178 results about "Image noise reduction" patented technology

The invention discloses a fishing boat name identification method and system based on image processing and deep learning. The method comprises the following steps: S1, fishing boat image acquisition; s2, image preprocessing and enhancement are carried out to realize image noise reduction, deblurring and brightness adjustment processing, and image quality judgment is carried out after processing to form closed-loop optimization; s3, self-adaptive image enhancement is carried out to realize illumination compensation and image dynamic enhancement; s4, ship name area detection and segmentation; s5, ship name region feature extraction and optimization; s6, feedback and optimization of an identification result; s7, character recognition and post-processing; s8, performing correction and verification; s9, log identification and report generation; and S10, displaying and storing a result. According to the invention, the fishing boat name identification precision can be effectively improved.

The invention discloses a bearing defect detection method and system based on machine vision and ultrasonic detection, and particularly relates to the technical field of industrial automatic detection, and the method comprises the steps: S1, a synchronous collection module: carrying out pulse triggering synchronous collection, and generating a time-space reference table; s2, a feature extraction module: performing image noise reduction segmentation and ultrasonic frequency domain decomposition, and outputting a defect feature vector; s3, a fusion identification module: performing cross-modal feature alignment fusion to generate a defect classification conclusion; s4, a size measurement module: performing contour fitting to calculate inner and outer diameters, and outputting a size deviation value; and S5, a comprehensive judgment module: carrying out threshold comparison logic judgment, and generating a multi-modal detection report. According to the method, a space-time reference is established through an encoder, images are segmented in a self-adaptive mode, features are extracted through wavelet decomposition ultrasound, feature weights are re-calibrated through a parallel network and an attention mechanism, composite defects are recognized through cross-modal fusion, comprehensive judgment is conducted in combination with dimensional deviation, and a multi-dimensional quality evaluation system is achieved.

The invention discloses a photovoltaic module infrared image fault detection method based on unmanned aerial vehicle inspection, which relates to the field of fault detection, and comprises the following steps: configuring an unmanned aerial vehicle platform, planning a flight path, setting aerial photography parameters, setting an infrared thermal image acquisition and infrared image data return and storage mechanism, and performing gray normalization processing, image noise reduction, histogram equalization, edge enhancement processing and size standardization processing on the infrared image data, and performing data enhancement operation. An unmanned aerial vehicle infrared inspection technology is combined with a deep learning target detection model, a set of complete photovoltaic module infrared image fault detection process is established, and full-process automatic processing from image acquisition, image preprocessing, model detection to result evaluation and visualization can be realized. The method has the comprehensive advantages of being high in fault recognition precision, high in detection speed, standardized in processing flow and the like, and the efficiency and the intelligent level of photovoltaic power station component-level fault inspection are remarkably improved.

The invention discloses a yarn weak twist and hairiness intelligent detection system based on visual stroboscopic synchronization, particularly relates to the field of quality control in the textile industry, is used for solving the problem of accurate detection of weak twist and hairiness defects in the yarn production process, and dynamically adjusts the flash frequency of a stroboscope according to the real-time speed and acceleration data of yarn, so that the detection accuracy is improved. The light source and the yarn are ensured to move synchronously, and a high-resolution image is obtained. In combination with yarn motion spectrum analysis and image quality evaluation, a complexity score and a matching degree score are generated and are used for dynamically regulating and controlling image noise reduction and edge detection parameters, and characteristic extraction requirements under different yarn motion conditions are met. The weak twist characteristic and the hairiness texture of the yarn are accurately extracted through a multi-scale edge detection and wavelet transform algorithm, a twist distribution model is constructed, the boundary range is corrected in combination with hairiness distribution data, characteristic interference is reduced, and the detection precision and reliability are improved.

