65 results about "Human visual perception" patented technology

The invention discloses a non-reference image quality evaluation method and device based on multi-perception feature fusion and dynamic enhancement, and the method comprises the steps: obtaining the quality information of a multi-domain distorted image based on superpixel segmentation and Gaussian kernel filtering texture generation; quality related feature extraction is performed on multi-domain information through a semantic perception module and a distortion perception module, bidirectional modulation is performed on global visual features of an original distorted image through a cross attention mechanism, and dynamic fusion of multiple perception features is realized; through a parallel feature enhancement unit formed by local adaptive filtering of a visual self-attention block and a dynamic residual block, dynamic allocation of perception modes to different content areas is realized; generating a weighted quality score consistent with human visual perception through a weighted dual-path regression device; and outputting a predicted score consistent with the human score from the distorted image through the three sub-networks. According to the method, the problems of insufficient adaptability to complex content of a distorted image and low local distortion sensitivity are effectively solved.

The invention provides a dynamic feature contribution degree-based color textile fabric image retrieval method. Innovation and improvement are carried out aiming at single feature limitation and fixed weight fusion defects existing in a traditional retrieval technology. Color, texture and shape features are separated through a multi-modal feature decoupling technology, and coupling interference between the features is eliminated; a dynamic contribution degree quantification mechanism is innovatively constructed, gradient back propagation is used for calculating the contribution proportion of features to retrieval results in real time, and weight self-adaptive adjustment is achieved. According to the method, the problem of retrieval deviation caused by complex characteristics such as color gradient and blending structure difference of the colored spun yarn fabric is solved in a breakthrough mode, weight distribution can be intelligently matched according to inquired image characteristics (for example, the color gradient fabric focuses on color characteristics, and the jacquard fabric focuses on shape edges), and the retrieval result better fits human visual perception. According to the technical scheme, the image retrieval precision of complex fabrics in the textile industry is effectively improved, and an intelligent solution is provided for production process data association.

The invention relates to the technical field of digital image processing, and discloses an image defogging method based on an atmospheric scattering model and color correction, and the method comprises the following steps: S1, carrying out the color correction of a foggy image with color cast; s2, calculating an atmospheric light value of the foggy image; s3, calculating the transmissivity value of the foggy image; s4, recovering the defogged image by adopting an atmospheric scattering model according to the atmospheric light value and the transmissivity value; and S5, solving a defogged image after adaptive brightness enhancement and adaptive contrast enhancement. According to the method, the color of the obtained defogged image can be recovered to be natural, the phenomena of color cast, incomplete defogging, detail information loss and overall darkness of the image are improved, the recovered defogged image better conforms to the perception of human eyes, meanwhile, the implementation process is simple, the time complexity is low, and better defogging efficiency is achieved.

The invention discloses a super-resolution image reconstruction method based on a multi-scale large separable kernel convolutional neural network, which is suitable for the field of image processing, and comprises the following steps: cutting a data set, inputting a cut original low-resolution image into a preprocessing module, carrying out image normalization and data enhancement operation, and carrying out image reconstruction; generating a preprocessed low-resolution image; the preprocessed low-resolution images form a distorted image block data set, and a training set, a verification set and a test set are formed; constructing a super-resolution image reconstruction method based on a multi-scale large separable kernel convolutional neural network according to an existing distorted image block data set; and inputting the data set into the constructed multi-scale large separable kernel convolutional neural network to extract semantic features, and amplifying a feature map by using an up-sampling module of the model to generate a super-resolution image. According to the method, a multi-scale large separable kernel convolution structure is introduced, a visual attention mechanism is used in the neural network, the extracted features better conform to human visual perception features, and super-resolution image reconstruction is more accurate.

