6 results about "Weighting filter" patented technology

The application discloses a low-noise tire pattern pitch optimization method and device based on image processing and a medium, and the method comprises the following steps: S1, processing initial pattern information and calculating pitch length; S2, extracting groove boundaries and tire mark boundaries of a pattern pitch graph; S3, inputting basic parameters such as pitch and mark; S4, introducing an A-weighting filter for acoustic weighting; S5, calculating an initial noise spectrum; S6, optimizing a spectrum peak value in a harmonic range; S7, iteratively adjusting a pitch ratio and quantity until noise energy meets a threshold value, and outputting an optimal pitch sequence. Through the fusion of image processing technology and acoustic analysis, the pattern pitch design is systematically optimized, tire noise is significantly reduced, design efficiency is improved, and a research and development cycle is shortened.

This invention provides a video signal decoding / encoding method and a bitstream transmission method. The video signal decoding method includes the following steps: determining an intra-frame prediction mode for a current block; obtaining reference samples based on neighboring samples of the current block; obtaining a first prediction sample for the current block based on the reference samples and the intra-frame prediction mode; obtaining a second prediction sample for the current block by applying weighted filtering to the reference samples and the first prediction sample; and obtaining a reconstructed sample for the current block based on the second prediction sample.

This invention relates to the field of image recognition technology, specifically a method for identifying transparent foreign fibers in cotton using polarization detection combined with image processing. The method includes the following steps: acquiring images using a polarization sensor and extracting temporal and angular information; calculating pixel phase delay; performing frequency domain analysis to determine the principal direction and generating a birefringence response; screening skeleton points based on grayscale and constructing fiber paths; combining phase gradients for extreme value analysis and intensity reconstruction to obtain global coordinates; and then weighted filtering based on directional differences to eliminate redundancy and obtain fiber position coordinates. In this invention, by combining image acquisition and feature expression paths, polarization response and texture information are fused to form multi-dimensional discriminative features, reducing noise and uneven illumination interference. Combining fiber continuity and optical response analysis enhances the ability to identify weak responses, making boundaries clearer and more stable. Furthermore, redundant points are compressed under directional classification and spatial constraints, improving positioning consistency and recognition accuracy, and achieving stable identification of transparent foreign fibers in complex scenarios.

This application provides an image enhancement method, electronic device, storage medium, and program product. The method includes: acquiring pixel difference data of a first chromaticity plane, a second chromaticity plane, and a luminance plane of an image to be processed, wherein the pixel difference data characterizes the difference between the pixel value of each pixel within a sliding window and the pixel value of the center pixel; the first chromaticity plane and the second chromaticity plane are obtained by performing resolution transformation processing on the first initial chromaticity plane and the second initial chromaticity plane respectively; determining filtering weights based on the pixel difference data, the filtering weights characterizing the contribution of the pixel value of each pixel within the current sliding window to the pixel value of the center pixel; performing weighted filtering processing on the luminance plane according to the filtering weights to obtain a target luminance plane; and determining a target image based on the target luminance plane, the first initial chromaticity plane, and the second initial chromaticity plane. This method can improve image quality during image conversion.

The application discloses a kind of dynamic residual threshold adaptive selection space-time filtering method, device, equipment and storage medium, the method includes obtaining the remote sensing time series image data of target area;By model training and predicting the possible change trend of remote sensing time series data obtains the prediction result of remote sensing time series data.Furthermore, the absolute residual between image predicted value and image true value is calculated, and the residual threshold determined based on the absolute residual median, quartile range, skewness and kurtosis is selected as the threshold of filtering method selection, specifically, for high residual area, spatiotemporal weighted filtering strategy combined with Euclidean distance is used;For low residual area, the filtering reconstruction method in time domain is used, so as to ensure the continuity of image while suppressing non-systematic noise.Compared with prior art, it has higher degree of automation and adaptability, and is especially suitable for remote sensing time series data processing in high-altitude, frequent cloud cover or complex terrain area.

The application provides a dual-mode end-to-end target detection method for unmanned aerial vehicles and complex scenes, and relates to the technical field of unmanned aerial vehicle target detection. In the input and feature extraction stage, a channel splitting operation is used to physically isolate the visible light and infrared data, and the data is transmitted to a parallel dual-flow convolutional network for independent multi-scale feature extraction. In the deep feature fusion stage, a cross-modal difference attention module is added. The spatial cosine similarity is calculated to generate a difference saliency mask, which helps the model to give high weights to the infrared features in areas with inconsistent feature structures. A semantic-guided spatial weighted filtering module is embedded in the neck network. The deep high-level semantic features are used to perform pixel-by-pixel weighted filtering on the shallow features, which can retain the edge details of small targets while greatly suppressing background clutter. At the same time, a scale adaptive weight mechanism is introduced during model optimization to increase the regression penalty for small targets.