The invention relates to the technical field of artificial intelligence, is suitable for financial and medical scenes, and particularly relates to a low-illumination physical examination image noise reduction enhancement method, device and equipment and a storage medium, the low-illumination physical examination image noise reduction enhancement method comprises the following steps: obtaining a to-be-processed low-illumination physical examination image; based on a Retinex decomposition model, acquiring a preliminarily estimated illumination component of the low-illumination physical examination image, acquiring an accurately estimated illumination image according to the preliminarily estimated illumination component, and enhancing the accurately estimated illumination image through gamma transformation to obtain an enhanced image; performing noise reduction on the enhanced image based on a BM3D algorithm to obtain a noise-reduced image; and combining the enhanced image and the noise reduction image to obtain a noise reduction enhanced image. The problem that in the prior art, when enhancement processing is carried out on the low-illumination physical examination image through an image enhancement algorithm, synchronous processing is not carried out on noise and amplified noise in the enhanced physical examination image is solved, and the accuracy and efficiency of physical examination image recognition are greatly improved.

The invention relates to a system and a method for making a colored low-light image. The system for making the color low-light-level image comprises a high-sensitivity CMOS imaging module which is used for enhancing the photon collection capability and reducing the noise level to complete imaging; the image enhancement processing module adopts a histogram equalization algorithm and comprises the steps of calculating a gray histogram of the original image, calculating a cumulative distribution function according to the gray histogram and performing gray mapping to obtain an enhanced gray value; the image noise reduction processing module is used for carrying out noise reduction on the image after enhancement processing, and filtering is carried out by adopting a night image noise reduction technology, a bilateral filtering method including spatial domain noise reduction and a band-pass filter including frequency domain noise reduction; and the colorful low-light-level image generation module obtains a colorful low-light-level night vision image by adopting an interpolation algorithm and makes a colorful low-light-level image. According to the system and the method for manufacturing the colored low-light-level image, special equipment is not depended on, the imaging quality of the colored low-light-level image can be improved, and the colored low-light-level image can be manufactured.

The invention belongs to the technical field of discrete element simulation, and particularly relates to a digital twinborn discrete element model construction method based on CT (Computed Tomography) scanning, which comprises the following three steps of: in a CT scanning stage, acquiring a high-resolution cross-sectional image by optimizing scanning parameters, and carrying out image noise reduction and feature enhancement processing; in the structural analysis stage, a segmentation model is constructed based on a deep convolutional neural network, and automatic annotation of a scanned image and intelligent prediction of a three-dimensional space topological relation are realized; in the discrete element modeling stage, the reconstructed three-dimensional grid model is converted into a discrete element model with real physical attributes through parameter calibration, and discrete element simulation is achieved. According to the method, the non-destructive detection advantage of industrial CT and the feature extraction capacity of deep learning are combined, microstructure characterization is converted into macroscopic performance prediction, and a reliable simulation analysis method is provided for performance optimization of the composite material.

The invention discloses an integrated circuit scanning electron microscope image noise reduction method, which can avoid photoresist shrinkage and make image imaging clearer compared with the traditional multi-frame superposition technology in the industry. The method specifically comprises the following steps: acquiring a wafer with consistent key feature size on a semiconductor manufacturing production line; obtaining SEM images of the target point in different core particles; searching one image from the SEM images as a reference image, and taking the rest images as to-be-compared images; acquiring all overlapped regions between the reference image and the to-be-compared image; and calculating the maximum intersection area of all the overlapped areas, and carrying out pixel-level averaging on the maximum intersection area to obtain a noise-reduced SEM image. According to the method, the noise of the SEM image can be reduced, the image quality is improved, and a reliable data source is provided for product quality inspection and photoetching model driving in the semiconductor manufacturing process.

