Methods for improving and modifying a High Dynamic Range (HDR) scene, captured as a series of images of the scene with different exposure levels and the scene through classification-based image merging, tuning, correction, and replacement. The approach employs mixing images to improve the selection and display of both shadowed and highlighted details. The increased efficiency resulting from improvements in computational resource utilization of image processing hardware can, from the implementation of the improved computational methods herein, significantly reduce the time required to generate and display a tone-mapped HDR image, a gamma-corrected HDR image, and/or a segmented and replaced HDR image.

An image processing system (10) according to an example aspect of the present disclosure includes: an image acquisition unit (102) configured to acquire a monochrome image; a pixel value correction unit (120) configured to correct a pixel value of the monochrome image based on information related to a pixel value; and a colorization generation unit (130) configured to generate a colorized image corresponding to the monochrome image from the corrected monochrome image by using a colorization prediction model trained by machine learning. With the present disclosure, it is possible to improve the reproduction accuracy of a color in colorization of a monochrome image.

Methods, systems, and devices for equipment reading in a factory or plant environment are described, including: capturing an image of an environment including a measurement device; detecting a target region included in the image, the target region including at least a portion of the measurement device; determining identification information associated with the measurement device based on detecting the target region; and extracting measurement information associated with the measurement device based on detecting the target region. In some aspects, detecting the target region may include: providing the image to a machine learning network; and receiving an output from the machine learning network in response to the machine learning network processing the image based on a detection model, the output including the target region.

The present disclosure relates to systems, methods, and non-transitory computer readable media for efficiently, quickly, and flexibly applying median filters to digital image utilizing a separable sorting network approach for computation sharing. For example, the disclosed systems generate a modified digital image by determining pixel values for a number of output tiles in applying a median filter. To generate output tiles, in some implementations, the disclosed systems utilize different forms of separability to precompute sorted columns of pixels to reduce the size of per-pixel tasks required to generate output pixels and to share computations among nearby pixels of an input tile captured from a digital image in generating output tiles. In some implementations, the disclosed systems utilize an interpreter to generate and apply a median filter to a digital image at runtime based on a user-selected filter size.

Embodiments disclose systems and methods that aid in screening, diagnosis and/or monitoring of medical conditions. The systems and methods may allow, for example, for automated identification and localization of lesions and other anatomical structures from medical data obtained from medical imaging devices, computation of image-based biomarkers including quantification of dynamics of lesions, and/or integration with telemedicine services, programs, or software.

The present application relates to a parallax image processing method, an apparatus, a computer device and a storage medium. The method includes: obtaining at least one mask image by performing down-sampling on an initial parallax image, and then performing patch processing and pixel replacement processing on the mask image to obtain a processed mask image, and determining an abnormal parallax value on the initial parallax image according to the processed mask image, and finally performing interpolation processing and filtering processing on the abnormal parallax value on the initial parallax image to obtain a target parallax image.

ROI (Region of Interest) detection is an important step in extracting relevant information from a document image. Such images are very high-resolution images in nature and size of images is in order of megabytes, which makes text detection pipeline very slow. Traditional methods detect and extract ROI from images, but these work only for specific image types. Other approaches include deep learning (DL) based methods for ROI detect which need intensive training and require high end computing infrastructure/resources with graphical processing unit (GPU) capabilities. Systems and methods of the present disclosure perform ROI extraction by partitioning input image into parts based on its visual perception and then classify the image in first or second category. Region of interest is extracted from a resized image based on the classification by applying image processing techniques. Further, the system determines whether the input image is a pre-cropped image or a normal scanned image.

A method of depth estimation, including, receiving an image frame, determining a relative depth map based on the image frame, receiving a sparse depth frame, preprocessing the sparse depth frame, determining a scale-adjusted relative depth map based on the relative depth map and the preprocessed sparse depth frame and fusing the relative depth map and the scale-adjusted relative depth map to produce an absolute depth map.

An image processing method, and electronic device are provided. The image processing method includes: obtaining a raw RGBW image by an RGBW sensor; processing the raw RGBW image by a pre-processor to obtain a raw RGB image; processing the raw RGB image by an image signal processor to obtain a target image. By adopting the image processing method of the present disclosure, conventional image processing modules are compatible with RGBW sensors without modification, and thus do not need to be replaced in practical applications, which saves hardware costs and facilitates product upgrades.

In part, the disclosure relates to an automated method of branch detection with regard to a blood vessel imaged using an intravascular modality such as OCT, IVUS, or other imaging modalities. In one embodiment, a representation of A-lines and frames generated using an intravascular imaging system is used to identify candidate branches of a blood vessel. One or more operators such as filters can be applied to remove false positives associated with other detections.