88 results about "Global illumination" patented technology

The invention discloses a complex material-oriented global full-frequency illumination neural rendering method and system, and belongs to the technical field of computer graphics, and the method comprises the steps: querying and extracting a primary frequency feature and a secondary frequency feature in a neural feature field according to the primary geometric buffer and the reflection geometric buffer of a rendering pixel; performing weighted combination on the primary frequency features and the secondary frequency features through a double-frequency fusion module to form full-frequency fusion features; inputting the full-frequency fusion feature and the initial geometric buffer into a decoder to obtain a pixel radiation brightness value; and optimizing the network by adopting a double-stage training strategy of low-frequency data and multi-frequency data, and then using the optimized network for neural drawing of global full-frequency illumination. The method can improve the accuracy and stability of global illumination simulation in a complex dynamic scene, and is suitable for modeling a multi-frequency global illumination effect in a dynamic three-dimensional scene.

The invention provides an illumination rendering method for a game scene and electronic equipment, and belongs to the technical field of global illumination. According to the method, the voxelization processing area is determined based on the observation point, only voxelization processing needs to be carried out on the scene in the limited range around the observation point, the calculation complexity can be greatly reduced, and memory resource occupation is reduced; tasks are distributed through a main thread, and a plurality of background threads execute voxelization processing in parallel, so that the utilization rate of a GPU can be improved, and blockage of the main thread can be avoided; by determining the processing priority of the voxel blocks, the voxel blocks which greatly influence the picture effect can be processed preferentially, real-time updating of the illumination effect of the central area of the visual field of a player is ensured, and delay or distortion of the key area of the picture is avoided.

The invention relates to a space-frequency domain collaborative low-light deblurring method based on Fourier transform and Mama architecture, and belongs to the technical field of image processing. According to the method, an image is decomposed through Fourier transform, and effective separation of low light degradation and fuzzy degradation is achieved at a frequency domain source according to the physical characteristics of amplitude spectrum dominant illumination and a phase spectrum dominant structure. In order to solve the problem of accurate reconstruction of frequency domain information in a spatial domain, a space-frequency domain double-branch collaborative architecture is constructed, global illumination recovery and local detail enhancement are respectively focused through low-frequency and high-frequency processing branches which are explicitly distinguished in the spatial domain, and frequency domain decoupling advantages and spatial domain sensing capability are adaptively bridged by combining a dynamic feature fusion mechanism. Meanwhile, in order to process different degradation types in the recovery process, a task-oriented decoupling optimization strategy is designed, the strategy activates amplitude adjustment and low-frequency optimization in the encoding stage to deal with low-light enhancement preferentially, and activates phase reconstruction and high-frequency enhancement in the decoding stage to process blurring removal in a targeted mode.

The invention discloses an infrared bidirectional thermal effect simulation method based on radiation and GPU acceleration, and belongs to the field of thermal radiation simulation and parallel computing. Dispersing the complex geometry into patch units, constructing an inter-patch radiation energy balance equation based on a radiance theory, and iteratively solving the final temperature of the patches through a radiance method; a three-level parallel strategy of a task level, a data level and an instruction level is designed, data intensive tasks such as shape factor calculation and radiance iteration are migrated to a GPU to be executed, data storage is optimized in combination with a structure array (SoA) layout and a sparse matrix compression technology, and efficient data sharing of a CPU end and a GPU end is achieved through a CUDA zero copy technology. According to the method, the dependence of a traditional method on regular grids is broken through, the calculation efficiency and precision of million-level surface patch heat radiation transmission in a complex scene are remarkably improved, and an efficient tool is provided for heat radiation indirect transmission calculation and a global illumination model in the field of three-dimensional scene infrared simulation.

