The disclosed computer-implemented method for protecting user privacy may include (i) receiving an indication to protect a photo with privacy-protecting blurring, (ii) generating a blurred version of the photo, (iii) generating, based on the blurred version of the photo, a video that progressively de-blurs the photo, (iv) linking through metadata the blurred version of the photo and the video that progressively de-blurs the photo as a combined motion-photo-object, and (v) storing the combined motion-photo-object in a configured location such that a photo display program uses the blurred version of the photo as a preview of the motion-photo-object when browsing but plays the video that progressively de-blurs the photo in response to additional user input selecting the preview. Various other methods, systems, and computer-readable media are also disclosed.

Techniques for generating a fused enhanced image. A first image is generated using a first camera of a first modality, and a second image is generated using a second camera of a second modality. Pixels that are common between the two images are identified. Textures for the common pixels are determined. A camera characteristic, which is linked to noise, is identified. A scaling factor is applied to the textures in the first image. A first saliency is determined using the scaled textures. A second saliency is determined using the textures from the second image. An alpha map is generated and reflects edge detection weights that have been computed for each one of the common pixels based on the two saliencies. Based on the alpha map, textures are merged from the common pixels to generate the fused enhanced image.

Disclosed is an image sensing device including a plurality of defect detectors each suitable for detecting whether a corresponding target image value is defective, and generating detection information corresponding to a result of the detection, a defect scheduler suitable for sequentially outputting one or more defective image values, which are the target image values detected as defective among the plurality of target image values, based on the detection information, and a defect corrector suitable for correcting the output defective image values.

A laparoscopic image smoke removal method based on a generative adversarial network, and belongs to the technical field of computer vision. The method includes: processing a laparoscopic image sample to be processed using a smoke mask segmentation network to acquire a smoke mask image; inputting the laparoscopic image sample to be processed and the smoke mask image into a smoke removal network, and extracting features of the laparoscopic image sample to be processed using a multi-level smoke feature extractor to acquire a light smoke feature vector and a heavy smoke feature vector; and acquiring, according to the light smoke feature vector, the heavy smoke feature vector and the smoke mask image, a smoke-free laparoscopic image by filtering out smoke information and maintaining a laparoscopic image by using a mask shielding effect. The method has the technical effects of robustness and ability of being embedded into a laparoscopic device for use.

A method of processing data in a graphics processor when performing tile-based rendering in which a render output is sub-divided into a plurality of tiles for rendering. The rendering is performed as two separate processing passes: a first processing pass that sorts primitives into respective regions of the render output and a second processing pass that renders the tiles into which the render output is sub-divided for rendering. During the first processing pass, “tile elimination” data is generated indicative of which of the rendering tiles should be rendered during the second processing pass. The tile elimination data generated in the first processing pass can then be used to control the rendering of tiles during the second processing pass.

Provided are a panoramic photographing method and device, a camera and a mobile terminal, the method includes: when panoramic photographing is performed, firstly, a ranging device acquires the depth information of the object to be photographed, that is, the distance between the object to be photographed and the photographing device, and then the offset corresponding to the current distance is acquired according to a preset mapping relationship, and the original imaging of the object to be photographed is subsequently shifted in the panoramic image to form a corrected panoramic image.

The present disclosure provides a method of volumetric imaging of a sample. The method comprises providing a plurality of depth images of a region of interest of the sample using a volumetric imaging system. The region of interest is below a surface area of interest of the sample. Each depth image is associated with a layer or slice of the region of interest and the plurality of depth images together forming a volumetric image of the region of interest. The method further comprises providing a surface image of the surface area of interest of the sample and identifying a surface image property of a surface feature of the surface area of interest. The method also comprises processing the plurality of depth images of the region of interest using the surface image property of the surface feature to improve a property of the depth images of the region of interest.

A division unit divides an axisymmetric structure in a medical image including the structure into a plurality of predetermined regions. A reference line derivation unit derives a reference line of the structure on the basis of the plurality of divided regions.

A medical data processing apparatus includes a memory and processing circuitry. The memory stores a learned model including an input layer to which first MR data and second MR data having the same imaging target as the first MR data and an imaging parameter different from the first MR data are inputted, an output layer from which third MR data is output with a missing portion of the first MR data restored, and at least one intermediate layer arranged between the input layer and the output layer. The processing circuitry generates third MR data relating to the subject, from the first MR data serving as a process target and relating to the subject and the second MR data relating to the subject and acquired by an imaging parameter different from the first MR data serving as the process target, in accordance with the learned model.

A computer-implemented method for automatically identifying subjects at risk of Multiple Sclerosis (MS) includes acquiring a plurality of images of a subject's brain using a Magnetic Resonance Imaging (MRI) scanner. A contrast enhancement process is applied to each image to generate a plurality of contrast-enhanced images. An automated lesion detection algorithm is applied to detect one or more lesions present in the contrast-enhanced images. An automated central vein detection algorithm is applied to detect one or more central veins present in the contrast-enhanced images. An automated paramagnetic rim detection algorithm is applied to detect one or more paramagnetic rims present in the contrast-enhanced images. The patient's risk for MS may then be determined based on the one or more of the lesions, central veins, and paramagnetic rims present in the contrast-enhanced images.