12 results about "Camera response function" patented technology

The invention relates to a method for reducing motion blur of an erf model by using events and frames, and belongs to the technical field of computer vision. The method comprises the following steps: converting an event stream into triple representation; generating a bidirectional optical flow field based on the event flow, and aligning the fuzzy frame and the event flow through event-guided deformation convolution; constructing a double-branch optimization framework; the double-branch features are dynamically fused through frequency domain attention gating, and the weight of the frequency domain attention gating is generated by event frequency spectrums through MLP; generating a pseudo-true value based on an event double integral model, aligning an event spectrum and rendering a high-frequency component, and carrying out chain constraint on the continuity of a cross-frame radiation field through event brightness change; event-frame non-linear response differences are modeled by a learnable camera response function. Clear reconstruction and rendering of the moving target in a complex dynamic scene are realized.

The invention discloses a semi-supervised non-contact neonatal jaundice home intelligent early warning method based on image calibration, and the method comprises the steps: building a camera response function database, and building a color constancy depth model and a skin region segmentation network; designing a strong and weak enhancement strategy by using a large amount of label-free home data, constructing a time and context comparison learning module, and performing self-supervised training on a feature encoder; a small amount of labeled hospital data and a large amount of unlabeled family data are combined, and semi-supervised fine tuning is realized through pseudo label generation and supervised comparative learning; image sequences continuously uploaded by a user are input into the model, multi-day prediction of the bilirubin level is achieved, prediction uncertainty is quantified in combination with a Bayesian method, the risk boundary is dynamically adjusted, and personalized early warning is provided. According to the method, a user does not need to use a physical colorimetric card, color measurement errors caused by model differences of mobile equipment and variability of household illumination are inhibited from a data source, and the accuracy and robustness of subsequent jaundice assessment are improved; through a training framework combining self-supervised pre-training and semi-supervised fine tuning, the understanding ability and generalization performance of the model for the sequential characteristics of jaundice are improved.

The invention provides an image enhancement method and device based on a camera response function nonlinear information color block, relates to the technical field of image processing, and aims to perform multiple times of different degrees of exposure on a to-be-enhanced image, restore details and colors of an abnormal exposure area in the to-be-enhanced image, improve the robustness of CRF estimation in the to-be-enhanced image and improve the image enhancement effect. Particularly, the robustness of CRF estimation in the image to be enhanced under noise and abnormal exposure conditions is improved. Moreover, each exposure image is subjected to down-sampling operation, so that lossless reduction of each exposure image can be realized, and color blocks with richer color types and changes can be obtained. By automatically selecting the nonlinear information color blocks of the camera response function, manual intervention can be avoided, noise can be suppressed, and the efficiency and accuracy of color block selection can be improved.

The application discloses a kind of high-efficiency high dynamic image fusion methods, belong to single camera subarea high dynamic imaging and time-sharing multi-exposure high dynamic imaging field.First, a group of input image groups are acquired, and rearrangement is carried out, and calibration data is automatically selected from the reordered image group, and a fitting data set is constructed.Then, the coefficients of the camera response function are calibrated according to the fitting data set, and the camera response function is obtained, and based on the camera response function, the scene brightness is reconstructed in the scene irradiation domain using the neighborhood information of the semi-saturation region.Finally, the scene brightness is mapped to the [0,1] interval through tone mapping, and finally the fused image is obtained through the inverse transformation of the camera response function.The application can improve the accuracy of polynomial model calculation and is suitable for most practical application scenarios.

The invention discloses a cross-terminal hand back vein living body detection method based on an illumination response characteristic. The method comprises the following steps: S1, respectively collecting a multi-light-intensity vein image sequence of a target hand back and a to-be-detected hand back through a first terminal and a second terminal; s2, performing hand back contour extraction, direction adjustment and image normalization preprocessing on the two terminal images to ensure that the sizes and the orientations of the image contours are consistent; s3, extracting a hand back region of interest, and synchronously realizing region scaling and noise reduction by adopting mean filtering; s4 and S5 are combined with the camera response function and the inverse function of each terminal, illumination response offset values of pixel points in the region of interest are calculated, and a first offset feature map and a second offset feature map are generated respectively; and S6, selecting a matching template from the second offset feature map, performing sliding matching with the first offset feature map, calculating similarity through a Pearson's correlation coefficient, and comparing the similarity with a preset threshold to judge whether the to-be-detected hand back is a living body or not. According to the invention, cross-terminal hand back vein in-vivo detection with high adaptability and high accuracy is realized.

This invention discloses a sequential image fusion enhancement method based on three-axis slide table displacement sensing, belonging to the field of machine vision technology. The method includes the following steps: S1, constructing discretized state and output equations to complete the three-axis slide table motion modeling, and establishing the correlation between motion parameters and image blur kernel and pixel position offset; S2, using the inverse function of the camera response function combined with Taylor linearization, performing exposure normalization processing on multiple frames of observed images to obtain scene illumination estimates with a unified radiance scale; S3, obtaining multi-dimensional fusion weights for the image by setting a fusion weight calculation mechanism that correlates the quality features of the fused image with the slide table position; S4, completing the fusion of multiple frames of images through a weighted fusion model to obtain a high-quality single-frame fused image. Using the above method, the problems of image motion blur and uneven multi-frame imaging quality caused by three-axis slide table motion are effectively solved, achieving accurate fusion and deblurring enhancement of sequential images.