The embodiment of the invention provides a global illumination rendering method, electronic equipment and a storage medium, belongs to the technical field of computer graphic processing, is applied to illumination rendering of a three-dimensional modeling scene, and comprises the following steps: obtaining an original picture without adding a global illumination effect, a corresponding camera viewpoint and a corresponding camera viewpoint; acquiring originally corresponding illumination radiation data based on a camera viewpoint, and determining the originally corresponding illumination radiation data as irradiance cache data; in response to the change of the camera viewpoint, performing ray tracing processing based on the changed camera viewpoint to obtain changed illumination radiation data; performing fusion processing on the irradiance cache data and the changed illumination radiation data to obtain target illumination radiation data; and performing global illumination generation based on the target illumination radiation data to add a global illumination effect to the original picture to obtain a target picture. According to the embodiment of the invention, the calculation complexity of indirect illumination can be reduced, and a global illumination effect is obtained.

The invention discloses a land environment shadow region optical feature recovery method and system, and the method comprises the steps: synchronously collecting a visible light image, a near infrared spectrum image and three-dimensional point cloud data of a target land region through a multi-mode sensor, and generating multi-source fusion data with a space-time alignment attribute; based on the multi-source fusion data, constructing an illumination transmission physical model of the scene, and separating a direct illumination attenuation component and an indirect illumination diffuse reflection component of a shadow region; performing compensation reconstruction on the direct illumination attenuation component by using a generative adversarial network, and generating an initial recovery image after shadow elimination; and outputting a final recovered image with global illumination consistency and local detail authenticity by adopting a cross-scale attention fusion mechanism. By using the embodiment of the invention, the real spectral attribute and rich spatial texture details of the ground feature can be maintained, and the visual consistency of the image and the reliability of subsequent quantitative application are improved.

The invention discloses a low illumination enhancement method based on event and image bidirectional collaborative guidance, and belongs to the technical field of computer vision. The method comprises an event feature extraction branch and an image-event fusion branch which are respectively provided with an image-guided event enhancement module and a Mama-based image-event fusion module; the two modules realize cross-modal interaction through bidirectional cooperative guidance. The image-guided event enhancement module suppresses event noise and compensates for context loss by using semantic context information of an image. The Mama-based image-event fusion module adjusts the global illumination and contrast of the image by using the global representation capability of Mama and the characteristics of linear space-time complexity, and fuses event data to the image in combination with a signal-to-noise ratio map to supplement structural information. According to the method, enhancement and fusion of the image and event information are realized through a bidirectional collaborative guidance mechanism, and the enhancement problems of image structure deficiency and illumination contrast degradation in a low-illumination scene are solved.

The invention discloses a medical image intelligent diagnosis and three-dimensional visualization interaction method and system based on artificial intelligence and Unreal Engine. The system is composed of a data acquisition module, an artificial intelligence analysis module, a three-dimensional reconstruction module, a rendering interaction module and a report generation module. The method comprises the following steps: firstly, carrying out voxel normalization on a DICOM / NIfTI image; the improved 3D U-Net is combined with CBAM attention and WGAN-GP training, and a focus initial density field is output; an AI enhanced implicit voxel reconstruction (AEIVR) algorithm is proposed, an enhanced density field is generated by using position coding + MLP, and grid extraction is completed by using improved Marking Cubes of a dynamic threshold; high-fidelity real-time rendering is realized in combination with a Nand micro polygon and Lumen global illumination in Unreal Engine, and VR / AR interaction and virtual surgery (real-time updating of resection mask driving) are supported. According to the method, the problems of large artifacts and insufficient interactivity and real-time performance of existing two-dimensional output and three-dimensional reconstruction are solved, and the end-to-end segmentation-reconstruction-rendering-interaction-reporting process is realized. On typical CT / MRI data, the segmentation Dice is about 0.90-0.94, the reconstruction voxel resolution is 0.1-0.5 mm, the artifacts are reduced by about 20%-30%, the PSNR is improved by about 3-7 dB, and the rendering interaction delay lt is achieved; the system is suitable for scenes such as lung CT, brain MRI, orthopaedic X-ray and cardiac ultrasound, and can be expanded to preoperative planning, remote consultation and teaching.