The invention provides a robust anti-attack method for a target detection system based on a reference image. The method comprises the following steps: selecting a detector, selecting a pedestrian image data set with a real label as a training data set, and training the detector by using the training data set to obtain a trained detector; selecting a reference image as an initialized patch, performing enhancement transformation processing on the initialized patch to obtain an optimized patch, generating an adversarial sample by using the optimized patch, inputting the adversarial sample into the trained detector to obtain a detection result, and further optimizing the patch through back propagation to obtain a final patch; and utilizing the final patch to carry out countermeasure attack on a detector in target detection. According to the method, the problems of non-concealment, poor robustness and the like of an adversarial patch in an existing scheme are solved, a novel adversarial patch method which is natural in appearance and conforms to human visual perception is provided, and meanwhile, the robustness of the patch in environment change and multi-view scenes can be improved.

The invention discloses a quantitative detection method for anti-perspective performance of a textile in a complex light environment, and belongs to the technical field of new material detection, a micro gap is arranged between the textile to be detected and a variable-frequency gray scale target to simulate a non-clinging state when clothes are worn and excite Fresnel diffraction and subsurface scattering effects, and the anti-perspective performance of the textile is detected. Complex illumination conditions such as backlight and sidelight are simulated by adjusting the light intensity ratio, and after a target perspective image is acquired by using an image acquisition unit, the modulation transfer function attenuation rate of a to-be-detected textile to the target is calculated, so that the anti-perspective performance of the textile is quantitatively represented; the interference of glare on the surface of the high-gloss fabric is effectively eliminated by introducing an orthogonal polarization modulation technology, and a detection result is highly consistent with the visual perception of human eyes by adopting a specific light source spectrum simulating the skin color of a human body; compared with a traditional light transmittance method, the method has the advantages that the dimension crossing from luminous flux measurement to image information fidelity evaluation is realized, and the accuracy, reliability and practicability of detection are remarkably improved.

The application discloses an infrared and visible light image fusion algorithm based on a self-encoder, and comprises the following steps: S1, an encoder network is constructed by combining a short cross-layer connection with an ECA attention mechanism; S2, registered infrared and visible light images are input into the encoder network, and the encoder and decoder network are trained to obtain an encoder meeting a condition; S3, feature fusion is performed on the input images by using the constructed fusion network, and the fusion network is trained to obtain a fusion network meeting a condition; and S4, the fused images are decoded and reconstructed by using the decoder.The evaluation index of the fused images is significantly improved, the target is clear, the details are prominent, the outline is obvious, and the images meet the human visual perception.

The application discloses a method for testing response time of a mini / micro LED direct display screen, which can avoid the influence of subjective evaluation of human visual perception on the response time, and provides a means for high-precision testing of the response time of the direct display screen through quantitative evaluation method. The method comprises the following steps: (1) a power supply inputs a stable voltage to the direct display screen, the direct display screen is lighted when a square wave driving voltage is input, light enters a microscope lens and converges on a target surface of a high-speed photoelectric detector, a response voltage signal is generated after the target surface is irradiated by the light, the voltage signal is filtered by a high-frequency filter to suppress high-frequency noise, and the voltage signal is connected to an oscilloscope through a BNC line; (2) the power supply is driven and controlled to realize different refresh rates; and step (1) is repeated to realize the response time test of the direct display screen under different refresh rates; and (3) different colors are realized by controlling a signal of the driving power supply; and step (1) is repeated to realize the response time test of the direct display screen under different colors.

The application discloses a perceptual-geometry joint code rate control method, system and medium, and belongs to the field of video coding. The method comprises the following steps: assigning weights to each CTU in each frame in a video according to the VVC standard; for each I frame, calculating the perceptual-geometry joint weight of each CTU in the I frame, and updating the weight of each CTU in the I frame; and assigning a code rate to each CTU according to the weight of each CTU in each frame to complete code rate control. The application can effectively eliminate geometric redundancy and improve human visual perception consistency, and promote efficient transmission of high-resolution videos.

The invention provides an infrared and visible light image fusion method based on a second-order attention mixed feature, and the method comprises the steps: inputting the first-order and second-order statistics of an infrared image and a visible light image, and adaptively integrating the global features and local features of the infrared and visible light images, thereby enabling the fused image to obtain a stronger feature expression, and improving the image fusion precision. Pixel intensity and texture details are reserved at the same time, comparable contrast is kept while a more vivid edge is achieved, an output result keeps rich edge and texture information transmitted by an input image, artifacts are few, and visual perception of human beings is better met. Compared with other infrared and visible light image fusion algorithms based on deep learning, the method has a better fusion effect.

