167 results about "Acutance" patented technology

The invention provides an intelligent control method for a floodlight illumination system, and the method comprises the steps: obtaining an environment surface roughness coefficient, spectral reflection distribution and spatial geometric change data, determining the wall surface material absorptivity and shielding region coordinates, and obtaining environment reflection characteristics and shielding distribution data; analyzing shielding boundary sharpness and shielding angle distribution according to the spatial geometric change and the coordinates of the shielding area, calculating a luminous flux distribution curve of a local dark area, and judging whether the influence range of the dark area exceeds a preset threshold range or not; calculating the illumination coverage integrity according to the updated light field distribution and polarization reflection characteristics, determining the boundary sharpness of a light beam, and obtaining a light field uniformity parameter; and according to the light field uniformity parameter and the shielding thermal radiation effect, calculating and optimizing light field parameters.

The invention relates to the technical field of image classification, in particular to a thyroid nodule ultrasonic image classification method and system based on feature extraction, and the method comprises the following steps: based on thyroid nodule ultrasonic image data, calling a pixel normalization function to adjust a gray scale range to a set interval, screening an isolated noise region, and carrying out the smooth filtering, and acquiring the ultrasonic image after smoothing filtering. According to the method, by adjusting the gray scale range and screening isolated noise areas, gray scale distribution is balanced, noise interference is reduced, image readability is improved, edge change is recognized by analyzing the gradient direction through a small-scale convolution kernel, texture features are extracted through a medium-scale filter, redundancy is reduced in combination with pooling operation, and the feature hierarchical expression ability is enhanced; the edge sharpness parameter is calculated, the feature weight is optimized, the edge sharpness and the classification adaptability are improved, the local feature segmentation precision is improved, the classification boundary is adjusted according to the similarity score, the classification loss measurement is optimized, the error offset is reduced, and the classification stability and accuracy are improved.

The invention relates to the technical field of centrifugal detection, and provides a centrifugal detection system which is characterized in that spectral data, image data and pressure data are acquired through a detection module, spectral features, layered edge sharpness and pressure change features are extracted, the features are fused through an attention mechanism to obtain centrifugal fusion features, and then an accurate centrifugal state score is analyzed; the control module combines the centrifugal state score and simulates the vibration amplitude and torque change in the centrifugal detection process to judge whether the centrifugal state is abnormal or not, the centrifugal state can be comprehensively and accurately evaluated in a multi-source data detection and multi-factor comprehensive judgment mode, the limitation of single data or single factor judgment is avoided, and the detection accuracy is improved. Potential problems of physical and biological levels in the centrifugal process can be timely and accurately found, and a reliable basis is provided for subsequent adjustment and control.

The invention relates to the technical field of industrial vision, in particular to an aviation part crack detection and repair method which comprises the following steps: acquiring an aviation part optical image at a reference time point and an aviation part optical image at a to-be-detected time point; according to the method, logarithmic polar coordinate transformation is carried out on different time point images, rotation, scaling and translation parameters are extracted, an image registration relation is established, the structural consistency of time sequence images in a local area is enhanced, a structural tensor is constructed for each pixel neighborhood, the change characteristics of the dual-time-phase tensor are compared, a structural change saliency map is generated, and the structural change saliency map is obtained. Sensitive capture of a tiny deformation area is achieved, the responsiveness to an initial crack is improved, then a crack propagation interval is further refined into a main crack path in a self-adaptive threshold segmentation and skeleton extraction mode, the tip acutance of the crack is calculated in combination with tip contour information of a geometric boundary of the crack, and the initial crack is obtained. And quantitative support is provided for the crack danger degree.

An automated resolution and sharpness calibration system for target scanning includes an imaging reference located on a scanner's scanning stage and an optical sensor configured to capture reference images before and after scanning a target. The system comprises a processor and a memory with instructions that enable the processor to receive the captured reference images, determine image metrics based on reference features, and detect deviations in these metrics between the images. The processor classifies the detected deviation to identify its cause and generates an alert signal accordingly. This system ensures precise calibration by analyzing deviations in image metrics, thereby maintaining optimal scanning performance and accuracy.