The invention relates to the technical field of image processing, and discloses a tea maturity detection method and system, and the method comprises the steps: obtaining an original image of tea in a natural illumination imaging environment; generating a global illumination difference chart representing an ambient illumination state, and performing illumination compensation on the to-be-detected area of the tea in the original image to obtain a compensated image of the tea; performing gray scale range adjustment on the compensated image of the tea leaves to obtain an enhanced spectral image of the tea leaves; integrating the color and spectral characteristics of the specific spectral reflection characteristics of the tea leaves with different maturity degrees in the enhanced spectral image and the three-dimensional morphological characteristics reflecting the spreading degree and thickness of the tea leaves into the fusion image characteristics of the tea leaves; mapping the color spectrum emission characteristics and texture characteristics of the tea leaves in the enhanced spectrum image to a maturity discrimination knowledge base of the tea leaves to obtain a preliminary maturity result; performing confidence coefficient weighting on the preliminary maturity result to obtain a target maturity; according to the invention, the tea maturity detection efficiency can be improved.

The invention provides an image enhancement method based on double-domain illumination prior and electronic equipment, and relates to the field of image processing. The method comprises the steps of obtaining a to-be-processed illumination image; according to local illumination distribution characteristics of the to-be-processed illumination image, multi-scale enhancement is carried out on the to-be-processed illumination image after global illumination enhancement, a first illumination image after spatial domain enhancement is obtained, and the local illumination distribution characteristics comprise a brightness mean value and a brightness standard deviation; amplitude information of the to-be-processed illumination image in the frequency domain is enhanced, and a second illumination image enhanced in the frequency domain is obtained; and fusing the to-be-processed illumination image, the first illumination image and the second illumination image to obtain an enhanced target illumination image.

Disclosed approaches provide for interactions of secondary rays of light transport paths in a virtual environment to share lighting contributions when determining lighting conditions for a light transport path. Interactions may be shared based on similarities in characteristics (e.g., hit locations), which may define a region in which interactions may share lighting condition data. The region may correspond to a texel of a texture map and lighting contribution data for interactions may be accumulated to the texel spatially and / or temporally, then used to compute composite lighting contribution data that estimates radiance at an interaction. Approaches are also provided for reprojecting lighting contributions of interactions to pixels to share lighting contribution data from secondary bounces of light transport paths while avoiding potential over blurring.

The invention discloses a global neural drawing method and system oriented to hybrid representation optimization, and belongs to the technical field of computer graphics, and the method comprises the steps: dividing a screen into a plurality of tiles, and obtaining a first workload index on each tile in a first drawing stage and a second workload index on each tile in a second drawing stage; generating a cross-tile and cross-stage unified scheduling queue based on the first workload index and the second workload index, and executing the drawing tasks on different tiles in parallel according to the unified scheduling queue; and triggering a subsequent feature fusion and neural global illumination drawing stage when the inter-stage dependency condition is met so as to generate an output image with a global illumination effect. According to the method, the end-to-end frame rate oriented to the modern AI accelerator / AI-GPU can be remarkably improved and the tail delay can be reduced on the premise of ensuring the visual quality and the time sequence stability, and the method is suitable for application scenes such as real-time global illumination, large-scale complex scene drawing and interactive neural rendering.

The invention provides an unmanned aerial vehicle target detection method and device based on infrared and visible light image fusion, and the method comprises the steps: collecting images of two modes of a same scene at the same time through a sensor carried on an unmanned aerial vehicle, and extracting global illumination information from a visible light image to generate an illumination weight, calculating weighting coefficients of the two modes according to the illumination weight; weighting the visible light feature map and the infrared feature map by using corresponding weighting coefficients to obtain weighted features; determining a frequency domain difference between the weighted features of the two modes, and enhancing the weighted features according to the frequency domain difference to obtain enhanced features; splicing the enhanced features of the two modes, respectively inputting a global branch and a local branch, obtaining a global offset field and a local offset field, superposing the global offset field and the local offset field to obtain a final offset field, and aligning the implicit positions of the infrared enhanced features by using the final offset field; and splicing the aligned enhanced features of the two modalities, and inputting the spliced enhanced features into a target detection head to obtain a target detection result. According to the invention, the target detection precision and robustness are improved.

