Various methods and systems are provided for computed tomography imaging. In one embodiment, a method includes acquiring, with an x-ray detector and an x-ray source coupled to a gantry, a three-dimensional image volume of a subject while the subject moves through a bore of the gantry and the gantry rotates the x-ray detector and the x-ray source around the subject, inputting the three-dimensional image volume to a trained deep neural network to generate a corrected three-dimensional image volume with a reduction in aliasing artifacts present in the three-dimensional image volume, and outputting the corrected three-dimensional image volume. In this way, aliasing artifacts caused by sub-sampling may be removed from computed tomography images while preserving details, texture, and sharpness in the computed tomography images.

An embodiment method for converting an initial digital image into a converted digital image, electronic chip, system and computer program product are disclosed, the initial digital image comprising a set of pixels, the pixels being associated respectively with colors, the initial digital image being acquired by an acquisition device, and the converted digital image able to be used by a neural network. The embodiment method comprises redimensioning of the initial digital image in order to obtain an intermediate digital image, the redimensioning being carried out by a reduction in the number of pixels of the initial image, modification of a format of one of the pixels of the intermediate digital image in order to obtain a converted digital image, the modification being carried out, after the redimensioning, by increasing the number of bits used to represent the color of the pixel.

An image processing system with scalable models is provided. The image processing system comprises computing devices having a graphic analysis environment that includes instructions to execute an analysis process on a first image having a native resolution. The analysis process causes the one or more computing devices to perform operations includes: resampling the first image to generate a second image, wherein the second image has a resampled resolution greater than the native resolution in pixel number; detecting a plurality of first patches and a plurality of second patches in the first image and the second image, respectively, wherein the first patches and the second patches are detected by different detection models of a first scalable model collection according to sizes of the first image and the second image; and aggregating the first patches and the second patches. A method for processing an image with scalable models is also provided.

Methods and systems for high-resolution image manipulation are disclosed. An original high-resolution image to be manipulated is obtained, as well as a driving signal indicating a manipulation result. The original high-resolution image is down-sampled to obtain a low-resolution image to be manipulated. Using a trained manipulation generator, a low-resolution manipulated image and a motion field are generated from the low-resolution image. The motion field represent pixel displacements of the low-resolution image to obtain the manipulation indicated by the driving signal. A high-frequency residual image is computed from the original high-resolution image. A high-frequency manipulated residual image is generated using the motion field. A high-resolution manipulated image is outputted by combining the high-frequency manipulated residual image and a low-frequency manipulated image generated from the low-resolution manipulated image by up-sampling.

The current invention relates to the use of a neural network to improve the quality of images obtained from light scattered by an intermediate object that scatters light, such as tissue or a frosted screen. The invention relates to a method of imaging a human or animal bode using a nanocrystal array capable of fluorescing upon excitation from light from a near-infrared light source. This invention also relates to detection means and apparatus used in said methods, as well as to quantum dots useful in said use.

In a method for super resolution imaging, the method includes: receiving, by a processor, a low resolution image; generating, by the processor, an intermediate high resolution image having an improved resolution compared to the low resolution image; generating, by the processor, a final high resolution image based on the intermediate high resolution image and the low resolution image; and transmitting, by the processor, the final high resolution image to a display device for display thereby.

Users often desire to capture certain images from an application. Existing methods of capturing images can result in low-resolution images due to limitations of the display device providing the images. This disclosure provides a method of capturing higher resolution images from source images. Techniques are also disclosed to reduce the storage size associated with the higher resolution images. Through capturing low-resolution versions of the same source images, image effects can be captured and applied to the higher resolution images where those image effects may be altered or missing. Frequency spectrum combination can be used to combine the low-resolution image data and the higher resolution image data. The higher resolution images can be processed using a segmentation scheme, such as tiling, without reducing or limiting the image effects.

A system and a method for super-resolution image processing in remote sensing are disclosed. One or more sets of multi-temporal images with an input resolution and one or more first target images with a first output resolution are generated from one or more data sources. The first output resolution is higher than the input resolution. Each set of multi-temporal images is processed to improve an image match in the corresponding set of multi-temporal images. The one or more sets of multi-temporal images are associated with the one or more first target images to generate a training dataset. A deep learning model is trained using the training dataset. The deep learning model is provided for subsequent super-resolution image processing.

A method of transmitting image data in an image display system, includes dividing the image data into framebuffers, and for each framebuffer: dividing the framebuffer into a number of vertical stripes, each stripe including one or more scanlines, dividing each vertical stripe into at least a first and a second block, each of the first and the second block comprising pixel data to be displayed in an area of the image, and storing first pixel data in the first block with a first resolution and second pixel data in the second block having a second resolution which is lower than the first resolution, transmitting the framebuffer over the digital display interface to a decoder unit, and unpacking the framebuffer, including upscaling the pixel data in the second block to compensate for the lower second resolution and optionally upscaling the pixel data in the first block.

The present invention discloses a high-resolution hyperspectral computational imaging method and system and a medium. The method of the present invention comprises: conducting spectral upsampling on an input RGB image Y to obtain an initial hyperspectral image X.sub.0; and inputting the initial hyperspectral image X.sub.0 into a pre-trained deep convolutional neural network guided by an imaging model, and conducting iteration computation to obtain a hyperspectral image X. The present invention can effectively achieve reconstruction of the RGB image to the high-resolution hyperspectral image and has the advantages of high reconstruction precision, high computational efficiency, little memory consumption and strong generalization ability.