The invention relates to the technical field of image analysis, in particular to a data visualization pathological diagram analysis system which comprises a pathological image noise reduction and enhancement module, a lesion area judgment module, a local texture and global structure fusion module, a self-adaptive multi-scale segmentation module and an error correction and optimization module. According to the method, by analyzing color channels, cell structure edge features and background noise distribution in the pathological image, accurately screening noise and optimizing image filtering, the image quality is effectively enhanced, the noise is reduced, the recognition accuracy of a lesion area is improved, and the recognition accuracy of the lesion area is improved in combination with cell nucleus gradient information and tissue edge distribution. The accuracy is further improved by using gray statistics and cell density, region division is weighted and optimized through the contrast and entropy of lesion tissues, the segmentation accuracy is ensured, the image segmentation scale is accurately adjusted in combination with the cell density and color gradient information, errors are reduced, accurate segmentation of lesion regions is ensured, and the accuracy of image segmentation is improved. And finally, the precision and reliability of overall image analysis are improved.

The invention provides a radar image noise reduction processing method based on deep learning, and belongs to the technical field of machine learning, and the method comprises the steps: obtaining historical echo signal data, and determining a historical radar image data set based on the historical echo signal data; determining a training image set and a test image set based on the historical radar image data set, and constructing a noise reduction neural network model based on the training image set; evaluating the noise reduction neural network model based on the test image set, and performing second optimization on the noise reduction neural network model; and performing noise reduction processing on a real-time radar image data set determined by the real-time radar echo data based on the optimized noise reduction neural network model. The noise reduction learning ability of the model can be enhanced, the processing ability of the model to a complex noise environment can be improved, gradual fine adjustment of the model can be realized to form a performance closed loop, high-precision rapid processing of real-time radar data can be realized, the denoising effect of a radar image can be improved, and the noise reduction precision in the complex environment can be improved.

The invention relates to the technical field of image noise reduction and enhancement, and discloses a CT image noise reduction and enhancement system and method which are used for providing a data interface module of a data transmission channel between the system and imaging equipment. The original data preprocessing module is used for preliminarily correcting the collected original projection data; the noise simulation module is used for establishing a noise model to simulate noise distribution in actual work according to physical characteristics and an imaging principle of the X-ray detector; the noise suppression simulation module is used for reducing noise based on a noise suppression algorithm of a physical model; the deep learning fusion module is used for optimizing the system in combination with a physical model noise reduction method and a deep learning technology; the image protection module is used for particularly paying attention to protection of important anatomical structures in the noise reduction process and avoiding loss of key diagnosis information due to excessive noise reduction; and the post-processing enhancement module is used for improving the image quality after the image processing is completed and ensuring the clinical readability of the image.

The invention provides a gamma imaging noise reduction detector based on depth information, which comprises a layered probe, a host and an acquisition circuit, and is characterized in that the layered probe is of a multi-layer structure formed by arranging single-layer position sensitive probes front and back; the acquisition circuit acquires timestamps, positions and amplitude information of the electric pulse signals; the host is responsible for collecting the number of the collection circuit and generating a detector image, forming the same particle event by using the electric pulse signals with the same timestamp, and determining thickness direction information of activated pixel data corresponding to each electric pulse signal through the number of the collection circuit. And only the data of the foremost activation pixel in the thickness direction is accumulated into the detector image. According to the method, a thick detector is divided into layered probes to obtain depth information of particle energy deposition, so that the noise influence caused by scattering in a detector crystal in medium-high energy gamma ray imaging is reduced, a first scattering point is speculated to eliminate secondary scattering noise of medium-high energy gamma rays, and the contradiction between the imaging speed and the imaging quality is solved.

The invention discloses a cement homogeneity detection method based on image processing. The method comprises the following steps: S1, image preprocessing: converting an original image into a grayscale image; s2, image noise reduction: salt and pepper noise is eliminated through a filtering technology, and the influence of the salt and pepper noise can be effectively eliminated; s3, image enhancement: carrying out sharpening and detail enhancement on the grayscale image by adopting a Laplace enhancement algorithm; s4, carrying out threshold segmentation: carrying out threshold segmentation through gray difference, and identifying an interested part which is not uniformly stirred; s5, watershed segmentation of the adhesion part: identifying a plurality of local maximum gray value points by adopting a watershed segmentation method based on extended maximum transformation, and combining the local maximum gray value points into a unique maximum value point to realize accurate segmentation of the adhesion area; and S6, homogeneity judgment. According to the invention, through an image processing algorithm, the number, size, distribution and other characteristics of the granular cement blocks are accurately analyzed, so that whether the cement is uniformly stirred is judged.