The invention discloses a multi-modal image processing method based on frequency perception and interactive fusion. The problem of image fusion under a low-light condition can be solved in a decoupling type double-branch parallel processing mode. The method comprises the following steps: importing a data set and performing characteristic decomposition, receiving visible light and infrared images, and decoupling each modal image into low-rank and sparse characteristics; carrying out interactive content fusion, carrying out cross-modal interaction on high and low frequency features through bilateral information exchange, carrying out enhancement, and reconstructing an intermediate fusion image; performing parallel global illumination estimation, inputting the visible light image into an illumination estimation network based on frequency domain processing, and generating an ideal illumination image; and performing physical illumination reconstruction and output, and performing brightness improvement on the intermediate fusion image to obtain a fusion image. According to the method, specialized processing of frequency perception and a cross-modal deep interaction mechanism are combined, and the quality and robustness of multi-modal image fusion under complex conditions are remarkably improved by decoupling content fusion and an illumination estimation task.

The invention discloses an image shadow removal method based on multi-scale illumination decomposition, and belongs to the field of image processing, and the method comprises the steps: proposing an improved Retinex-based illumination decomposition network, and decoupling an input image into an illumination component representing illumination distribution and a reflection component containing the inherent texture of an object; a local illumination recovery method based on wavelet features is provided, frequency domain features are extracted by using multi-scale wavelet transform, weight distribution of a shadow area and a non-shadow area is dynamically adjusted through a wavelet attention mechanism, and global illumination consistency is ensured in combination with a reflection component; and a local detail enhancement method based on spatial domain features is provided, the image is further enhanced and refined by using the spatial domain features under the optimized illumination condition, and the texture authenticity of an output shadow removal result is ensured. According to the method and the device, end-to-end shadow removal of the input shadow image can be realized without depending on shadow position prior information, and a high-quality shadow removal result can be obtained.

The embodiment of the invention provides a global illumination rendering method and device, a GPU and a computer readable storage medium, and relates to the technical field of global illumination. Acquiring a plurality of local illumination models corresponding to the current frame; each local illumination model corresponds to one illumination probe, and the local illumination models are calculated by the CPU according to GI primitive parameters of adjacent grid units corresponding to the illumination probes; for each illumination probe in the current frame, performing iterative calculation according to a local illumination model of the illumination probe in the current frame and historical visibility information and historical illumination information of the illumination probe and an adjacent illumination probe of the illumination probe in the previous frame to obtain visibility information and illumination information of the illumination probe in the current frame; and performing global illumination rendering on the current frame according to the visibility information and the illumination information of each illumination probe in the current frame, thereby improving the operation performance and the picture effect of GI processing of the mobile terminal equipment.

The present invention relates to the field of image correction and proposes an image correction method, device, system, and storage medium. The method involves synchronously capturing a color image and three-dimensional point cloud data of a target object using an RGB-D sensor, constructing an elastic mesh model (including stiffness attributes), and achieving high-precision surface unfolding based on finite element analysis to generate a geometrically corrected image. Multispectral shadow separation and generative adversarial network (GAN) inpainting techniques are combined to eliminate occluded shadows and generate textures with consistent illumination. Finally, global illumination uniformity is optimized through dynamic compensation based on ambient light perception. This technical solution aims to reduce errors in unfolding correction of non-planar objects, efficiently distinguish between real content and shadow areas, adapt to dynamic ambient light changes in real time, and improve the ability to generate complex textures.

