4 results about "Image reduction" patented technology

The present application relates to remote sensing image processing technical field, especially to a kind of high bit depth remote sensing image reduction and enhancement method based on three levels optimization, comprising: cutting to remote sensing image, and screening out representative local sample block;Representative local sample block is decoupled and image enhancement in Lab space;With enhancement parameter as optimization variable, construct hierarchical objective function, and obtain optimal parameter by optimization algorithm optimization, and generate enhanced image by optimal parameter full-width processing original image;The whole scene enhanced image obtained is corrected.The present application is cooperated between representative local sample block construction, Lab decoupled enhancement and optimization learning, whole scene correction, realizes three levels closed loop operation of sample layer, parameter layer, whole scene layer, so that the last obtained high bit depth optical remote sensing image has good brightness level, detail distinguishability, display balance and color naturalness whole scene enhanced result.

To realize a new technology for performing multiple image processing operations using a support program in an information processing device that incorporates a general-purpose printing program. [Solution] When a print command is output from the editing application 43, the general-purpose printing program 41 generates intermediate data based on the image to be printed, and the auxiliary program 42 obtains the intermediate data and the print settings corresponding to the print command from the general-purpose printing program 41. The auxiliary program 42 performs a synthesis process to synthesize composite information such as watermarks into the intermediate data according to the print settings obtained from the general-purpose printing program 41, and after the synthesis process, it performs a layout process to reduce the size of images from multiple pages and place them on a single sheet of paper, or to divide an image from one page so that it is printed on multiple sheets of paper. Furthermore, the auxiliary program 42 controls the printer 2 to perform printing based on the edited image.

The present disclosure relates to adaptive neural network selection methods and systems, the method comprising: obtaining one or more images from a camera; obtaining a zoom factor of the camera, the zoom factor representing a measure of a current zoom level applied by the camera when capturing the one or more images; selecting a neural network from a plurality of neural networks configured to segment the one or more images and / or detect one or more objects in the one or more images based on the zoom factor, the plurality of neural networks being configured to operate at different image resolutions, a higher zoom factor corresponding to selecting a neural network configured to operate at a lower image resolution, and a lower zoom factor corresponding to selecting a neural network configured to operate at a higher image resolution; downscaling the one or more images to an image resolution required by the selected neural network; applying the selected neural network to segment the downscaled one or more images and / or detect the one or more objects in the down scaled one or more images.

The application discloses an unmanned aerial vehicle horizon real-time detection method and system based on improved YOLO segmentation, and relates to the technical field of horizon detection, and comprises the following steps: S1, acquiring a front view image or a video frame sequence of an unmanned aerial vehicle; S2, performing sky region segmentation by using a lightweight sky segmentation network; S3, extracting a low sky boundary point set from a sky mask; S4, fitting a current frame horizon parameter based on the low sky boundary point set; S5, performing adaptive time sequence smoothing processing based on boundary integrity; and S6, outputting the horizon parameter after time sequence smoothing. The application adopts the unmanned aerial vehicle horizon real-time detection method and system based on improved YOLO segmentation, extracts a sky region by using a lightweight sky segmentation network, reduces the horizon search range from a complex original image to a real demarcation region between the sky and the non-sky, and effectively reduces the interference of buildings, ground textures, cloud changes and complex background edges on the detection result.