A method and a device for enhancing brightness and contrast of a video image are provided. In the method, an inflection point, a truncation point, a maximum value of a brightness component, and a minimum value of the brightness component of an image frame to be processed are determined based on the brightness component of the image frame to be processed, a piecewise linear function is determined based on the inflection point, and brightness and contrast enhancement processing is performed on the image frame to be processed based on the piecewise linear function. Compared with the brightness and contrast enhancement method in conventional art, the method and device of the application can achieve better brightness and contrast enhancement effects.

To enable practical and quick generation of a family of good looking HDR gradings for various displays on which the HDR image may need to be shown, we describe color transformation apparatus (201) to calculate resultant colors (R2, G2, B2) of pixels of an output image (IM_MDR) for a display with a display peak brightness (PB_D) starting from input colors (R,G,B) of pixels of an input image (Im_in) having a maximum luma code corresponding to a first image peak brightness (PB_IM1) which is different from the display peak brightness, characterized in that the color transformation apparatus comprises: a color transformation determination unit (102) arranged to determine a color transformation (TMF; g) from color processing specification data (MET_1) comprising at least one tone mapping function (CC) for at least a range of pixel luminances received via a metadata input (116), which color transformation specifies the calculation of at least some pixel colors of an image (IM_GRAD_LXDR) having corresponding to its maximum luma code a second image peak brightness (PB_IM2), which is different from the display peak brightness (PB_D) and the first image peak brightness (PB_IM1), and whereby the division of the first image peak brightness by the second image peak brightness is either larger than 2 or smaller than ½; a scaling factor determination unit (200) arranged to determine a resultant common multiplicative factor (gt), by comprising: a capability metric determination unit (1303) arranged to determine a metric for locating positions of display peak brightnesses between the first image peak brightness (PB_IM1), and the second image peak brightness (PB_IM2) and outside that range; and a resultant multiplier determination unit (1310) arranged to determine from the display peak brightness (PB_D), the metric, and the color transformation the resultant common multiplicative factor (gt), and wherein the color transformation apparatus (201) further comprises a scaling multiplier (114) arranged to multiply a linear RGB color representation of the input colors with the resultant common multiplicative factor (gt).

A system and method for processing mammographic images of target breast tissue is provided. The mammographic images are processed to generate modified images. A deep learning algorithm, having a tailored Convolutional Neural Networks (CNN) model, is applied to the modified images to generate a first output and a second output. Global features associated with the entirety of the mammographic images are extracted by using the first output. Local features associated with Regions of Interest (ROIs) of the mammographic images are extracted by using the second output. The global features and the local features are combined and fuse to generate an indicator representative of likelihood of malignancy of the target breast tissue.

Disclosed are an image data processing method and a sensor device performing the same. The sensor device includes an image sensor configured to acquire image data, an image buffer configured to store the image data, and an image processor configured to generate image-processed data by applying a filter corresponding to a storage pattern of the image buffer to the image data stored in the image buffer.

In order to improve the noise suppression in a video image stream 3 of a medical image recording system, the video image stream including a sequence of frames, it is provided that an image processing unit 5 of the image recording system analyses the video image stream 3 continuously in real time and determines at least one variability between successive image pixels of the frames, for example of spatially adjacent image pixels of frames and/or of image pixels of a plurality of the frames corresponding to one another spatially and temporally, in order, on the basis of the variability determined, to set at least one parameter of a noise suppression subsequently applied to the video image stream 3. As a result, the noise suppression can be adapted continuously to a current recording situation.

For medical imaging such as MRI, machine training is used to train a network for segmentation using both the imaging data and protocol data (e.g., meta-data). The network is trained to segment based, in part, on the configuration and/or scanner, not just the imaging data, allowing the trained network to adapt to the way each image is acquired. In one embodiment, the network architecture includes one or more blocks that receive both types of data as input and output both types of data, preserving relevant features for adaptation through at least part of the trained network.

Systems and methods are disclosed and an example system includes a digital image receiver for receiving a digital image, and an automatic obscuration processor coupled to the image receiver and configured to determine whether the digital image includes a region that classifies as an image of a category of object and, upon a positive determination, to obscure the region and output a corresponding obscured-region digital image.

Systems and methods to perform noise-adaptive non-blind deblurring on an input image that includes blur and noise involve implementing a first neural network on the input image to obtain one or more parameters and performing regularized deconvolution to obtain a deblurred image from the input image. The regularized deconvolution uses the one or more parameters to control noise in the deblurred image. A method includes implementing a second neural network to remove artifacts from the deblurred image and provide an output image.

An electronic device configured to control a host vehicle includes: an image sensor configured to photograph a surrounding environment of the host vehicle; and a processor configured to perform an image processing operation based on a first image captured by the image sensor, and control the host vehicle based on the processing result, wherein the processor determines whether to use a high speed performance of the image processing operation based on a speed of the host vehicle, and the electronic device is configured such that when the high speed performance is not used, the processor performs the image processing operation by using a first image processing module, and when the high speed performance is used, the processor performs the image processing operation by using a second image processing module having less data throughput than the first image processing module.

An image processing device and image processing methods that improve image quality by reducing latency and improving the performance of local processing are provided.

An image processing device using an image input signal as an input and an image output signal as an output includes a first image processing unit and a second image processing unit. The first image processing unit includes a histogram processing unit for extracting image characteristic data from the image input signal, a first image parameter processing unit for creating a first parameter group for performing image processing from the image characteristic data, and an arithmetic processing unit for processing the image input signal to create an image output signal. The second image processing unit has a second image parameter processing unit for creating a second parameter group for performing image processing from the image characteristic data.