The invention provides an unsupervised low-dose photon counting CT (Computed Tomography) reconstruction method and an unsupervised low-dose photon counting CT reconstruction device based on optimal transmission. According to the method, a low-dose photon counting CT reconstruction problem is modeled as a Cantrovitch problem under a distribution consistency constraint, and the method comprises the following steps: constructing and training an optimal transmission-based unsupervised projection recovery network and constructing and training an optimal transmission-based unsupervised image noise reduction network; performing detail recovery on the photon counting CT low-dose projection by using the trained unsupervised projection recovery network to generate a denoised projection; reconstructing the denoised projection by using an image reconstruction algorithm to generate an intermediate image; and performing noise reduction on the intermediate image by using the trained unsupervised image noise reduction network to obtain a final de-noised image. According to the method, the problem that a high-quality pairing data set is difficult to obtain and a photon counting CT low-dose image is difficult to reconstruct can be solved.

The invention relates to the technical field of image recognition anti-counterfeiting, in particular to an authenticity identification method for analyzing micro-stereoscopic structure distribution on the surface of a product by using a mobile phone deep learning model. Comprising the following steps: image acquisition; image preprocessing: performing image noise reduction and region segmentation operation on the acquired image, extracting the edge of the micro-stereo structure through a Canny edge detection algorithm, performing fitting to generate a closed contour, and positioning an effective detection region of the micro-stereo structure in combination with contrast enhancement processing; extracting features; performing feature comparison; and outputting a result. According to the method, the limitation of the traditional one-dimensional or two-dimensional anti-counterfeiting feature is broken through by extracting the multi-dimensional features such as the height difference, the spatial arrangement and the geometrical shape of the micro three-dimensional structure on the surface of the product. The features are uniquely combined by depending on the three-dimensional distribution characteristic of the micro-stereo structure, and the complexity of the features enables high-precision equipment to be difficult to accurately imitate and copy, so that the anti-counterfeiting reliability is improved.

The invention discloses an image adaptive enhancement method and system based on deep learning, and relates to the field of remote control. The stamping plate image acquisition module is used for acquiring an image of a to-be-detected stamping plate of a production line through an industrial camera; the deep learning-based image adaptive enhancement method and system comprises the following steps: collecting an image of a stamped plate: storing and saving the image of the stamped plate through an industrial camera by the system; according to the step of image processing, due to the fact that noise and uneven illumination exist in collected stamping plate images, image noise reduction and filtering algorithm processing are carried out on the images, and stamping plate surface defect detection is mainly carried out on the input stamping plate images through a defect classification algorithm. And information about whether the surface of the stamped plate has defects and defect positions is stored.

The invention provides a method and a system for removing calcification artifacts of a CT (Computed Tomography) image. The method comprises the following steps of: preprocessing the CT image; semantic feature information extraction is carried out on the preprocessed CT image; fusing the preprocessed CT image and the semantic feature information, and inputting the fused image and semantic feature information into a GAN-based artifact removal model to eliminate calcification artifacts so as to realize noise reduction; an improved perception extraction module is introduced into a generator, and a global / local-semantic judgment module is added into a discriminator, so that the image noise reduction effect after the calcification artifacts are removed is further improved. By means of the GAN-based artifact removal model, common noise reduction can be effectively achieved, meanwhile, calcification artifacts in the CT image are removed, so that the quality of the CT image is improved, the judgment accuracy of a doctor for the blood vessel stenosis degree is further improved, and then the misdiagnosis rate of CT diseases is reduced.

