Various methods and systems are provided for reducing blur in a diagnostic image. In one example, a method includes reducing blur in a diagnostic image by applying a deconvolution filter to the diagnostic image, the deconvolution filter generated from a point spread function (PSF) estimation of blur at each pixel of the diagnostic image, the PSF estimation generated based on a motion vector field between the diagnostic image and a pre-shot image acquired prior to the diagnostic image.

An apparatus includes a subject detection unit configured to detect a specific subject in an input image, an acquisition unit configured to acquire camera information, an estimation unit configured to estimate a target of interest in an image using the subject information and the camera information, a first motion detection unit configured to detect background motion and subject motion, a conversion unit configured to convert the detected background motion and the detected subject motion to a first blur correction amount for correcting background blur and a second blur correction amount for correcting subject blur, respectively, and a correction amount calculation unit configured to, based on the target of interest, combine the first blur correction amount and the second blur correction amount and generate a final blur correction amount.

An image processing method for improving image quality selects one of a plurality of two-dimensional pixel matrix source images of an imaged target as a reference image. Movement of the target is detected in the source images. The detected movement between the reference image and at least one other image of the source images is compensated differently in at least two different sections of the at least one other source image. The reference image and the movement compensated at least one other image are combined for forming an improved image.

An image processing apparatus includes a determination unit configured to determine an optical transfer function based on shooting condition information, an acquisition unit configured to acquire shake information, a control unit configured to select a method of image restoration based on the optical transfer function and the shake information, and an image restoration unit configured to perform the image restoration based on the selected method of the image restoration.

A method of video deblurring by an electronic device is described. The processing circuitry of the electronic device acquires N continuous image frames from a video clip the N being a positive integer, and the N continuous image frames including a blurry image frame to be processed. The processing circuitry of the electronic device performs three-dimensional (3D) convolution processing on the N continuous image frames with a generative adversarial network model, to acquire spatio-temporal information corresponding to the blurry image frame. The spatio-temporal information includes spatial feature information of the blurry image frame, and temporal feature information between the blurry image frame and a neighboring image frame of the N continuous image frames The processing circuitry of the electronic device performs deblurring processing on the blurry image frame by using the spatio-temporal information corresponding to the blurry image frame through the generative adversarial network model, to output a sharp image frame.

A method for evaluating image quality is provided. The method may include: obtaining an image, the image including a plurality of elements, each element of the plurality of elements being a pixel or voxel, each element having a gray level; determining, based on a maximum gray level of the plurality of elements, one or more thresholds for segmenting the image; determining one or more sub-images of a region of interest by segmenting, based on the one or more thresholds, the image; and determining, based on the one or more sub-images of the region of interest, a quality index for the image.

A vehicle control system for automated driver-assistance includes a low-resolution color camera that captures a color image of a field of view at a first resolution. The vehicle control system further includes a high-resolution scanning camera that captures a plurality of grayscale images, each of the grayscale images at a second resolution. The second resolution is higher than the first resolution. The grayscale images encompass the same field of view as the color image. The vehicle control system further includes one or more processors that perform a method that includes overlaying the plurality of grayscale images over the color image. The method further includes correcting motion distortion of one or more objects in the grayscale images. The method further includes generating a high-resolution output image by assigning color values to one or more pixels in the grayscale images based on the color image using a trained neural network.

A computer-implemented method of validating motion correction of magnetic resonance (MR) images in a quantitative parametric map of a parameter is provided. The method includes receiving a series of motion corrected images of a series of MR images, wherein the series of MR images are MR images of an anatomical region at different points of time. For each pixel of a plurality of pixels in one of the series of motion corrected images, the method further includes generating a time series at the pixel based on the series of motion corrected images, fitting the time series to a model of the parameter, and estimating an indicator of goodness of fit of the time series to the model. The method also includes generating a metric indicating effectiveness of the motion correction based on estimated indicators of goodness of fit of the plurality of pixels, and validating the motion correction based on the metric.

A method for automatically enhancing image data comprising receiving image data. Utilizing a neural network to analyze the image data to predict how each of a plurality of image enhancement processes will affect the image data. Utilizing the neural network to calculate a reward value for each of the plurality of enhancement processes that can be applied to the image data. Determining if the image data should be enhanced or not, wherein the determination is based on the predictions how each of the plurality of image enhancement processes will affect the image data. When it is determined that the image data should be enhanced, then determining which of the plurality of image enhancing process should be applied to the image data, wherein determination is based on the reward value for each of the plurality of enhancement processes. Applying the determined enhanced processes to the image data.

A method for enhancing image quality using an electronic device is provided. The method includes receiving, by the electronic device, an image frame from a network, monitoring, by the electronic device, a network parameter of the network, determining, by the electronic device, a context of the electronic device, identifying, by the electronic device, a visual degradation in the image frame based on the monitored network parameter and the context of the electronic device, and modifying, by the electronic device, a visual quality factor of the image frame based on the visual degradation in the image frame for enhancing the image quality.