The invention relates to the technical field of image data processing, in particular to a water-soluble fertilizer dissolution rate visual measurement method and system, and the method comprises the steps: obtaining a sequence image of a dissolution process; performing multi-scale frequency domain analysis on the image, and calculating a scale convergence index by using the deviation between a local phase and a weighted average phase; a spectral sharpness factor is calculated according to the ratio of the high-frequency energy response to the low-frequency energy response; fusing the scale convergence index and the spectral sharpness factor to obtain a solid boundary probability; segmenting the particle region based on the probability and converting the particle region into physical mass, and differentiating a mass change sequence to obtain a dissolution rate. By fusing the phase consistency and the frequency domain energy distribution characteristics, the physical entity edge and the optical refraction halo are effectively distinguished, the refraction interference problem during high-concentration dissolution is solved, and the dissolution rate measurement accuracy is improved.

The invention relates to the technical field of deep learning model training, solves the technical problem that gradient pointing is inaccurate when model parameters are updated in a traditional SAM algorithm, and particularly relates to an image classification method based on sharpness perception minimization. A gradient direction correction mechanism is introduced for an image classification task, so that the stability in the optimization process and the generalization ability of the model on image recognition test data are remarkably improved. According to the method, the gradient disturbed by the SAM algorithm is corrected by using the feature vector, and the abnormal component of the gradient in the feature vector direction is effectively reduced, so that model parameter updating is prevented from pointing to a sharp region of a loss function. The correction of the optimized path significantly improves the accuracy of gradient updating in the training process of the image classification model, so that the model can learn more discriminative visual feature representation, and finally the classification accuracy and the generalization performance of the model on a test set are improved.

The invention discloses an image processing method for improving dazzle light and shadow environments, which comprises the following steps of: adjusting focus offset in real time by adopting a dynamic compensation algorithm, and judging the correctness of a current focusing direction and the suitability of a focusing amplitude by monitoring the change trend of a contrast value and the convergence state of a definition index in a focusing process; analyzing transition features of shadow and non-shadow areas through a shadow edge enhancement algorithm, and dynamically adjusting the direction and amplitude of focus transformation to obtain a real position locking result of a shadow edge; and for the improved edge sharpness distribution parameters, verifying the focus consistency of the whole image by adopting a feature matching algorithm, and obtaining an evaluation result of global focus quality by calculating the definition difference between different regions.

The invention relates to the technical field of face recognition, and discloses a face recognition system and a detection method thereof, and the recognition system is composed of a face image acquisition module, an environment perception and preprocessing module, a face detection feature extraction module, a comprehensive analysis and comparison module and a data storage monitoring module. According to the method, environmental factors such as illumination intensity, light source type and color temperature are detected through a sensor, corresponding preprocessing is carried out on the image, the influence of the environmental factors on the recognition precision can be reduced by correcting illumination unevenness, denoising and enhancing contrast and sharpness, meanwhile, image compensation and denoising are carried out, illumination unevenness is compensated, and denoising processing is carried out, so that the recognition precision is improved. The beneficial effects of comprehensive analysis of age, health conditions and illumination condition environmental factors through face recognition and more stable recognition precision are achieved.

The invention discloses an X-ray image artifact definition automatic correction and enhancement method based on deep learning, and belongs to the technical field of image artifact correction, and the method specifically comprises the steps: obtaining an X-ray image, employing frequency domain decomposition and edge detection to generate an artifact image and a sharpness image, and extracting an imaging parameter set and an anatomical region label; establishing a parameter-driven artifact migration network, inputting an imaging parameter set and an artifact graph, learning an artifact vector field and phase prior, and forming reversible representation of an artifact source; constructing a double-branch decoder, dissecting branches to generate a structural skeleton diagram, and performing cross attention fusion to obtain a candidate correction field; according to the candidate correction field, geometric remapping and sub-band compensation are carried out on the image to obtain an intermediate correction result image, and an artifact vector field is sent to a feedback loop; and the consistency of the sharpness graph and the structural skeleton graph is taken as a loss item, artifact residual constraint is combined, a correction field and decoder parameters are optimized, and non-anatomical textures are suppressed.

The disclosure deals with system and method for image gradient and derivative computation image processing. Noise, image sharpness, orientation, empirical parameters, and computational complexity are examples of image gradient and derivative computation challenges. Many traditional kernel-based operators excel at tackling one of these problems, but trade off their ability to handle others. Two new gradient detection kernels based on two-dimensional high order Taylor Series expansion tackle many such problems. The first kernel uses a wide range of the pixels in view to suppress noise, thereby improving the gradient intensities of edges. The second kernel builds on the first to leverage its noise suppression benefits while tackling an additional problem of degraded and low contrast edge boundaries. It can detect smooth lines in the presence of discontinuities and poor quality. The filter architecture allows for precise gradient calculation, edge detection, and orientation determination to less than one degree of the true value even when faced with signal to noise ratios that exceed 0.75.