The invention provides an image noise reduction enhancement method based on an additive Gaussian diffusion model, which comprises the following steps: selecting a plurality of clear images, gradually adding additive Gaussian white noise to the clear images until the images are completely polluted by the noise, and generating a series of noise images with different pollution measures; forming a training data set by the noise images with different pollution measures, the corresponding time steps and the global noise samples obeying standard normal distribution, and training a U-Net network; establishing an additive Gaussian diffusion model for reversely deriving and estimating a noise image at the previous moment; collecting a noise image to be denoised, obtaining an initial value of the noise level of the noise image, and calculating a time step number corresponding to the initial value of the noise level through a time coding function; and inputting a polluted image to be subjected to noise reduction and the corresponding time step number into the trained U-Net network, connecting a vertical additive Gaussian diffusion model in parallel, and removing the noise of the noise image by adopting a loop iteration optimization strategy through back diffusion calculation.

The invention relates to a low-dose SPECT image noise reduction method and system based on an unsupervised strategy and a generalized diffusion model. The method comprises the following steps: carrying out slicing processing on a low-dose SPECT image to obtain a training image; in a forward propagation stage of the unsupervised diffusion training model, determining an attenuation coefficient degradation operator according to the body mass index of the patient, and carrying out image noise addition to obtain a random Poisson noise image; in a back propagation stage of the unsupervised diffusion training model, extracting optimization factors and time step length features, and performing denoising processing on the random Poisson noise image to obtain a predicted low-dose SPECT image; and loading the optimal weight into a back diffusion inference model, carrying out back diffusion by taking the predicted low-dose SPECT image as a diffusion end point, and carrying out noise reduction processing on the predicted low-dose SPECT image to obtain a predicted full-dose SPECT image. The data preparation cost can be reduced, artifacts are reduced, and the denoising effect is improved.

The invention relates to the technical field of metal detection, and discloses a polarization imaging metal surface scratch identification system. The system comprises a polarization imaging unit, an image preprocessing unit, a polarization feature extraction unit, a scratch recognition unit and a parameter self-adaption unit. The polarization imaging unit collects and transmits a metal surface polarization image; the image preprocessing unit performs noise reduction and enhancement on the image to generate an optimized image; a polarization feature extraction unit analyzes polarization state parameters of the optimized image to obtain polarization degree and azimuth angle distribution, and extracts polarization features of the scratch candidate area; the scratch identification unit compares the polarization characteristics with a preset template to generate an identification result; and the parameter adaptive unit dynamically adjusts the acquisition parameters of the polarization imaging unit according to the definition and contrast ratio of the scratch in the recognition result. According to the system, the polarization imaging technology is utilized, multi-unit cooperative work is combined, metal surface scratches can be accurately recognized in a complex environment, the system adapts to different detection conditions, and the recognition effect is improved.

The invention relates to the technical field of unmanned aerial vehicle image processing, and discloses an unmanned aerial vehicle image noise reduction and color enhancement optimization method and system. The method comprises the following steps: firstly, acquiring an original image sequence acquired in the flight process of the unmanned aerial vehicle, and identifying corresponding environment illumination intensity change data and motion fuzzy feature distribution; time dimension features of the original image sequence are divided based on environment illumination intensity change data, and a noise feature distribution map is constructed in combination with motion blur feature distribution; and finally, calling an equipment imaging parameter set of the original image sequence, analyzing a corresponding imaging mode constraint condition, and formulating a multi-stage noise reduction strategy framework for the original image sequence according to the constraint condition and the noise feature distribution map. The method realizes effective noise reduction and color enhancement of the unmanned aerial vehicle image by comprehensively considering the environment illumination change, the motion blur feature and the equipment imaging parameter, improves the image quality, and is suitable for the field of unmanned aerial vehicle image processing.

