Disclosed are a method of acquiring an image using a light-emitting element array and an apparatus therefor. The method of acquiring an image using a light-emitting element array includes reconstructing a first image from some images among source images, detecting a partial region containing a detection target object from the first image, acquiring partial-region images corresponding to the partial region from each of the source images, and reconstructing a second image from the partial-region images using the FPMP.

The present embodiment provides an image restoration method and apparatus which generate independent different restoration models by performing learning for each of different resolutions, receive a distorted image, and apply a restoration model corresponding to the resolution of the distorted image among the independent different restoration models to restore the distorted image into an improved upscaled image centering on a restoration target object within the distorted image.

The present invention discloses a multi-frame image super resolution system that utilizes both deep learning models and traditional models of enhancing the resolution of an image so that minimal computational resources are used. A frame alignment module of the invention aligns the frames of the image after which a processing module configured within the system process the Y and the UV channels of the image by using multiple deep and traditional resolution enhancement models. A merging unit merges the output of the processors to produce a super resolution image incorporating the advantages of both of the image enhancement methods.

A method the following operations: downscaling an input image to generate a scaled image; performing, to the scaled image, a first convolutional neural networks (CNN) modeling process with first non-local operations, to generate global parameters; and performing, to the input image, a second CNN modeling process with second non-local operations that are performed with the global parameters, to generate an output image corresponding to the input image. A system is also disclosed herein.

An image processing method and apparatus, and a method and apparatus for training an image processing model, which relates to the technical field of image processing. The image processing method includes: acquiring an original diffraction image; inputting the original diffraction image into an image processing model (S304); and by using the image processing model, performing restoration processing to the original diffraction image, to obtain a target standard image corresponding to the original diffraction image.

An electronic endoscope system includes a plotting unit which plots pixel correspondence points, which correspond to pixels that constitute a color image that has multiple color components, on a first color plane according to color components of the pixel correspondence points, the first color plane intersecting the origin of a predetermined color space; an axis setting unit which sets a predetermined reference axis in the first color plane; a transform unit which defines a second color plane that includes the reference axis, and subjecting the pixel correspondence points on the first color plane to projective transformation onto the second color plane; and an evaluation value calculating unit which calculates a prescribed evaluation value with respect to the color image based on the pixel correspondence points subjected to projective transformation onto the second color plane.

Provided are an image enhancement method and apparatus, an electronic device, and a storage medium. The image enhancement method includes: acquiring an original image, and configuring the original image as a current image; selecting a renderer from a plurality of pre-trained renderers as a current renderer in response to the current image satisfying a preset enhancement condition; and inputting the current image to the current renderer, and outputting, through the current renderer, an enhanced image of the current image in a dimension corresponding to the current renderer; and repeating the preceding operation by configuring the enhanced image of the current image in the dimension corresponding to the current renderer as the current image until the current image does not satisfy the enhancement condition.

A method for handling a fixed bound calibration data in an electronic device includes: determining, by the electronic device, a calibration data from a calibration setup; and generating and encoding, by the electronic device, a fixed bound calibration data based on the determined calibration data.

A processor-implemented method of generating feature data includes: receiving an input image; generating, based on a pixel value of the input image, at least one low-bit image having a number of bits per pixel lower than a number of bits per pixel of the input image; and generating, using at least one neural network, feature data corresponding to the input image from the at least one low-bit image.

An image processing method and a device configured to implement the same are disclosed. The method comprises: obtaining optical input from a hybrid imaging device, wherein an obtained optical input comprises a first component and a second component that temporally corresponds to the first component; wherein the first component of the obtained optical input corresponds to a first temporal resolution, while the second component of the obtained optical input corresponds to a second temporal resolution higher than that of the first component; performing image restoration operation on a first subset of the first component of the obtained optical input in accordance with data from the second component of the obtained optical input; and performing image fusion operation to generate fused image data from an output of the image restoration operation and a second subset of the first component of the obtained optical input.