The invention provides a sweet menu embryo pelleting seed breakage rate detection method based on machine vision, and relates to the technical field of seed detection.The method comprises the steps that firstly, visible light and near-infrared images of seeds under standard illumination and non-standard visible light images under multiple light sources are collected, and reflectivity calibration and polarization difference correction processing are carried out; features such as colors and textures are extracted based on visible light and near-infrared reflectivity images, and multispectral feature vectors are constructed; a surface normal vector is calculated by using a photometric stereoscopic vision technology and combining a plurality of non-standard visible light images, an initial three-dimensional height map is generated through integration, and a plurality of abnormal judgment features are extracted to mark a suspected damaged area. And further calculating an edge sharpness index, removing a mechanical damage area, and determining a final damage area and a three-dimensional contour thereof. And finally, combining the volume loss rate, the projection area ratio and other multi-dimensional features with the multispectral feature vector to construct a damage probability prediction model, thereby realizing accurate and efficient detection of the seed damage rate.

The invention discloses a moso bamboo age identification method and system based on an improved YOLO11 model, and belongs to the crossing field of artificial intelligence and forestry resource monitoring. Aiming at the problems in the prior art that bamboo age identification depends on artificial experience, image processing is easily interfered by illumination, and a general model is insufficient in cross-scale texture feature capture and the like, the invention provides the following innovations: 1) an anti-reflection-texture decoupling fusion framework is designed, reflection high-frequency noise is eliminated through a GhostConv module, longitudinal textures are enhanced in combination with a CBAM channel-space attention mechanism, and the cross-scale texture features are not sufficiently captured; a C2PSA multi-scale pyramid is used for fusing the features under different illumination conditions; 2) constructing a multi-granularity age sensitive detection head, and extracting bamboo joint spacing features in a cross-scale manner by adopting a cavity convolution combination (1 * 1 / 3 * 3 / 5 * 5) of a Basic RFB module; and 3) introducing lightweight collaborative design, compressing the volume of the model to 12.5 M (42% less than that of YOLOv5s) by using depth separable convolution (kernel size = 2) of a C3k2 module and a GhostConv channel compression technology, and keeping edge sharpness at the same time. Through verification of 2086 annotated images in three places, the age identification accuracy of the method reaches 89.5% and is improved by 3.8% compared with a base line, 120ms real-time detection of a mobile terminal is supported, and the method can be efficiently used for bamboo forest resource investigation and dynamic monitoring.

The invention discloses an atherosclerotic plaque risk assessment method and system based on artificial intelligence, relates to the technical field of medical image analysis, and provides the following scheme: synchronously acquiring medical image sequence and physiological signal time sequence data, generating a synchronization data set through time-space registration, and performing time-space registration on the synchronization data set; and inputting the synchronized data set into a pre-trained LSTM neural network, and outputting a dynamic artifact offset field which represents a displacement vector of a medical image pixel point along with a physiological signal phase. Gradient amplitude analysis is performed on the medical image sequence frame by frame to generate the gradient characteristic evolution graph, high, medium and low response areas are divided according to the dynamic gradient threshold interval, and the displacement correction vectors are configured for the areas, so that the problem of gradient response heterogeneity compensation deficiency in a conventional correction strategy is solved, adaptive deformation correction of an anatomical structure is realized, and the correction accuracy is improved. And low-gradient region structure dispersion and vascular wall edge sharpness attenuation caused by global compensation are inhibited.

The invention discloses a fingerprint verification method and device based on double-finger fingerprint image acquisition, and relates to the technical field of biological recognition, and the method comprises the following steps: deploying a light sensor, carrying out the primary adjustment of an illumination light source, collecting the light intensity data of the fingerprint surface when two fingers press through the light sensor, and obtaining a fingerprint verification result according to the light intensity data; the method comprises the following steps: deploying a light sensor, constructing a closed-loop adjustment mechanism of primary adjustment, secondary adjustment and quality feedback, acquiring light intensity data of the fingerprint surfaces of the two fingers in real time, judging an abnormal condition, performing secondary adjustment on an illumination light source, acquiring fingerprint images of the two fingers, and judging whether the quality of the fingerprint images reaches the standard or not. According to the two-finger fingerprint verification method, the abnormity of insufficient light source intensity or improper angle is pointedly corrected through secondary adjustment, and the image quality evaluation standard combining the edge sharpness and the effective area proportion provides reliable data support for two-finger fingerprint verification, and the fingerprint verification accuracy and the actual application effect are improved.

