing method includes following operations: generating an average brightness value of each of a plurality windows in image data; generating a pixel ratio value according to the image data; generating a weight value of the each of the windows according to the pixel ratio value, a first brightness weight, and a second brightness weight; generating an adjusted brightness value according to the average brightness values of the windows and the weight values of the windows; and performing an auto exposure process according to the adjusted brightness value to generate final image data. The final image data is for a display panel to display.

Methods and apparatuses are disclosed for modifying images of skin so as to reduce or enhance the appearance of component pigments, such as melanin and hemoglobin. A diffuse reflectance image of skin, such as a cross-polarized contact dermoscopy image, which conveys information regarding subsurface features of the skin, is processed so as to extract pigment distribution information, which is then used to correct the diffuse reflectance image, such as by reducing the appearance of melanin to allow better visualization of hemoglobin-related structures, such as vasculature. Alternatively, the diffuse reflectance image can be corrected so as to reduce the appearance of hemoglobin to allow better visualization of melanin-related structures.