Devices, systems, and methods for enhancing color consistency in images are disclosed. A method may include activating, by a device, a camera to capture first image data; while the camera is capturing the first image data, activating of a first light source; receiving the first image data, the first image data having pixels having first color values; identifying first light generated by the first light source while the camera is capturing the first image data; identifying, based on the first image data, second light generated by a second light source; generating, based on the first light, the second light, and a distance between the camera and the vehicle light, second color values for the pixels of the first image data; generating second image data based on the second color values; and presenting the second image data.

Provided are an image exposure adjustment method and apparatus, a device, and a medium. The image exposure adjustment method includes performing human body detection on a collected image; in a case where a human body is detected, segmenting the image to determine a foreground region and a background region in the image; determining a mask image according to the foreground region and the background region; and determining an exposure weight table according to the mask image, and performing exposure value adjustment on the image according to the exposure weight table.

There is provided a see-through display and a method for operating a see-through display. The display is configurable to display additional image content for augmenting a user's view of a scene visible through the display. According to the method, image data are received defining an image of a scene visible through the display. By analysis of the received image data, one or more characteristics of the scene are determined. A light effect to be applied to the user's view of the scene is determined. Additional image content is generated according to the determined light effect and according to the one or more determined characteristics of the scene. The generated additional image content is displayed to the user such that light received from the scene is combined with the additional image content, thereby to implement the determined light effect in the user's view of the scene.

The present disclosure relates generally to the field of video processing, and more particularly to high dynamic range (HDR) video and image processing. In particular, the present disclosure relates to determining one or more curve parameters of a tone mapping curve. For example, a device (encoder or decoder) may obtain a high dynamic range (HDR) video frame and metadata associated to the HDR video frame. The device further obtains a pair of anchor points, based on the HDR video frame and the metadata. The pair of anchor points comprises a first anchor point and a second anchor point of the tone mapping curve. Moreover, the device generates the one or more curve parameters of the tone mapping curve based on the pair of anchor points.

An image processing method includes: acquiring a standard image matched with an image processing model; determining light field variation parameters of a use environment of the image processing model based on the standard image; determining an image training sample set based on the light field variation parameters, the image training sample set including images of different light field backgrounds; training a correction network of the image processing model through the image training sample set to obtain a model updating parameter adapted to the correction network, and generating a trained image processing model based on the model updating parameter; acquiring a single image in the use environment, and performing prediction processing on the single image through the trained image processing model to obtain light field information corresponding to the single image; and correcting a light field of the single image based on the light field information.

Certain aspects of the present disclosure provide an ophthalmic imaging device for enhancing images. The device comprises an image capture component configured to generate an image stream comprising a first frame preceding a second frame, both capturing a branch of veins in an eye. The device further comprises an image processor configured to calculate first order statistics for a plurality of blocks for the first frame and to interpolate first order statistics for the first frame based at least in part on the branch of veins. The image processor is also configured to generate a tone mapping function and calculate tone mapping values for individual pixels of the second frame based on the tone mapping function. The image processor is additionally configured to generate an enhanced frame based on the calculated tone mapping values for individual pixels of the second frame to the pixels of the second frame.

One embodiment provides a computer-implemented method that includes providing a machine learning network including a global inverse tone mapping (GITM) structure and a local inverse tone mapping (LITM) structure that utilize one or more non-linear basis functions with one or more coefficient functions. The one or more non-linear basis functions learn linearly to facilitate combination with at least one convolution layer for jointly learning the machine learning network. A weighted mask (WM) is provided for reducing one or more visual artifacts, including one or more quantization artifacts in a smooth region of an output of the machine learning network.

This application discloses a method for adjusting image luminance performed at an electronic device. The method includes: determining a target pixel with original luminance lower than a luminance threshold in an original image, the luminance threshold being determined according to luminance of pixels in the original image; obtaining a luminance distribution intensity of pixels adjacent to the target pixel; determining a difference between the luminance threshold and the luminance distribution intensity of the adjacent pixels; and adjusting the target pixel to corresponding target luminance according to the difference and the original luminance of the target pixel. According to this application, a changing characteristic of relative luminance between the target pixel and the adjacent pixel is reserved. The luminance adjustment is more consistent with the luminance propagation of the original image, thereby achieving a technical effect of improving an adjustment effect of luminance adjustment on an image.

An apparatus for image conversion includes a processor configured to classify a reference region representing a predetermined feature into a shadowed region and an unshadowed region, the reference region being in an aerial image represented in RGB color space; determine a tone correction factor so that a difference between an average luminance of the shadowed region and an average luminance of the unshadowed region in the aerial image represented in predetermined color space to which the color space of the aerial image is converted from RGB color space is less than a difference between an average luminance of the shadowed region and an average luminance of the unshadowed region represented in RGB color space; correct tones of the aerial image with the tone correction factor; and convert the color space of the aerial image from RGB color space to the predetermined color space to generate a color conversion image.

Multiple lookup tables (LUTs) storing different numbers of control point values are used to process pixels within different blocks of an image, such as after image processing using tone mapping and/or tone control, and/or to collect histogram information or implement 3D LUTs. First control point values stored within a first LUT are applied against pixels of a given block of an image to produce a distorted image block. Second control point values stored within a second lookup table are applied against a pixel of the distorted image block to produce a processed pixel. The second LUT is one of a plurality of second LUTs and stores fewer values than the first LUT. A processed image is produced using the processed pixel. The processed image is then output for further processing or display.