The invention discloses a near-infrared confocal microscopic imaging system and a near-infrared confocal microscopic imaging method, and relates to the technical field of optical microscopic imaging. The method comprises the following steps that near-infrared exciting light sequentially passes through a beam expanding system, a liquid lens and a confocal scanning light path to irradiate a to-be-detected sample so as to generate a near-infrared fluorescence signal, and the liquid lens is used for adaptively adjusting the focal length and wavefront morphology according to the depth of the to-be-detected sample; a photomultiplier detects the near-infrared fluorescence signal after non-focus scattered light is filtered out through the confocal scanning light path and the pinhole, and a fluorescence signal image is obtained; and according to the fluorescence signal image, carrying out iterative optimization on the focal length and phase modulation parameters of the liquid lens to maximize the image sharpness or the signal-to-noise ratio and realize the three-dimensional microscopic imaging of the deep tissue. According to the near-infrared confocal microscopic imaging method provided by the invention, deep near-infrared microscopic imaging with low phototoxicity, low cost and high resolution can be realized.

The invention relates to the technical field of image recognition, in particular to an automatic detection grabbing system of an earphone cover assembly line, which comprises an earphone cover image acquisition module for capturing an earphone cover image; according to the invention, by calculating the edge sharpness integral of the earphone cover image and extracting the contour coordinate sequence, the image contour positioning precision is improved, and the ambiguity problem of earphone cover boundary identification is solved; main harmonic morphological characteristics are obtained based on low-order descriptor coefficient energy distribution, the expression mode of the morphological characteristics of the earphone cover is optimized, the accuracy degree of matching of the form of the earphone cover and a standard template is further improved, and the appearance misjudgment probability is reduced; in addition, morphological opening operation is carried out by applying multi-scale structure elements, the structure scale entropy is calculated to reflect change characteristics of fine textures on the surface of the earphone cover, finer texture particle size distribution is established, the texture consistency deviation degree is measured and calculated, and the error of surface texture detection is reduced.

The invention provides an intelligent feedback control glass outer diameter screening method, which comprises the following steps: collecting the reflected light intensity and refracted light angle of the surface of glass, analyzing the spatial frequency distribution characteristics of local scattering intensity, identifying the surface microcrack distribution mode and quantifying the defect depth change; distinguishing real structure defects and surface tiny scratches, and obtaining defect boundary sharpness and defect size distribution parameters; based on the denoised signal data, dynamic evolution characteristics of defect distribution are extracted, and a space-time incidence matrix containing light intensity spatial frequency periodicity and angle deviation periodicity is constructed; carrying out weight redistribution on the space-time incidence matrix by utilizing a dynamic weight distribution coefficient, and carrying out weighted fusion on local scattering intensity information and refracted light phase difference information to generate a fused feature signal; based on the space-time characteristic parameters of the fused characteristic signals, the influence of sudden drop of reflected light intensity and signal distortion on outer diameter judgment is reduced to the maximum extent, and optimized outer diameter judgment signals are output.

The invention belongs to the technical field of image processing, and relates to an image video rain removal method based on local perception modeling and multi-stage span fusion, which adopts parallel branch networks with three level scales to respectively and sequentially carry out feature extraction, feature extraction and multi-stage span fusion on raining images with corresponding resolution sizes, performing high-level semantic information mining on the input raining image to obtain a high-dimensional feature map; high-resolution image reconstruction: adopting a local set perception implicit representation module, mapping the high-dimensional feature map to a continuous RGB color space through linear coordinate projection, and compensating continuous textures and edges sheltered by rain in the image to obtain a reconstructed image; and cross-scale feature fusion: carrying out information fusion between stages and scales on the image through double branches, and aligning and enhancing multi-scale semantics to obtain a high-resolution rain-removed image. According to the method, artifacts of a raining image can be suppressed in a multi-domain and multi-scale level, and sharpness and consistency are enhanced.