The invention discloses an image illumination anomaly processing method and device, electronic equipment and a storage medium, which are used for solving the technical problem that the processing effect and the real-time performance cannot be considered at the same time in the prior art. Comprising the following steps: acquiring a brightness component of an RGB image to construct a single-channel brightness matrix; marking an overexposure area and a low-illumination area of the single-channel brightness matrix and calculating a global illumination intensity index; adjusting bilateral filtering parameters according to the global illumination intensity index to adjust a single-channel brightness matrix, and generating a filtered brightness matrix; carrying out enhancement through a Retinex algorithm to obtain an enhanced brightness matrix; determining a key area according to the enhanced brightness matrix and generating a local processing strategy; performing two-layer wavelet transform on the RGB image to obtain a plurality of frequency band components; adjusting each frequency band component according to the global illumination intensity index and the local processing strategy; reconstructing and adjusting the frequency band component through wavelet inverse transformation to obtain a reconstructed RGB image; and converting the reconstructed RGB image into a target image in a preset format.

The utility model relates to a laser demarcation device with a lamp strip, which comprises a demarcation device body, an accommodating caulking groove is arranged on the demarcation device body, a neon lamp strip assembly is arranged in the accommodating caulking groove, the neon lamp strip assembly is connected with an energy supply control port in the demarcation device body, and a light guide device is arranged at the front end of the neon lamp strip assembly. A covering assembly is distributed on the outer surface of the light guide device. Therefore, by selecting different covering assemblies, the neon lamp strip assembly can achieve different display effects such as overflow display, frosted soft light, global lighting and graphic display, and the requirements of decoration display and function display are met. The functions of the demarcation device body can be associated through different colors of the neon lamp strip assembly, for example, green is in a surveying and mapping state, red is in a charging state, blue is in a standby state and the like, so that a user can intuitively know the current state of the demarcation device body.

The invention discloses a complex-material-oriented multi-path full-frequency global illumination neural rendering method and system, and belongs to the technical field of computer graphics, and the method comprises the steps: carrying out the scene query of light emitted by pixels, and obtaining a primary geometric buffer and a multi-path mixed geometric buffer of the multi-time ejection of the light; respectively extracting main frequency features and multi-path sub-frequency features in a neural feature field based on the two features; performing directed graph structured modeling based on the multi-path sub-frequency features to obtain propagation features representing light path propagation information and global features representing overall light path information; performing multi-path frequency joint feature fusion based on the dominant frequency feature, the propagation feature and the global feature to generate a fusion feature with a full-frequency feature; and estimating a final radiation brightness value of the pixel based on the fusion feature, and completing neural rendering of global illumination. According to the method, efficient modeling and high-quality drawing of multi-path full-frequency global illumination under a complex material can be realized, and the sense of reality and dynamic consistency of a rendered image are remarkably improved.

This invention discloses a method and system for enhancing low-light underwater images, including constructing a low-light underwater image dataset, constructing a low-light underwater image enhancement network model, training the constructed low-light underwater image enhancement network model based on the low-light underwater image dataset, and finally using the trained low-light underwater image enhancement network model to predict low-light underwater image test samples to obtain high-quality underwater images, which are then evaluated using evaluation metrics. The solution of this invention employs a Retienx decomposition module based on deep learning, which effectively alleviates the specific scene limitations of pure physical algorithms. Utilizing a decoder feature module and a global illumination feature module, it promotes information interaction between encoding layer features and decoding layer features at the global level, thereby greatly improving the model's restoration capability. Extensive experiments have demonstrated the effectiveness of this method, and experiments on real low-light underwater image datasets show that the proposed method has good performance.

The invention belongs to the field of image intelligent processing, and relates to a low-light image enhancement method, which comprises the following steps of: 1) acquiring a low-light image; 2) generating a feature map U fusing global illumination distribution and local details based on the low-light image; 3) performing brightness enhancement on the feature map U fusing the global illumination distribution and the local details obtained in the step 2) to obtain a brightness enhanced feature map theta; and 4) carrying out contrast self-adaptive iterative optimization on the brightness enhancement feature graph theta obtained in the step 3) to obtain an enhanced RGB image.The invention provides the low-light image enhancement method which can improve the image quality, remarkably reduce the calculation complexity and provide an efficient solution for real-time processing of a complex low-light scene.

