5 results about "Blind deconvolution" patented technology

The present application relates to the technical field of image processing, and more particularly, to a kind of aluminized film quality detection method and system based on image recognition, comprising: obtaining the fuzzy image of aluminized film, and the aluminized film is printed with Chinese and English characters;The fuzzy image is divided into multiple image blocks, and for each image block, based on the gradient feature of the image block, its fuzzy intensity index is calculated.The present application divides the image into blocks, and calculates the fuzzy intensity index and the fuzzy sensitivity degree for each image block respectively, and then generates a spatial variability guide weight by combining the two.The weight can intelligently identify the key areas in the image that are both severely blurred and contain complex characters, and guide the blind deconvolution algorithm to focus on accurately restoring these areas, effectively solving the defect that traditional methods are difficult to handle spatially varying blur, and significantly improving the restoration quality of the blurred image.

The present application relates to the technical field of unmanned aerial vehicle, especially to a kind of unmanned aerial vehicle aerial target detection repair method for optical distortion, comprising the following steps: obtaining unmanned aerial vehicle aerial video sequence, extract continuous frame image;Based on the statistical characteristics of thermal turbulence in a short time, construct space-time varying distortion model, distortion estimation is carried out to video sequence.The present application can effectively estimate and reverse optical distortion caused by thermal turbulence by constructing space-time varying distortion model and combining blind deconvolution technology, thereby significantly improving the definition and geometric consistency of unmanned aerial vehicle aerial image, this method makes full use of the time sequence information between continuous frames and turbulence statistical characteristics, realizes the accurate modeling of distortion field, and then restores the image with more detailed and clearer edges by inverse processing, provides high-quality input for subsequent target detection.

An image processing method for measuring object displacement is provided. The method includes acquiring a first sequence of images and a second sequence of images, two adjacent images of the first sequence of images including a first overlap portion, two adjacent images of the second sequence of images including a second overlap portion, the first sequence of images corresponding to a first three-dimensional (3D) surface including a curved surface on an object in a first state, the second sequence of images corresponding to a second 3D surface on the object in a second state. The method also includes deblurring the first sequence of images and the second sequence of images based on a blind deconvolution method to obtain sharp focal plane images, stitching the sharpened first sequence of images and the sharpened second sequence of images into a first 3D image and a second 3D image based on camera pose estimation performed by solving a perspective-n-point (PnP) problem using a refined robustly weighted Levenberg-Marquardt (RRWLM) algorithm. The method also includes forming a first two-dimensional (2D) image and a second 2D image by unwrapping the first 3D image and the second 3D image, respectively, and generating a displacement map image from the first 2D and second 2D images by performing a two-dimensional digital image correlation (DIC) method.

A profile generation method and device based on an intelligent agent for high-resolution processing of seismic data, comprising processing original seismic data through a blind deconvolution model to obtain an initial wavelet and an initial reflection coefficient; convolving the initial wavelet and the initial reflection coefficient to obtain an initial seismic profile; extracting initial profile features of the initial seismic profile; inputting the initial profile features, parameters of the initial wavelet, and parameters of the initial reflection coefficient into a pre-trained reinforcement learning intelligent agent for calculation; updating the parameters of the initial wavelet and the parameters of the initial reflection coefficient by the reinforcement learning intelligent agent to obtain parameters of a final wavelet and parameters of a final reflection coefficient; and convolving the final wavelet and the final reflection coefficient to obtain a target high-resolution seismic profile. The method of the present application uses a convolution model as the physical basis of the reinforcement learning environment, realizes reinforcement learning driven by a physical model, and obtains a seismic profile with higher resolution and more consistent with actual geological structure.

This disclosure relates to a method, apparatus, electronic device, and storage medium for reconstructing a single-frame astronomical image, comprising: determining a blind deconvolution optimization model corresponding to an initial single-frame astronomical image using a blind deconvolution algorithm based on a reweighted graph total variational prior; resampling the initial single-frame astronomical image to determine n skeleton images corresponding to the initial single-frame astronomical image; performing iterative optimization and intensity correction processing based on the blind deconvolution optimization model and the n skeleton images to determine a target blur kernel corresponding to the initial single-frame astronomical image; and deblurring the initial single-frame astronomical image based on the target blur kernel to determine a deblurred single-frame astronomical image. This disclosure can utilize a blind deconvolution algorithm based on an RGTV prior to iteratively optimize and correct the intensity of the initial single-frame astronomical image, obtaining a high-quality deblurred single-frame astronomical image with less computational resources and time required, thereby reducing image reconstruction costs and improving processing efficiency.