The invention relates to the technical field of digital image processing, in particular to a knitted label scanning image completion operation method, which comprises the following steps of: acquiring a knitted label image and converting the knitted label image to a frequency domain, locking a texture fundamental frequency based on energy distribution, executing protective denoising, repairing a texture phase flow field, extracting a character stroke skeleton in combination with gradient analysis, and completing the completion of the knitted label scanning image. According to the method, the image is converted to the frequency domain, the texture fundamental frequency energy is locked, the periodic structure characteristics of the knitted fabric are separated and protected, continuous solution is carried out on the phase field, smooth reconstruction of the warp and weft texture manifold is achieved, the phase dislocation defect caused by local pixel matching is eliminated, and the method has the advantages of being high in accuracy and high in reliability. A comprehensive steering vector field and a character stroke skeleton path are generated through orthogonal gradient synthesis, strong geometric constraint is introduced in the pixel filling process, it is ensured that yarn textures follow the original weaving trend after repairing, and character stroke coherence and edge sharpness are ensured.

The invention discloses an image processing method and system for a laser printer, relates to the technical field of image processing, and aims to eliminate detail loss and edge distortion caused by resolution difference by performing printer DPI matching on single-channel image data, and to analyze and perceive image local feature difference through local frequency so as to improve image quality. Adaptive processing strategies are determined for different areas, on the basis, binarization processing is carried out by adopting blue noise jitter and error diffusion technologies, high-frequency texture sharpness is reserved by utilizing the blue noise jitter and error diffusion technologies, and smooth transition of the low-frequency area is optimized by utilizing the error diffusion technologies. And finally, according to a local frequency analysis result, carrying out linear interpolation mixing on two binarization processing results so as to realize accurate combination of processing advantages of different regions. Thus, through multi-link cooperation, image detail reservation and artifact suppression are effectively balanced, and the output quality and adaptability of the laser printer in complex scenes such as images and texts and photos are improved.

The invention discloses an unmanned aerial vehicle-mounted through-wall SAR motion error inversion method based on image sharpness and a GCBP algorithm. In order to solve the problem that SAR images are seriously defocused due to airborne platform motion errors, a traditional self-focusing method is popularized to the field of unmanned aerial vehicle-mounted through-wall SAR, and an accurate unmanned aerial vehicle-mounted through-wall SAR signal model is constructed based on the refraction effect of a wall; motion errors are analyzed from geometric characteristics, frequency dependence characteristics, time-varying characteristics and space-varying characteristics, and an accurate phase error model is constructed by adopting sub-aperture division and sub-band division strategies in combination with a GCBP algorithm; and finally, based on an image sharpness criterion and a motion continuity constraint, realizing accurate estimation of the motion error of the unmanned aerial vehicle-mounted through-wall SAR by adopting a gradient descent method combined with a line search strategy. The actual measurement experiment verifies that the method has good refocusing effect and parameter inversion performance in the field of unmanned aerial vehicle-mounted through-wall SAR (Synthetic Aperture Radar).

The application provides an image classification training method based on AUC optimization sharpness perception minimization, and the technical scheme of the application adjusts the perturbation used in the sharpness perception minimization technology, maps the first gradient about the model parameter by using a preset scrambling hyperparameter, and determines the perturbation corresponding to the model parameter, so as to avoid the complex minimax-minimax optimization problem caused by the direct adaptation of the instance level form I-AUC and the sharpness perception minimization technology, effectively reduce the model training time, and reduce the energy consumption; at the same time, with the aid of the sharpness perception minimization technology, the generalization ability of the model is effectively improved.

A method includes capturing images of a surface of a workpiece using an optical sensor. Each image respectively images a region of the surface. Each image is assigned a defined 6-DOF pose of the optical sensor relative to the workpiece and a defined focal plane position of the optical sensor. The captured images form an image stack. The method includes determining a sharpness value for each picture element of each image of the image stack to generate a sharpness map for each image. The sharpness maps of the images form a sharpness map stack. The method includes transforming the sharpness maps of the sharpness map stack into a defined reference system based on the respective assigned 6-DOF pose and focal plane position of the optical sensor in order to generate a sharpness cloud. The method includes generating a surface profile of the workpiece based on the sharpness cloud.

