Provided are a fiber bundle image processing method (200) and an apparatus. The method (200) includes: determining pixel information corresponding to a center position of a fiber in a sample image; correcting the determined pixel information; and reconstructing the sample image based on the corrected pixel information to obtain a reconstructed image. The method (200) and apparatus can not only obtain a more ideal fiber-bundle processed image, but also have a smaller calculation amount, and the entire calculation process takes less time.

An image processing device includes a processor; and a memory to store the program which performs processing including: measuring a first texture amount indicating luminance variation in image regions, based on the image regions obtained by dividing the captured image and previously determining an image processing target region based on the first texture amount; judging whether the image processing on a current captured image is necessary based on luminance of the image processing target region in the current captured image; calculating a second texture amount indicating luminance variation in the image processing target region, and judging whether the image processing should be performed on the image processing target region or not based on the second texture amount; and performing the image processing on the image processing target region on which the judging based on the second texture amount is that the image processing should be performed.

Noise is adequately reduced irrespective of the content of an input image. In an embodiment of the present invention, an image processing apparatus that executes noise reduction processing of an image includes: a first estimation unit that estimates noise contained in the image; a second estimation unit that estimates an original image, which is the image from which the noise is removed; a noise reduction unit that performs the noise reduction processing on each of partial areas of the image by using the first estimation unit or the second estimation unit depending on the contents of the partial areas; and an integration unit that integrates the partial areas on which the noise reduction processing is performed.

A multi-scale image processing algorithm for enhancing contrast in an electronic representation of an image represented by a) decomposing said digital image into a set of detail images at multiple resolution levels and a residual image at a resolution level lower than said multiple resolution levels, b) processing at least one pixel of said detail images, c) computing a processed image by applying a reconstruction algorithm to the residual image and the processed detail images, said reconstruction algorithm being such that if it were applied to the residual image and the detail images without processing, then said digital image or a close approximation thereof would be obtained. The processing comprises the steps of: d) calculating for said pixel, at least one statistical measure for two or more translation difference image pixel values within a neighbourhood of said pixel; and e) modifying the value of said pixel of said detail images as a function of said statistical measure and said value of said pixel of said detail images.

Disclosed in this specification are methods, systems and apparatus, including computer programs encoded on non-transitory computer storage media for unmanned aerial vehicle (UAV) flight operation and privacy controls. Based on geofence types, and UAV distance from a geofence, sensors and other devices connected to a UAV are conditionally operational. Image data collected during a UAV flight may be obfuscated by the UAV while in flight, or via a post-flight process using log data generated by the UAV.

Provided are an image processing method and device, a mobile terminal, and a storage medium. The method includes: acquiring a first image output by an image sensor; responsive to that a first signal value of the first image after being processed exceeds a saturation value of a first bit width at a preset image processing stage, recording the first signal value of the processed first image as a second signal value with a second bit width, the second bit width being greater than the first bit width; and mapping the second signal value recorded with the second bit width to a third signal value recorded with the first bit width to obtain a second image. Through the method, an operation of increasing a bit width can be used to retain more information of an image and improve the quality of the image, and an implementation is simple and effective.

This application discloses a video data processing method and apparatus, a computer device, and a storage medium, and belongs to the field of video processing technologies. According to this application, when a second video is embedded into a target sub-image of a first video, a position movement of the target sub-image in the first video is obtained based on an artificial intelligence technology. According to the position movement, motion blur processing is performed on the picture of the second video correspondingly, so that the second video, after being embedded into the first video, will have the same motion blur effect as the target sub-image in the first video. Therefore, the first video and the second video can be better fused, and a better video transition effect can be obtained.

A method of improving image quality of a stream of input images is described. The stream of input images, including a current input image, is received. One or more target objects, including a first target object, are identified spatio-temporally within the stream of input images. The one or more target objects are tracked spatio-temporally within the stream of input images. The current input image is segmented into i) a foreground including the first target object, and ii) a background. The foreground is processed to have improved image quality in the current input image. Processing of the foreground further comprises processing the first target object using a same processing technique as for a prior input image of the stream of input images based on the tracking of the first target object. The background is processed differently from the foreground. An output image is generated by merging the foreground with the background.

An image processing apparatus including a division unit configured to divide first image data having a first dynamic range into a plurality of regions, an obtaining unit configured to obtain distance information indicating a distance from a focal plane in each of the plurality of regions, a determining unit configured to determine a conversion characteristic of each of the plurality of regions based on the distance information, a conversion unit configured to convert each of the plurality of regions into second image data having a second dynamic range smaller than the first dynamic range by using the conversion characteristic determined by the determining unit, and a storage unit configured to store a first conversion characteristic and a second conversion characteristic that can be used for the conversion.

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.