According to the invention, the saturation thresholds (th.sub.r; th.sub.g; th.sub.b) used for this detection depend on color coordinates (r.sub.w, g.sub.w, b.sub.w) representing an illuminant (ILL) of the image.

An information processing apparatus includes one or more processors that function as the following units: a detection unit configured to detect a defect occurring in a structure by using local information about the structure based on a first image of the structure; a first determination unit configured to determine a global state of the structure based on a result of detection of the defect by the detection unit and global information indicating information about a wider area than an area of the structure indicated by the local information, the global information being based on a second image having a lower resolution than that of the first image; and a second determination unit configured to determine a degree of damage to a predetermined area of the structure based on a result of determination made by the first determination unit of the global state of the structure.

A method for automatic selection of display settings for a medical image is provided. The method includes receiving a medical image, mapping the medical image to an appearance classification cell of an appearance classification matrix using a trained deep neural network, selecting a first WW and a first WC for the medical image based on the appearance classification and a target appearance classification, adjusting the first WW and the first WC based on user preferences to produce a second WW and a second WC, and displaying the medical image with the second WW and the second WC via a display device.

An adaptive image shading correction method and an adaptive image shading system are provided. The method includes: configuring an image capturing device to obtain a current frame; and configuring a processing unit to: divide the current frame into blocks; select block pairs from the blocks, in which each of the block pairs includes an inner block and an outer block; perform a filtering process for each of the block pairs to determine whether a brightness condition, a saturation condition, a hue similarity condition, and a sharpness similarity condition are met; in response to obtaining filtered block pairs, calculate a sum similarity threshold based on hue statistical data, a saturation difference, and a brightness difference; and use filtered blocks with individual thresholds less than the sum similarity threshold to calculate a shadow compensation value to adjust the current frame.

In various embodiments, a computer-implemented method of training a neural network for relighting an image is described. A first training set that includes source images and a target illumination embedding is generated, the source images having respective illuminated subjects. A second training set that includes augmented images and the target illumination embedding is generated, where the augmented images corresponding to the source images. A first autoencoder is trained using the first training set to generate a first output set that includes estimated source illumination embeddings and first reconstructed images that correspond to the source images, the reconstructed images having respective subjects that are i) from the corresponding source image, and ii) illuminated based on the target illumination embedding. A second autoencoder is trained using the second training set to generate a second output set that includes estimated augmented illumination embeddings and second reconstructed images that correspond to the augmented images.

An image processing method including obtaining image data. The image data includes a plurality of image data values. The image processing method also includes processing the image data, thereby generating output data. Processing the image data includes applying a convolution operation to the plurality of image data values using a kernel including a plurality of coefficients. Applying the convolution operation includes obtaining a sum of image data values of the plurality of image data values that correspond respectively to coefficients of the plurality of coefficients that each have a common coefficient value. Applying the convolution operation also includes multiplying the sum by the common coefficient value.

An imaging system includes a processor, a memory, a visible light camera configured to record a first image of a scene, and an infrared camera configured to record a second image of the scene. The processor configured to execute instructions stored in the memory to input the first image and the second image into a neural network. The neural network relights the first image, based on characteristics of the second image, to correspond to an image of the scene under canonical illumination conditions.

The present disclosure discloses an image data processing system, and discloses an image data processing system for a vehicle, which compensates image data by reflecting an ambient illumination.

An image processing apparatus and method that can enhance visibility of medicines are provided. An image obtained by imaging medicines is acquired, the medicines are detected from the acquired image, and images of the detected medicines are cropped to generate individual medicine images. It is determined whether visibility of each generated individual medicine image is good or poor, and an individual medicine image with poor visibility is subjected to processing to enhance visibility and displayed on a display device.

A system and method. The system may include a display, a lens having distortion, an image generator, and a processor. The lens may be configured to focus light received from an environment. The image generator may be configured to receive the light from the lens and output a stream of images as image data, wherein each of the stream of images is distorted. The processor may be configured to: receive the image data from the image generator; detect a point source object in the stream of images of the image data; enhance the point source object in the stream of images of the image data; undistort the stream of images of the image data having an enhanced point source object; and output a stream of undistorted images as undistorted image data to the display.