The application discloses a kind of for promoting image processing method under glare, shadow environment, comprising: using dynamic compensation algorithm to focus point offset amount is adjusted in real time, by monitoring the change trend of contrast value and the convergence state of definition index in focusing process, the correctness of current focusing direction and the suitability of focusing amplitude are judged;The transition characteristics of shadow and non-shadow area are analyzed by shadow edge enhancement algorithm, the direction and amplitude of focus point transformation are dynamically adjusted, and the real position locking result of shadow edge is obtained;For the edge sharpness distribution parameter after promotion, the focus consistency of overall image is verified using feature matching algorithm, and the evaluation result of global focus quality is obtained by calculating the definition difference between different regions.

An information processing device includes an image transformation unit, a left-right difference estimation unit, and an image generation unit. The image transformation unit performs warping to move positions of a feature point of a right-eye image and a feature point of a left-eye image based on right-eye and left-eye viewpoint information. The left-right difference estimation unit estimates a portion where a difference exceeding an allowable level occurs, due to the warping, between the right-eye image and the left-eye image as an inconsistent portion. The image generation unit makes a sharpness of the inconsistent portion different between the right-eye image and the left-eye image.

The invention relates to a contrast-agent-free angiography generation system and method based on mask guidance, a parameter optimization device is used for performing parameter optimization on a conversion device by utilizing a loss function capable of highlighting a contrast agent shadow, and the capability of capturing complex image details of the conversion device is improved. Then, the Gaussian noise image is fused into a contrast-agent-free computed tomography image through a conversion device, computed tomography angiography is obtained through a fusion mode of feature sampling and gradual denoising, and it is guaranteed that the obtained computed tomography angiography has enough consistency and sense of reality; and the sharpness and fidelity of the generated vascular structure are improved.

The invention relates to a method (200) for imaging a scene with at least one camera module, the method comprising acquiring a plurality of images (IM1-IMn) of the scene at various discrete focusing distances, DFi, each with a respective depth of field, PCi={PCi,min;PCi,max}, comprising the respective focusing distance and extending over a respective sharpness range, NETi =PCi,max-PCi,min, the method furthermore comprising a step (202) of determining: - the smallest focusing distance (DF1) such that the lower limit (PC1,min) of the depth of field (PC1) associated therewith is less than or equal to a predetermined lower distance (Dmin); and / or - the greatest focusing distance (DF1) such that the upper limit (PCn,max) of the depth of field (PCn) associated therewith is greater than a predetermined upper distance (Dmax). The invention also relates to a computer program, an apparatus and a vehicle implementing such a method.

This invention discloses an automatic calibration method for airborne lidar placement deviation based on voxel sharpness. The method includes: selecting a natural calibration site containing building facades, linear markers, and point targets; collecting point cloud and POS data via a UAV flying in multiple headings; modeling the placement deviation to be calibrated using six parameters; optimizing the parameters using a genetic algorithm; using voxel sharpness as the fitness function; voxelizing the point cloud in the region of interest; counting the number of points within each voxel and calculating the sharpness score; a higher sharpness score indicates a more pronounced clustering of point clouds in the feature region; and iteratively optimizing the method through selection, crossover, and mutation operations to output the optimal placement deviation parameters. This invention uses an ordinary basketball court as a natural calibration site, eliminating the need for artificial targets. After calibration, the point cloud sharpness is improved by more than 150%, and the positioning accuracy reaches the centimeter level.

The invention relates to the technical field of microscopic imaging, and discloses a multi-level physical model and double-domain deep learning structured light illumination imaging reconstruction method, which comprises the following steps: firstly, constructing a multi-level physical degradation model based on illumination light field modulation, optical diffraction sensor discrete sampling and photoelectric noise, and generating a simulation data set containing random errors; then constructing a deep neural network containing spatial domain and frequency domain branches, and performing iterative training on the network by using a joint loss function; and finally, acquiring an original structured light stripe image of a real sample, inputting the original structured light stripe image into the trained model to execute end-to-end reasoning, and outputting a super-resolution fluorescent microscopic image. According to the method, the problem of obtaining real pairing training data is solved through physical simulation, high-frequency detail recovery is enhanced by utilizing frequency domain constraint, reconstruction artifacts are effectively removed, the imaging resolution is improved, and the spatial resolution and edge sharpness of a reconstructed image are improved while the ringing effect and background noise are effectively suppressed.