The invention provides a satellite-borne synthetic aperture radar signal processing method based on deep learning, and relates to the technical field of radar signal processing, and the method comprises the steps: collecting echo data of a satellite-borne synthetic aperture radar, carrying out the imaging processing of the echo data based on a constructed RATIR-Net network structure, obtaining a satellite-borne synthetic aperture radar image, and carrying out the processing of the satellite-borne synthetic aperture radar. And carrying out noise reduction processing on the obtained spaceborne synthetic aperture radar image by adopting a spaceborne synthetic aperture radar image noise reduction algorithm based on deep residual learning. According to the method, the imaging quality and the anti-interference performance are remarkably improved, and powerful technical support is provided for application of the satellite-borne SAR in the fields of disaster monitoring and the like.

The invention discloses pavement crack detection equipment based on double spectrums, and the equipment comprises an equipment design module which comprises a camera module, a supporting structure, an inclination angle monitoring module, an image processing unit and a mobile device; the dual-spectrum fusion detection module comprises a thermal diffusion enhancement dual-spectrum multi-scale feature fusion image preprocessing module, noise reduction and multi-scale feature fusion are carried out on images collected by the infrared thermal imaging camera and the visible light camera, and a detection result and category and position information are output through an unsupervised enhancement BiSeNet hybrid wavelet transform network to complete detection; and the pavement quality evaluation module is used for realizing pavement crack detection through image splicing and BiSeNet network segmentation, realizing pavement quality evaluation through crack distribution diagram generation and area proportion calculation, and realizing detection result display through visual labeling. According to the invention, the detection efficiency can be greatly improved, the maintenance cost can be reduced, and powerful support can be provided for timely maintenance of the road.

The invention provides an image noise reduction model compression method based on structure re-parameterization, and the method comprises the steps: S1, constructing a dark light image data set, and carrying out the image preprocessing; s2, loading to a multi-branch noise reduction network; s3, training the model by adopting an MAE loss function and an Adam optimizer; s4, performing structure re-parameterization on the trained multi-branch model; and S5, loading the complete image into the model after the re-parameterization. According to the method, through a structure re-parameterization technology, a large model can be constructed for training, and the large model is converted into a small model suitable for edge equipment and an embedded system during reasoning, so that resource limitation and real-time reasoning requirements are met.

The invention discloses a video analysis-based early warning and monitoring method for children hyperactivity behavior and a storage medium, and the method comprises the steps: firstly, guaranteeing the comprehensive monitoring of children behaviors through employing a multi-channel video collection device, and providing a sufficient continuous data source for subsequent analysis; furthermore, the quality of monitoring data is ensured through the use of image noise reduction and segmentation technologies, and accurate motion features are effectively extracted through the combination of an optical flow algorithm and a background subtraction algorithm; further, the movement behaviors of the children are comprehensively analyzed by calculating behavior parameters such as movement amplitude, movement direction change and continuous movement time, and potential behavior abnormity is revealed; the multi-dimensional behavior feature vector obtained through behavior parameter analysis is compared with the standard template, the behavior type of the child is automatically recognized, early-stage symptoms of abnormal behaviors such as hyperactivity and the like are found in time, and a basis for early diagnosis and intervention is provided for behavior problems such as hyperactivity and the like of the child.

An apparatus for detecting and identifying foreign matter in a shallow stratum on the basis of distributed acoustic wave sensing. The apparatus comprises: a vibration-sensitive optical cable (1), a distributed acoustic wave sensing demodulator (2), a seismic source system (3), a vibration data processing unit (4), a velocity structure inversion unit (5) and an artificial intelligence positioning and identification unit (6), wherein when performing detection and identification, the distributed acoustic wave sensing demodulator (2) transmits a vibration signal to the vibration data processing unit (4) for preprocessing, so as to acquire a waveform signal; the velocity structure inversion unit (5) inverts a shallow stratum underground velocity structure on the basis of the waveform signal, so as to acquire the shallow stratum underground velocity structure; the location of foreign matter in a shallow stratum is identified by means of the underground velocity structure and an abnormal waveform; and the type of the foreign matter in the shallow stratum is identified by means of the artificial intelligence positioning and identification unit (6), image noise reduction and object segmentation. In the present application, a vibration signal is acquired by using a distributed optical cable acoustic wave sensing method, velocity imaging is performed on a shallow stratum velocity structure, and the positioning of shallow stratum underground foreign matter and the identification of the type of the foreign matter are realized by utilizing artificial intelligence. The present application further relates to a method for detecting and identifying foreign matter in a shallow stratum on the basis of distributed acoustic wave sensing.