This invention discloses a method for fusing infrared and visible light images based on multi-layer convolution. The network structure includes an encoder network, a decoder network, and a multi-layer convolutional fusion network. The encoder is composed of nested multi-layer convolutional blocks and an ECA attention mechanism. The decoder is mainly composed of decoding blocks, each of which consists of two convolutional layers. The multi-layer convolutional fusion network is mainly composed of gradient convolutional blocks, downsampling convolutional blocks, a convolutional spatial channel attention mechanism, and several convolutional layers. The method includes the following steps: S1, the registered infrared source image and visible light source image are fed into the encoder in pairs, and the encoder extracts the source image features; S2, the multi-layer convolutional fusion network fuses the source image features to obtain the fused features; S3, the decoder reconstructs the fused features and outputs the image. The fused image produced by this invention has prominent targets, clear details, obvious contours, and significantly improved performance indicators, conforming to human visual perception.

The application provides a color spun fabric image retrieval method based on dynamic feature contribution degree, and innovatively improves the single feature limitation and fixed weight fusion defects existing in traditional retrieval technology. Color, texture and shape features are separated through a multi-modal feature decoupling technology to eliminate the coupling interference between features; a dynamic contribution quantification mechanism is innovatively constructed, the contribution proportion of features to retrieval results is calculated in real time by using gradient back propagation, and adaptive adjustment of the weight is realized. The method breaks through the retrieval deviation problem of color spun yarn fabrics caused by complex characteristics such as color gradient and difference in blended structure, can intelligently match the weight distribution according to the query image features (such as color gradient fabric focusing on color features and jacquard fabric focusing on shape edges), and makes the retrieval results more consistent with human visual perception. The technical scheme effectively improves the image retrieval accuracy of complex fabrics in the textile industry and provides an intelligent solution for production process data association.

The application discloses a kind of based on layered feature fusion's no reference image usability evaluation method.There is following step: first, respectively extracting the low-level picture quality feature vector of the image to be evaluated, middle layer structure feature vector and high-level semantic feature vector;Subsequently, respectively to feature vector is normalized and splicing;Then, total feature vector is input to the regression network of integrated layered attention fusion ware, the network passes through multi-head self-attention mechanism dynamically learns the contribution weight of each level feature to the final availability score, and carries out regression prediction, outputs the availability evaluation score of image.The application realizes the fundamental change of evaluation target from "human visual perception quality" to "machine task availability" by systematically fusing multidimensional features from picture quality to semantics, and the evaluation result is strongly related to the performance of high-level visual tasks such as object detection and pose estimation, and is suitable for image screening and quality control in scenarios such as autonomous driving and intelligent monitoring.

The invention discloses a lamp light quality detection system and method, relates to the technical field of lamp detection, and solves the problems that traditional detection depends on physical parameters and cannot reflect real visual perception of a human body, and the system comprises a spectacle frame, and a physiological signal sensor, a signal processing unit and a light quality detection unit which are integrated on the spectacle frame. Visual physiological reaction signals of a user under illumination of the lamp are collected in real time through the sensor, and after physiological features are extracted through the processing unit, quantitative detection indexes of light quality are output through a model built in the detection unit. According to the invention, objective and personalized in-situ detection and evaluation of the light quality of the lamp in a natural eye using state are realized, and the method is mainly suitable for field detection and health evaluation of various lighting environments such as classrooms and offices.

The invention discloses a physical perception fusion driven adaptive texture generation optimization method and system. The method comprises the following steps: collecting spectral reflectivity data of a material and microscopic texture image data of the surface of the material, and preprocessing the spectral reflectivity data and the microscopic texture image data; constructing an initial physical and perception fusion driven texture generation model; carrying out training optimization on the model by adopting a cross-domain joint training strategy through a physical priority-perception priority-joint fine tuning alternative optimization scheme to obtain a physical and perception fusion driven texture generation model; and performing actual texture generation based on the obtained physical and perception fusion driven texture generation model. According to the method, a new-generation adaptive texture optimization framework is constructed by deeply fusing a physical rule and a human visual perception mechanism, the problem of physical-perception splitting existing in the high-fidelity rendering field for a long time is solved, and breakthrough in spectrum modeling precision, rendering efficiency and visual quality is achieved.

