Various methods and systems are provided for tracking a biopsy target across one or more images. In one example, a method includes determining a position of a biopsy target in a selected image of a patient based on an image registration process with a reference image of the patient, and displaying a graphical representation of the position of the biopsy target on the selected image.

Methods, devices, and systems for identifying charging devices are provided. In one aspect, a method of identifying a charging device include: capturing an infrared image and a depth image of a current field of view with a depth camera; determining, according to the infrared image, that there are one or more suspected charging device areas that satisfy first specified conditions; determining, according to the depth image, that there is a target charging device area whose height relative to a depth camera is within a specified range in the one or more suspected charging device areas; and identifying the charging device according to the target charging device area. The first specified conditions indicate that a gray-scale value of each of pixels in an area is greater than a second specified value, and a number of the pixels in the area is greater than a third specified value.

Embodiments of the present application provide a method and a device of inverse tone mapping and an electronic device. The method includes: obtaining one or more low dynamic range images; performing a decomposition operation to the low dynamic range image to acquire a detail layer and a basic layer of the low dynamic range image; restoring the detail layer and the basic layer by using a predetermined first restoration network and a second restoration network to acquire restored detail layer and basic layer; and adjusting the restored detail layer and basic layer by using a predetermined fusion network to acquire an adjusted high dynamic range image. With the technical solution of the present application, the conversion from a low dynamic range image to a high dynamic range image can be more robustly completed without complicated parameter settings.

A device includes an offset subtraction unit; an image sensor which receives, for each of a plurality of bright frames, a respective image signal obtained during a respective exposure time of the image sensor, and transmits the same to the offset subtraction unit, and receives, for a dark frame, a respective image signal obtained during a respective exposure time of the image sensor, and transmits the same to the offset subtraction unit; and a control unit which ensures that the image sensor alternately transmits a number of bright frames and one dark frame to the offset subtraction unit. An amount of light by which the respective image signal for each of the bright frames is generated is larger than an amount of light by which the respective image signal for the dark frame is generated; and the offset subtraction unit obtains an offset and subtracts the offset from a signal.

A method for analyzing an immunohistochemistry (IHC) image is provided and includes: segmenting nuclei from the IHC image according to a machine learning model; removing pixels belonging to the nuclei and pixels in a color range from the IHC image to obtain multiple cytoplasmic pixels; assign the cytoplasmic pixels to the nuclei to form multiple cells according to the locations of the cytoplasmic pixels; and calculate a pixel staining score of each pixel in the cells, thereby calculating a cell staining score for each cell.

An endoscopic camera device having an optical assembly; a first image sensor in optical communication with the optical assembly, the first image sensor receiving a first exposure and transmitting a first low dynamic range image; a second image sensor in optical communication with the optical assembly, the second image sensor receiving a second exposure and transmitting a second low dynamic range image, the second exposure being higher than the first exposure; and a processor for receiving the first low dynamic range image and the second low dynamic range image; wherein the processor is configured to combine the first low dynamic range image and the second dynamic range image into a high dynamic range image using a luminosity value derived as a preselected percentage of a cumulative luminosity distribution of at least one of the first low dynamic range image and the second low dynamic range image.

An image recognition device (image recognition system 100) according to the present disclosure includes an imaging unit (10) and a recognition unit (14). The imaging unit (10) captures a plurality of images at the same exposure start timing in one frame period by using imaging pixels having different sensitivities to generate image data. The recognition unit (14) recognizes a subject from each of the image data. The imaging unit (10) includes a pixel array in which a plurality of imaging pixels having different exposure times, different light transmittances of color filters, or different light receiving areas are two-dimensionally arranged.

A method includes obtaining multiple images of a scene using at least one red-green-blue-white (RGBW) image sensor. The method also includes generating multi-channel frames at different exposure levels from the images. The method further includes estimating motion across exposure differences between the different exposure levels using a white channel of the multi-channel frames as a guidance signal to generate multiple motion maps. The method also includes estimating saturation across the exposure differences between the different exposure levels to generate multiple saturation maps. The method further includes using the generated motion maps and saturation maps to recover saturations from the different exposure levels and generate a saturation-free RGBW frame. In addition, the method includes processing the saturation-free RGBW frame to generate a final image of the scene.

A processing method for enhancing the image quality of an image, more particularly a digital medical grey scale image, that comprises the steps of a) decomposing an original image into multiple detail images at different resolution levels and/or orientations, b) processing the detail images to obtain processed detail images, c) computing a result image by applying a reconstruction algorithm to the processed detail ages, said reconstruction algorithm being such that if it were applied to the detail images without processing, then said original image or a close approximation thereof would be obtained, the processing of the detail images comprises the steps of: d) calculating at least one conjugate detail image, and e) computing at least one value of the processed detail images as a function of said conjugate detail image and said detail images.

Example methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to implement global tone mapping of images based on luminance and chrominance are disclosed. Examples disclosed herein determine a chromatic gain to apply to input chrominance components corresponding to an input color of a pixel of the input image, the chromatic gain based on an input luminance component corresponding to the input color of the pixel and a luminance gain to be applied to the input luminance component of the pixel to determine an output luminance component of the pixel. Disclosed examples also apply the chromatic gain to the input chrominance components of the pixel to determine output chrominance components of the pixel. Disclosed examples further combine the output luminance component and the output chrominance components to determine an output color of the pixel.