The invention discloses a degraded image target detection method based on uncertainty semantic modulation, and the method comprises the steps: generating a haze and dark light image data set according to an atmospheric scattering model and a simulation camera response function, training a proposed network, and carrying out the verification through employing a foggy day and dark light real data set. The network comprises a semantic modulation module SMM, a multi-scale feature modeling module MSFM, a cross-stage local module CSP and a YOLOX detector. The MSFM is based on wavelet transform and multi-scale space pooling, high-frequency details of the image are enhanced, and a global structure is stabilized. The SMM is based on an uncertainty theory, calculates a feature information entropy to construct an uncertainty map, guides a recovery branch to enhance a key target area, guides semantic features of a detection branch to enhance a target as spatial prior, and relieves feature conflicts between image recovery and target detection tasks. When the method is used for target detection, the target discrimination capability in a severe environment can be remarkably improved, and the omission ratio and the false detection ratio of the target are reduced.

The invention discloses a multi-exposure fusion angle resolution scattering micro-nano structure measurement method, and relates to the technical field of integrated circuit measurement. The method comprises the following steps: acquiring a multi-exposure sequence image set, and modeling a camera response function CRF; estimating an overexposed pixel value by adopting a linear extrapolation method based on a tangent line at the endpoint of a response curve, and calculating the confidence coefficient of an extrapolated pixel; when the confidence coefficient of the extrapolated pixel is lower than the basic confidence coefficient, enhancing the extrapolated pixel by using a segmented conservative estimation strategy; a correction image set and a corresponding confidence coefficient image set are obtained through iterative processing; carrying out weight distribution on the images in the corrected image set to obtain a corresponding normalized weight set; and performing multi-scale fusion according to the normalized weight set and the corrected image set to obtain a fused image. Pixel irradiance of an overexposure area is effectively reconstructed by performing pixel-level correction on an image, and high-fidelity fusion of Fourier space intensity is realized by performing multi-scale fusion on the image.

The application provides an image enhancement method and device based on a camera response function nonlinear information color block, relates to the technical field of image processing, and can restore details and colors of non-normal exposure areas in the image to be enhanced by multiple different degrees of exposure of the image to be enhanced, improve the robustness of CRF estimation in the image to be enhanced, especially the robustness of CRF estimation in the image to be enhanced under noise and non-normal exposure conditions. Moreover, the downsampling operation is respectively performed on each exposure image, lossless reduction of each exposure image can be realized, and color blocks with richer color types and changes are obtained. By automatically selecting the camera response function nonlinear information color block, manual intervention can be avoided, noise can be inhibited, and the efficiency and accuracy of color block selection can be improved.

The invention relates to the technical field of intelligent driving, in particular to a vehicle-mounted AI visual recognition terminal in an intelligent driving scene, which comprises a space-time alignment module, a physical illumination recovery module, a self-adaptive recognition module and a decision fusion module. The space-time alignment module realizes sampling time difference compensation and space remapping of camera images and millimeter wave radar point cloud data; the physical illumination recovery module adopts a physical parameterized illumination model fusing an atmospheric scattering equation and a camera response function, and reconstructs real details of an overexposure / underexposure region in combination with an illumination transmission equation and a Monte Carlo ray tracing algorithm; the adaptive recognition module calculates topology complexity according to the curvature change rate of the lane line and the spatial distribution density of the traffic signs, and dynamic loading and efficient recognition of a recognition model are achieved; and the decision fusion module rejects a false detection target in combination with space-time consistency verification. The method has high recognition precision and high decision reliability in complex illumination and high-dynamic traffic environments, and is suitable for an intelligent driving system.

The application discloses a kind of multi-exposure fusion's angle resolution scattering micro-nano structure measurement method, it is related to integrated circuit measurement technical field.The method includes: obtaining multi-exposure sequence image set, camera response function CRF is modeled;Overexposed pixel value is estimated using linear extrapolation method based on tangent line at response curve end point, and the confidence of extrapolated pixel is calculated;When the confidence of extrapolated pixel is lower than basic confidence, segmented conservative estimation strategy is used to enhance to extrapolated pixel;Corrected image set and corresponding confidence image set are obtained by iterative processing;Weight distribution is carried out to the image in corrected image set, and corresponding normalized weight set is obtained;According to normalized weight set and corrected image set, multi-scale fusion is carried out, and fusion image is obtained.Pixel level correction is carried out to image, and the pixel irradiance of overexposure area is effectively reconstructed, and multi-scale fusion is carried out to image, and high-fidelity fusion of Fourier space intensity is realized.

The present application belongs to the technical field of structured light three-dimensional measurement, and particularly relates to a structured light measurement camera exposure optimization and image quality evaluation method. First, the camera response function is solved for a pre-acquired image group to restore the true pixel value of the image group. In order to achieve a greater dynamic range under single exposure, the ideal pixel mean value is set, the optimal exposure time is solved through linear fitting of the corrected pixel value and the exposure time, and the optimal exposure time is used to collect the image of the measured object for quality evaluation. The image overexposure and underexposure evaluation parameters p1 and the image quality evaluation parameters p2 are calculated to obtain the image quality evaluation result P. In order to verify the effectiveness of the method, the experimental results show that when the measured object image is collected under different exposure times for model reconstruction, the number of matched points of the image collected under the optimal exposure time is obviously higher than that under other exposure times, and the key feature measurement result is accurate.