The application discloses a kind of based on real-time global illumination and grating hybrid acceleration ultra-high voltage station rendering method, it is related to ultra-high voltage station inspection simulation technical field, including the following steps: in strong electromagnetic interference environment, by adaptive electromagnetic interference filtering algorithm is combined with global illumination probe dynamic interpolation compensation algorithm, dynamic adjustment illumination sampling frequency and insert pre-computed global illumination data.The application is by adaptive electromagnetic interference filtering and illumination sampling dynamic compensation mechanism, effectively suppresses the high-frequency jitter of illumination data caused by strong electromagnetic noise, can according to the noise spectrum characteristics of real-time acquisition intelligent adjustment global illumination probe sampling frequency, and is combined with material perception illumination interpolation algorithm, ensure that metal and insulator surface under different interference intensity all present continuous, smooth illumination performance, so that ultra-high voltage station still can maintain stable visual output under strong electromagnetic interference environment, guarantee the reliability and authenticity of inspection simulation process.

The invention discloses a self-calibration surface normal field reconstruction method and device based on asynchronous event streams. The method comprises the following steps: controlling an illumination unit to actively scan to generate a time-varying illumination field, and acquiring an event stream through a monocular asynchronous vision sensor; taking the ratio of the ambient light to the surface albedo as a fourth component to construct a joint state vector containing a three-dimensional surface normal vector, deducing the orthogonal constraint of the joint state vector and a four-dimensional differential observation vector, and realizing mathematical decoupling of ambient light interference; representing a time-varying illumination track as weighted superposition of discrete anchor points by adopting a parameterized basis function with a local support characteristic, and establishing a corresponding relation between an asynchronous event and global illumination; eliminating generalized bas-relief transformation fuzziness by utilizing rigid constraint provided by preset spatial geometric configuration of the lighting unit; and the surface normal field and illumination parameters are solved at the same time through global joint iterative optimization. According to the method, precise illumination calibration is not needed, and low-data-bandwidth and high-dynamic-range surface reconstruction can be achieved under the non-darkroom condition.

This invention relates to the field of satellite simulation technology, and discloses a method, device, and electronic equipment for simulating ultra-high resolution multi-view satellite images. The method includes: constructing a rigorous imaging geometric model between ground points in a preset three-dimensional scene and image points of a satellite sensor based on atmospheric refraction correction and solid tide correction; generating global illumination realistic shadows under the rigorous imaging geometric model based on solar radiation, and simulating the pixel output of the satellite sensor according to the global illumination realistic shadows to obtain a multi-view satellite image model; simulating different view images of the satellite sensor based on the multi-view satellite image model to obtain multi-view satellite images, and determining the rational function model of the rigorous imaging geometric model. This invention can overcome the limitations of traditional DSM / DOM data sources, taking into account the requirements of multi-view, ultra-high resolution, ultra-high precision, and fine detail, and achieving engineering simulation output of multi-view satellite images at the 0.2m level.

The invention belongs to the technical field of image processing, and particularly discloses a real-time global illumination processing method and system, and the method comprises the steps: arranging an illumination probe network in a virtual scene; a preset number of rays are emitted from the position of each illumination probe, intersection points of the rays and the surface of an object or the sky serve as sampling samples, and the sampling sample set is optimized; dynamically updating the radiance value of the sampling sample based on the current light source information; the radiance value is integrated and coded through the spherical harmonic function to generate the irradiance coefficient, through the technical scheme that the illumination probe network is combined with the sampling sample set, the processing efficiency is remarkably improved while the illumination calculation precision is guaranteed, the multi-order spherical harmonic function is adopted to carry out integral coding on the radiance value to generate the irradiance coefficient, and the processing efficiency is improved. The spatial continuity of illumination is ensured; and the reality sense and adaptability of global illumination are improved, and the requirements for high-fidelity real-time rendering in the fields of games, virtual reality and the like are met.

