An image adjustment device includes: an illumination component derivation unit that derives an illumination component of a grayscale image; a reflectance component derivation unit that derives a reflectance component image that is a resulting image in which the illumination component is removed from the grayscale image; a contrast component derivation unit that derives a contrast component based on a contrast value between a pixel of the reflectance component image and a peripheral area of the pixel; a histogram derivation unit that derives a luminance histogram of the grayscale image weighted according to the contrast value for each pixel of the contrast component; a conversion function derivation unit that derives a luminance conversion function for converting a luminance such that a luminance histogram of a converted grayscale image in which the grayscale image is converted by the luminance conversion function and a predetermined histogram are matched with or similar to each other; and a luminance conversion unit that generates the converted grayscale image.

Disclosed are a signal processing device and an image display apparatus including the same. The signal processing device and the image display apparatus including the same according to an embodiment of the present disclosure includes: a first decoder to reconstruct image data received from an external electronic device, an encoder to compress the image data reconstructed in the first decoder, a memory to store the image data compressed in the encoder, and a second decoder to reconstruct the image data stored in the memory. Accordingly, despite of the increases of the amount of the input image data and the bandwidth thereof, the image data may be stored in the memory efficiently.

A generation unit that generates an output image by correcting a first image based on deterioration of the first image due to a substance generated during surgery estimated based on the first image that is an image regarding surgery and a second image that is an image prior to the first image is included.

Tunnel defect detecting method and system using unmanned aerial vehicle (UAV) are provided, and the UAV is equipped with a light-emitting diode (LED) module, a camera, a laser radar, an ultrasonic distance meter and an inertial measurement unit (IMU). The method includes: collecting images in a tunnel based on the LED module and the camera to obtain a training image set; training by using the training image set to obtain a defect detecting model, collecting real-time tunnel images, detecting suspected defects to the real-time tunnel images by the defect detecting model, obtaining pose information of the UAV based on the camera, the laser radar, the ultrasonic distance meter and the IMU to control the UAV to hover. The method can realize accurate pose estimation and defect detection in the tunnel with no GPS signals and highly symmetrical inside.

Examples are provided that relate to processing depth camera data over a distributed computing system, where phase unwrapping is performed prior to denoising. One example provides a time-of-flight camera comprising a time-of-flight depth image sensor, a logic machine, a communication subsystem, and a storage machine holding instructions executable by the logic machine to process time-of-flight image data acquired by the time-of-flight depth image sensor by, prior to denoising, performing phase unwrapping pixel-wise on the time-of-flight image data to obtain coarse depth image data comprising depth values; and send the coarse depth image data and the active brightness image data to a remote computing system via the communication subsystem for denoising.

A method includes: obtaining a plurality of view images; identifying a representative value from among difference values between values of a plurality of sub-pixels corresponding to a first position in the plurality of view images and an intermediate value of a bit range of a display; determining filtering strength corresponding to the representative value, based on a correspondence map indicating a correspondence relationship between filtering strength and a difference value between a value of a sub-pixel and the intermediate value; and applying a filter having the determined filtering strength to the plurality of sub-pixels corresponding to the first position, wherein a value resulting from applying the filter having the determined filtering strength to the plurality of sub-pixels corresponding to the first position is included in a range of sub-pixel values according to the bit range of the display.

A storage medium stores correction data for obtaining a correction amount for correcting image data, obtained from an image formed by a lens apparatus, with respect to a distribution of a light amount in the image, wherein the correction data includes a coefficient of an n-th order polynomial (where n is a non-negative integer) with respect to an image height h, the coefficient corresponding to a state of the lens apparatus. The coefficient satisfies a first inequality
−0.15≤dD′(h)−dDlens(h)≤1.98, where dDlens(h) represents a change amount of the light amount at the image height h per an increase amount dh of the image height h, and dD′(h) represents a change amount of an inverse of a value of the n-th order polynomial at the image height h per the increase amount dh.

An image processing apparatus including a division unit configured to divide first image data having a first dynamic range into a plurality of regions, an obtaining unit configured to obtain distance information indicating a distance from a focal plane in each of the plurality of regions, a determining unit configured to determine a conversion characteristic of each of the plurality of regions based on the distance information, a conversion unit configured to convert each of the plurality of regions into second image data having a second dynamic range smaller than the first dynamic range by using the conversion characteristic determined by the determining unit, and a storage unit configured to store a first conversion characteristic and a second conversion characteristic that can be used for the conversion.

DCE MR images are obtained from a MR scanner and under a free-breathing protocol is provided. A neural network assigns a perfusion metric to DCE MR images. The neural network includes an input layer configured to receive at least one DCE MR image representative of a first contrast enhancement state and of a first respiratory motion state and at least one further DCE MR image representative of a second contrast enhancement state and of a second respiratory motion state. The neural network further includes an output layer configured to output at least one perfusion metric based on the at least one DCE MR image and the at least one further DCE MR image. The neural network with interconnections between the input layer and the output layer is trained by a plurality of datasets, each of the datasets having an instance of the at least one DCE MR image and of the at least one further DCE MR image for the input layer and the at least one perfusion metric for the output layer.

The present disclosure relates generally to medical imaging, and more specifically to enhancing medical images (e.g., images taken in low-light conditions) using machine-learning techniques. An exemplary method of obtaining an enhanced fluorescence medical image of a subject comprises: receiving a fluorescence medical image of the subject (e.g., NIR images); providing the fluorescence medical image to a generator of a trained generative adversarial network (GAN) model trained using a plurality of white light images; and obtaining, from the generator, the enhanced fluorescence medical image of the subject.