From three-dimensional numerical data of surface irregularities of a tire inner surface in a tire of a construction where a sheet member is disposed on the tire circumference, a tire inner surface image is formed for which height direction data of the surface irregularities of the tire inner surface are expressed as pixel gradation values, and the tire circumferential direction and the tire width direction on the tire inner surface correspond to either of the longitudinal direction and the lateral direction of the image. A surface irregularity enhanced image is generated by subjecting this tire inner surface image to spatial frequency processing using a low-pass filter for the tire width direction and a band-pass filter for the tire circumferential direction. A determination is made of whether a recessed region exists which indicates a bonding defect of the end portions of the sheet member.

An image processing apparatus configured to correct at least part of a face image has an image acquisition unit that acquires an input image, a region extraction unit that extracts a pupil region, which is a region corresponding to an eye of a person, from the input image, a pixel extraction unit that extracts a pixel from the pupil region whose brightness value is higher than a threshold value, a determination unit that determines, based on the result of extraction performed by the pixel extraction unit, whether or not halation has occurred in the pupil region, and a correction unit that performs, based on the result of determination performed by the determination unit, processing for correcting halation on the pupil region. The pixel extraction unit sets the threshold value based on a bias of a brightness distribution of the pixels included in the pupil region.

A method upsamples an image using a non-linear fully connected neural network to produce only global details of an upsampled image and interpolates the image to produce a smooth upsampled image. The method concatenates the global details and the smooth upsampled image into a tensor and applies a sequence of nonlinear convolutions to the tensor using a convolutional neural network to produce the upsampled image.

The present invention provides an image processing circuit and associated image processing method. In the image processing circuit, a characteristic value calculation circuit is designed to calculate the plurality of characteristic values of consecutive-three-pixels with increasing/decreasing brightness, the plurality of left-side characteristic values of consecutive-three-pixels with increasing/decreasing brightness and the plurality of right-side characteristic values of consecutive-three-pixels with increasing/decreasing brightness, for the brightness adjustments. The adjusted brightness values of the present invention have sharper edges to improve the image quality.

There is provided an image processing device, an image projection device, a control device, an information processing device, an image projection system, an image processing method, and a program that make it possible to suppress a reduction in a subjective image quality. On the basis of each frame image of a moving image, a time during which a projection image of each frame image of the moving image is viewable by a user is controlled. For example, a controller controls the time during which the projection image is viewable by the user, to cause a difference between the moving image and the projection image due to a reduction in a luminance and a difference between the moving image and the projection image, which increases due to an increase in the time during which the projection image is viewable by the user, to be suppressed.

The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize context-aware sensors and multi-dimensional gesture inputs across a digital image to generate enhanced digital images. In particular, the disclosed systems can provide a dynamic sensor over a digital image within a digital enhancement user interface (e.g., a user interface without visual elements for modifying parameter values). In response to selection of a sensor location, the disclosed systems can determine one or more digital image features at the sensor location. Based on these features, the disclosed systems can select and map parameters to movement directions. Moreover, the disclosed systems can identify a user input gesture comprising movements in one or more directions across the digital image. Based on the movements and the one or more features at the sensor location, the disclosed systems can modify parameter values and generate an enhanced digital image.

A method of enhancing the visibility of blood vessels in a colour image captured by an image capturing device of a medical device, the colour image having a plurality of colour channels and a plurality of pixels, the method including for at least some of said plurality of pixels the steps of: (a) processing data obtained from a first colour channel together with data obtained from a second colour channel to determine a value of a first parameter indicative of the intensity in the red spectrum relative to the total intensity of said pixel; and (b) using said value of said first parameter to alter said pixel, wherein said first parameter has at least three possible values, and wherein the strength of the alteration is dependent on the value of said first parameter.

The present disclosure discloses a method and apparatus for generating an image, a device, a storage medium and a program product, relates to the field of artificial intelligence, and particularly to computer vision and deep learning technologies, and may be applied in smart cloud and power grid inspection scenarios. A particular implementation of the method comprises: acquiring an original insulator image; performing an image transformation on the original insulator image to obtain a composite insulator image; and inputting the original insulator image and the composite insulator image into a pre-trained generative adversarial network to generate a target insulator image. According to the implementation, the image transformation is performed on the original insulator image, and then, massive target insulator images are generated through the generative adversarial network.

A medical data processing apparatus includes a memory and processing circuitry. The memory stores a learned model including an input layer to which first MR data and second MR data having the same imaging target as the first MR data and an imaging parameter different from the first MR data are inputted, an output layer from which third MR data is output with a missing portion of the first MR data restored, and at least one intermediate layer arranged between the input layer and the output layer. The processing circuitry generates third MR data relating to the subject, from the first MR data serving as a process target and relating to the subject and the second MR data relating to the subject and acquired by an imaging parameter different from the first MR data serving as the process target, in accordance with the learned model.

A medical image processing device includes: a memory; and a processor including hardware. The processor is configured to: generate, by performing enlargement processing or shrinking processing to first observation image information input from an outside, second observation image information having number of pixels different from predetermined number of pixels, the first observation image information being generated by capturing a subject and having the predetermined number of pixels; generate third observation image information by performing enhancement processing for enhancing a structure of the subject to the second observation image information, the structure of the subject being contained in a second observation image corresponding to the second observation image information; and generate and output fourth observation image information having different number of pixels from that of the second observation image information by performing enlargement processing or shrinking processing to the third observation image information.