5 results about "Local histogram" patented technology

This application provides a multi-scale hybrid DR image enhancement method, apparatus, electronic device, and storage medium. The method includes: acquiring a target image to be identified; dividing the target image into N blocks according to N different block scales to obtain a first block image corresponding to each block scale; for each block scale, performing histogram equalization processing on each first block image corresponding to that block scale according to its corresponding contrast constraint to obtain a corresponding second block image; for each block scale, performing edge smoothing processing on each second block image corresponding to that block scale to obtain a corresponding third block image; and fusing all third block images corresponding to the N different block scales to obtain an enhanced image. This application solves the problem in related technologies where single local histogram equalization is difficult to simultaneously capture image details at different scales.

The application discloses a large-scale remote sensing image color correction method based on block local histogram matching, belongs to the technical field of remote sensing image processing, and aims at the problems of low calculation efficiency, neglect of local features and difficulty in maintaining overall color tone when existing methods process large-scale orthographic images. The correction is realized through five steps of adaptive image blocking, multi-scale statistical feature extraction, hierarchical nonlinear mapping model, high-precision seamless splicing and parallel computing framework optimization. The method can efficiently process massive data, takes into account local correction accuracy and overall color tone consistency, improves robustness through intelligent parameter adaptive adjustment, significantly eliminates image color differences, is suitable for multiple fields such as land resource investigation and city planning, and provides a high-quality solution for large-scale image splicing.

The application provides a computer vision-based structural vibration displacement monitoring method and system and a storage medium, and the method comprises the following steps: step 1, structural vibration video preprocessing; basic information of video data is automatically acquired by using a video basic information automatic acquisition algorithm, and an image gray scale and local histogram equalization processing method is used to simplify the image calculation complexity and enhance the image features; step 2, structural displacement monitoring based on improved FAST corner detection; an automatic threshold calculation method based on the maximum inter-class variance is introduced to form an improved FAST corner detection algorithm, the image features obtained by the improved FAST corner detection algorithm are combined with the pyramid Lucas-Kanade optical flow method, and a whole-process automatic image feature detection and motion tracking scheme is established. The method has the advantages that the overall monitoring process is optimized, and the method is non-contact, simple to operate and high in automation.

The application discloses a kind of depth-intensity joint optimization light weight SPAD neural network reconstruction methods, belong to single-photon imaging and depth learning technical field, including: based on wavelet transform to the original histogram data of SPAD array is carried out multistage wavelet decomposition, obtain compressed time-frequency domain data;Utilize multiscale superpixel to carry out non-maximum suppression, and obtain estimated depth map by voting mechanism;Local histogram is extracted from compressed time-frequency domain data, and sum along time dimension obtains intensity map;The estimated depth map is expanded, and expanded depth map is obtained;The double-branch complementary depth reconstruction network including depth branch, edge branch and perception interaction module is constructed;Real depth map and real edge gradient map are used to guide network, and training is carried out using joint loss function, to realize light weight SPAD neural network reconstruction.The method significantly improves the depth reconstruction accuracy under low illumination while greatly reducing the amount of calculation.

This invention relates to the fields of machine vision and wind power inspection technology, and discloses a method and system for identifying surface defects in wind turbine rotor cores. The method includes acquiring an original image of the rotor core surface and performing grayscale mapping to obtain a single-channel grayscale image; performing frequency domain periodic texture suppression and contrast-limited local histogram equalization on the single-channel grayscale image to obtain an enhanced feature image; constructing a background fitting model for the enhanced feature image and performing difference operations to obtain a background difference image; performing adaptive segmentation and texture direction-based morphological refinement based on the background difference image to obtain refined defect connected components; extracting local grayscale distribution and performing sub-pixel-level geometric feature calculations to obtain defect geometric attribute data; and performing spatial clustering and hierarchical evaluation based on the data to determine the final defect distribution map. This method can effectively suppress periodic texture interference and achieve sub-pixel-level accurate positioning and intelligent hierarchical classification of minute defects.