An image quality enhancing apparatus, an image display apparatus, an image quality enhancing method, and a computer readable storage medium which make a learning-type image quality enhancing method utilizing a sparse expression practical are provided. The image quality enhancing apparatus calculates, from the feature quantity of an image, coefficients of low-image-quality base vectors expressing a feature quantity with a linear sum and generates the image with the enhanced image quality by calculating a linear sum of high-image-quality base vectors using the calculated coefficient. When calculating the coefficient, T base vectors highly influential on the feature quantity are selected from among a plurality of base vectors and an analytic solution making L2 norm of a coefficient matrix α as small as possible is calculated. A sparse solution of the coefficients can be obtained without using the iteration method and a practical image quality enhancing apparatus can be realized.

An information processing device according to the present invention includes: a proper identifier output unit which outputs proper identifiers for identifying learning images; a feature vector calculation unit which calculates feature vectors of at least a part of patches included in registered patches that are registered in a dictionary for compositing a restored image; and a search similarity calculation unit which calculates a similarity calculation method that classifies the proper identifiers to be given to the registered patches based on the feature vectors.

The invention discloses a method for correcting aero-optical thermal radiation noise, comprising steps of: pretreating a degraded image to obtain a multi-scale degraded image group, conducting iteration process of obtaining an optimal solution by using a last scale estimation result as an original value of next scale estimation according to the multi-scale degraded image group, thereby facilitating original-scale bias field estimation, and restoring the degraded image according to the original-scale bias field estimated value thereby obtaining an image after aero-optical thermal radiation noise correction. The invention also discloses a system for correcting aero-optical thermal radiation noise. The invention is capable of solving problems with conventional methods, comprising poor correction effect, high complexity, and incapability in correcting the thermal radiation noise at an image level, and applicable to restoration of an image with aero-optical thermal radiation noise.

The present disclosure according to at least one embodiment relates to, in the learning process of a child using smart toys, a method and system for providing an interactive service by using a smart toy, which provide more accurate classified emotional state of the child based on at least one or more sensed data items of an optical image, a thermal image, and voice data of the child, as well as adaptively provide a flexible and versatile interactive service according to classified emotions.

A method may include obtaining a first set of projection data with respect to a first dose level; reconstructing, based on the first set of projection data, a first image; determining a second set of projection data based on the first set of projection data, the second set of projection data relating to a second dose level that is lower than the first dose level; reconstructing a second image based on the second set of projection data; and training a first neural network model based on the first image and the second image. In some embodiments, the trained first neural network model may be configured to convert a third image to a fourth image, the fourth image exhibiting a lower noise level and corresponding to a higher dose level than the third image.

A method generates a high-resolution (HR) image from a low-resolution (LR) image using regression functions. During a training stage, training HR images are downsampled to LR images. A signature is determined for each LR-HR patch pair based on a local ternary pattern (LTP). The signature is a low dimensional descriptor used as an abstraction of the patch pair features. Then, patch pairs with the same signature are clustered, and a regression function which maps the LR patches to the HR patches is determined. In some cases patch pairs of similar signatures can be combined for learning and a single regression function determined, thus decreasing the number of required regression functions. During actual upscaling, LR patches of an input image are similarly processed to obtain the signatures and from the regression functions. The LR patches can then be upscaled using the training regression functions.

Super-resolution virtual-reality (VR) head-mounted displays, and methods of operating the same are disclosed herein. An example disclosed method includes emitting light from a pixel at a first location in a display assembly in a VR head-mounted display, and emitting light from the pixel at a second different location in the display assembly in the VR head-mounted display

Certain embodiments pertain to Fourier ptychographic retinal imaging methods and systems that focus on a retina of an eye to acquire a sequence of raw retinal images, construct a full-resolution, complex retinal image from the sequence of raw retinal image and correct the aberration in the full-resolution, complex retinal image to generate a substantially aberration-free retinal image.

An imaging apparatus includes first and second imaging devices configured to capture first and second images of a scene, respectively. The first and second images include multiple first image blocks relative to a first coordinate system and multiple second image blocks relative to a second coordinate system, respectively. The apparatus further includes a processor configured to calibrate one or more first image blocks and one or more corresponding second image blocks using the first and second coordinate systems, convert each calibrated first image block to an intensity image and a first depth map, convert each calibrated second image block to a grayscale image, and generate a second depth map associated with the second image by enhancing a resolution of the first depth map for each calibrated first image block based on calculating a relationship between the intensity image and the grayscale image for each calibrated first and second image blocks.

Enabling adjustment of sharpness and details of an input image in upscaling, including: applying a Fourier transform function on a brightness channel of the input image to generate a 2-D frequency map; adjusting the 2-D frequency map to control a target amount of sharpness and details in an upscaled output image; and using the adjusted 2-D frequency map as an additional input channel along with standard color image data for a training and upscaling process.