The invention concerns a method for converting an input image comprising an input luminance component made of elements into an output image comprising an output luminance component made of elements, the respective ranges of the output luminance component values and input luminance component element values being of different range extension. the method comprises for the input image: computing a value of a general variable representative of at least two input luminance component element values; transforming each input luminance component element value into a corresponding output luminance component element value according to the computed general variable value; and converting the input image using the determined output luminance component element values. The transforming step uses a set of pre-determined output values organized into a 2D Look-Up-Table (2D LUT) comprising two input arrays indexing a set of chosen input luminance component values and a set of chosen general variable values respectively, each pre-determined output value matching a pair of values made of an indexed input luminance component value and an indexed general variable value, the input luminance component element value being transformed into the output luminance component element value using at least one predetermined output value.

Systems and methods of PET attenuation correction using low-field MR image data includes receiving a first set of image data and a set of low-field magnetic resonance (MR) image data. An attenuation correction map is generated from the low-field MR image data using a first trained neural network. At least one attenuation correction process is applied to the first set of image data based on the attenuation correction map to generate at least one clinical attenuation-corrected image.

In some implementations, a method includes: determining a complexity value for first image data associated with of a physical environment that corresponds to a first time period; determining an estimated composite setup time based on the complexity value for the first image data and virtual content for compositing with the first image data; in accordance with a determination that the estimated composite setup time exceeds the threshold time: forgoing rendering the virtual content from the perspective that corresponds to the camera pose of the device relative to the physical environment during the first time period; and compositing a previous render of the virtual content for a previous time period with the first image data to generate the graphical environment for the first time period.

A high efficiency method of processing images to provide perceptual high-contrast output. Pixel intensities are calculated by a weighted combination of a fixed number of static bounding rectangle sizes. This is more performant than incrementally growing the bounding rectangle size and performing expensive analysis on resultant histograms. To mitigate image artifacts and noise, blurring and down-sampling are applied to the image prior to processing.

The present invention relates to a method for size estimation by image recognition of a specific target using a given scale. First, a reference objected is recognized in an image and the corresponding scale is established. Then the specific target is searched and the size of the specific target is estimated according to the acquired scale.

Disclosed are an image synthesis method and a related apparatus, the method includes: determining a target area on a preview image by means of eyeball tracking technology; on the basis of brightness parameters of the target area, determining multiple exposure parameter sets; setting a camera module with the multiple exposure parameter sets to acquire multiple reference images, each reference image corresponding to a different exposure parameter set; and synthesizing the multiple reference images to obtain a target image.

Embodiments of the present invention are a high dynamic range (HDR) synthesis method and apparatus, an image processing chip and an aerial camera. The method includes: acquiring a plurality of to-be-synthesized images having different exposure time; calculating a mean brightness of the to-be-synthesized images; determining an image brightness type of the to-be-synthesized images according to the mean brightness; calculating a brightness difference between adjacent pixel points in one to-be-synthesized image; calculating an inter-frame difference of different to-be-synthesized images at a same pixel point position according to the brightness difference; determining a motion state of the to-be-synthesized images at the pixel point position according to the inter-frame difference; and weighting and synthesizing the to-be-synthesized images into a corresponding HDR image according to the image brightness type and the motion state.

An image processing unit of a processor for an endoscope includes: an emphasis processing calculation unit performing nonlinear gradation conversion for a pixel value of a pixel of interest, using each pixel of a captured image as the pixel of interest; and a preprocessing unit setting a reference upper limit characteristic line and a reference lower limit characteristic line in order to adjust an output pixel value after the nonlinear gradation conversion and calculating a degree of variation in pixel values in a partial setting region around the pixel of interest. The emphasis processing calculation unit calculates an output ratio of the output pixel value to a maximum pixel value that can be taken by the captured image, an adjustment upper limit value and an adjustment lower limit value, and an emphasis-processed pixel value, using the adjustment upper limit value, the adjustment lower limit value, and the output ratio.

A method of enhancing an x-ray image is disclosed. The method involves obtaining an input image based on a source x-ray image of an object. Compositional information representing physical characteristics of the object is also obtained. An image enhancement process is applied to the input image to generate a processed image. Application of the image enhancement process is controlled by one or more parameters determined in dependence on the compositional information. An output image is then provided based on the processed image.

To enable a high quality HDR video communication, which can work by sending corresponding LDR images potentially via established LDR video communication technologies, which works well in practical situations, applicant has invented a HDR video decoder (600, 1100) arranged to calculate a HDR image (Im_RHDR) based on applying to a received 100 nit standard dynamic range image (Im_RLDR) a set of luminance transformation functions, the functions comprising at least a coarse luminance mapping (FC), which is applied by a dynamic range optimizer (603), and a mapping of the darkest value (0) of an intermediate luma (Y′HPS), being output of the dynamic range optimizer, to a received black offset value (Bk_off) by a range stretcher (604), the video decoder comprising a gain limiter (611, 1105) arranged to apply an alternate luminance transformation function to calculate a subset (502) of the darkest luminances of the HDR image, from corresponding darkest lumas (Y′_in) of the standard dynamic range image.