The application discloses a low-light enhancement method based on event and image bidirectional collaborative guidance, and belongs to the technical field of computer vision.The application contains an event feature extraction branch and an image-event fusion branch, and an image-guided event enhancement module and an image-event fusion module based on Mamba are respectively arranged; the two modules realize cross-modal interaction through bidirectional collaborative guidance; the image-guided event enhancement module suppresses event noise and compensates for context loss by using semantic context information of the image; the image-event fusion module based on Mamba adjusts global illumination and contrast of the image by using the global representation ability and linear space-time complexity characteristics of Mamba, and combines the signal-to-noise ratio map to fuse event data to the image to supplement structural information.The application realizes enhancement and fusion of image and event information through a bidirectional collaborative guidance mechanism, and solves the enhancement problem of image structure loss and illumination contrast degradation in a low-light scene.

The present invention discloses a diffuse global illumination method and system based on sparse distance fields and voxelization, comprising: constructing a sparse distance field, voxelizing a scene, constructing a state machine for a lighting probe, using the distance field to perform ray tracing to obtain radiance, performing a mixing process on the radiance to obtain irradiance, applying lighting through the lighting probe, and finally realizing dynamic diffuse global illumination. The use of sparse distance fields and voxelized scene expressions solves the dependence of such algorithms on dedicated ray tracing hardware, and the present invention performs many effective optimizations to help the system run efficiently. Therefore, the present invention analyzes the dynamic diffuse global illumination algorithm and adopts a distance field ray tracing method that does not rely on a specific hardware acceleration structure, thereby realizing the use of distance fields for ray tracing to obtain radiant brightness in the dynamic diffuse global illumination algorithm, improving the operating efficiency, and enabling the algorithm to be applied in a real-time rendering environment.

The invention relates to the technical field of vehicle visual perception, and discloses an intelligent driving decision-making system based on an intelligent camera, which comprises the following steps: quantifying the dynamic stability of a scene in real time by calculating the structure entropy of an optical flow field in a foreground dynamic region, and combining with parallel monitoring of global illumination change to determine the dynamic stability of the scene; the risk early warning signal is generated only when the dynamic instability is judged to be from a real physical risk instead of an illumination artifact, the dynamic stability of a scene can be directly quantified, alertness is generated in the germination stage of risk occurrence, the limitation that the traditional technology depends on semantic recognition and responds to a tiny dynamic risk is delayed is avoided, and the risk early warning signal is generated only when the dynamic instability is judged to be from the real physical risk instead of the illumination artifact. And an endogenous sensing credibility self-calibration mechanism ensures the decision reliability of the system under a complex illumination condition, and improves the safety of the intelligent driving system.

The invention provides a multi-scale dual-perception low-light image enhancement method based on alternate compensation, and belongs to the technical field of computer vision and image processing. According to the method, a collaborative enhancement network structure coupling detail perception and illumination perception is adopted, and the brightness recovery capability and the detail reconstruction capability of the low-illumination image are effectively improved through a multi-scale dynamic interaction mechanism. The structure comprises a detail sensing module used for extracting local texture and space structure information and enhancing dark area detail expression; the illumination sensing module realizes global illumination consistency and smoothness enhancement through a structured modeling and sequential space modeling mechanism; and the two are fused layer by layer through a cross-scale alternating compensation module, so that collaborative modeling and dynamic adjustment of illumination and details are realized, and the enhancement process is guided to improve the image perception quality while keeping the illumination naturalness. In order to solve the problems of uneven brightness, detail missing, local overexposure and the like in a traditional method, the invention further designs an illumination smoothing mechanism based on state space modeling and a guide feature alignment method fusing local-global perception, so that the model can accurately recover scene structure and texture information under different illumination conditions. The method supports enhancement tasks in various low-light image scenes, can improve the accuracy of subsequent perception tasks (such as target detection and face recognition) while keeping the sense of reality and the sense of naturalness of the image, and has good universality and robustness.