The invention, which relates to the field of power transmission detection, discloses a visualization-based power transmission detection system comprising an image acquisition module, a preprocessing module, a feature extraction module, a defect identification module and an alarm module. A high-definition camera is used for collecting images, a path is planned according to an inspection task during preparation, equipment is debugged, shake is prevented in the process, and a continuous shooting mode is selected; noise reduction is carried out on the image, the image is enhanced after being processed through a wavelet transform algorithm, and the image is standardized into a specific resolution ratio and a specific pixel value through self-adaptive histogram equalization operation; extracting multi-scale features based on a deep learning model, extracting frequency domain features during defect identification, fusing the frequency domain features into vectors, inputting the vectors into a classifier, and optimizing through a feature pyramid network; and finally, sound-light alarm is generated when defects are detected, information is uploaded to a remote monitoring center, geographic information system linkage and local storage are supported, data distribution and decision support are ensured to be timely and accurate, and power transmission equipment is effectively maintained.

A deep learning segmentation method for asphalt mixture CT images relates to the technical field of aggregate image segmentation. CT images of asphalt mixtures are collected and voids are removed. Image equalization is performed using a gamma beam hardening correction algorithm. A filter window is selected, and an adaptive bilateral filtering denoising algorithm based on local image information is used to denoise the image. The U-Net model is improved, with the Inception convolution module replacing the standard convolution operation and residual connections replacing skip-layer connections. A spatial attention mechanism is introduced, and a joint loss function is used. Image samples are selected, aggregates are labeled, and data augmentation is used to increase the sample size to form a training set for model training. After training, this set is used to segment other images. By equalizing and denoising CT images and using the improved U-Net model, the method can achieve accurate segmentation of aggregate-mortar boundary information, avoiding aggregate adhesion issues.

The invention belongs to the field of financial data processing, and discloses a system for automatically processing financial information by using an image recognition technology, which comprises a graying unit, a noise reduction unit and a binarization unit, the graying unit is used for carrying out graying processing on the image of the financial bill to obtain a first image; the noise reduction unit is used for carrying out noise reduction on the first image to obtain a second image; the binarization unit is used for carrying out binarization processing on the second image to obtain a binarized image, and comprises the following steps: respectively calculating a binarization threshold value of each pixel point in the second image; and determining a gray value of the pixel point in the second image in the binarization image based on the binarization threshold. According to the method, the calculated binarization threshold value can be suitable for financial bills with different paper colors, the occurrence probability of incomplete characters in the binarization image caused by uneven illumination can be reduced, the images of the financial bills can be identified more accurately, and the accuracy of the identified financial information is improved.

The invention discloses a radiation hot spot noise removal method and device based on a dual-CMOS image sensor, and belongs to the technical field of CMOS image sensor noise reduction, and the method comprises the steps: carrying out the calibration of a dual-camera system, and eliminating a mechanical assembly error; the imaging focal planes of the double cameras are ensured to be consistent through definition evaluation; pixel-level geometric alignment is realized by using Fourier-Mellin transform, so that an imaging error is smaller than a specific threshold value; adaptive weighted fusion is performed based on local features, and rigid transition of simple binary selection is avoided. According to the method, image details can be effectively reserved while the hot spot noise is removed, particularly, when an image containing English letters is processed, the contour definition and the recognizability are remarkably superior to those of a traditional algorithm, and an efficient solution is provided for image noise reduction in the intense radiation environment.