The invention discloses an intelligent ship sensing method based on scene complexity. The method comprises the following steps: defining a scene complexity level; screening image texture feature quantities; calculating image texture feature parameters; classifying scene complexity by using an ensemble learning network model; according to the method, uncertain factors in ship navigation scene images are comprehensively considered, image features are extracted by using a gray-level co-occurrence matrix, and an XGBoost network model of integrated learning is used for sensing the complexity of a navigation scene. The method comprises the following steps: by simulating human visual perception, combining a plurality of parameters such as energy, entropy, contrast, inverse difference moment and correlation of an image, and constructing a mathematical model capable of truly reflecting scene complexity; the method provides important reference for the design and construction of the autonomous navigation scene of the intelligent ship, and helps to improve the safety and reliability of the intelligent ship in actual navigation by evaluating the complexity of the virtual test scene.

This invention relates to the field of HDR reconstruction, specifically to the task of reconstructing HDR images from multi-exposure LDR images. A perceptual artifact detection and elimination method for high dynamic range reconstruction is proposed, comprising the following steps: Step 1, establishing an HDR-AD dataset; Step 2, constructing and training an HDR-ADet model; building an artifact detector HDR-ADet based on ViT, and training this detector using the HDR-AD dataset to locate artifact regions in the image; Step 3, using the artifact mask obtained from HDR-ADet to fine-tune the HDR reconstruction model to improve the quality of the reconstructed HDR image; and further improving the quality of the reconstructed HDR image by integrating HDR-ADet into the loss function calculation process of the HDR reconstruction framework. This invention can improve the quality of HDR imaging, achieving breakthroughs not only in quantitative indicators but also more closely approximating human visual perception.

The invention relates to an evaluation method and system applied to restored images of ancient Chinese calligraphy and painting, and the method comprises the steps: 1, taking the restored images of a first ancient calligraphy and painting and a second ancient calligraphy and painting, which are restored through an artificial intelligence technology and PS software, as evaluation preprocessing samples, selecting a peak signal-to-noise ratio, a structural similarity index and a color difference to combine with a subjective visual MOS score to evaluate a restoration result; 2, according to the evaluation condition, optimizing the weight of each feature component in the ancient calligraphy and painting repair evaluation for one objective evaluation index in the step 1, and the like. The evaluation system comprises an ancient calligraphy and painting repairing module, an evaluation module and a verification module. The invention further provides electronic equipment and a computer readable storage medium which are applied to the evaluation method and system. The method has the advantages that the restoration result of the ancient calligraphy and painting is assessed through a multi-dimensional assessment system, four indexes of PSNR, SSIM, HSV and MOS are fused, and the texture, color, structure and human eye perception full-dimensional requirements of restoration of the ancient calligraphy and painting are covered.

This disclosure presents an efficient rendering method based on scene-aware mapping for complex scenes. One specific implementation of this method includes acquiring a scene model, camera parameters, and user gaze point information corresponding to the target scene; performing spatial mapping processing on the scene model based on the camera parameters to obtain a two-dimensional geometric scene graph; generating a consistent visual importance graph based on the user gaze point information and the two-dimensional geometric scene graph; generating a pixel size control graph based on the two-dimensional geometric scene graph and the consistent visual importance graph; and rendering the two-dimensional geometric scene graph based on the pixel size control graph to obtain a rendered scene image. This implementation can reduce the computational resources required for rendering and improve rendering speed while ensuring the quality of human visual perception.

The application discloses a blind image quality evaluation method based on multi-scale features and long-distance dependence, and comprises the following steps: step 1, acquiring a training sample set and a test sample set; step 2, constructing a network model of a no-reference image quality evaluation method based on multi-scale features and long-distance dependence; the network model is used for extracting and fusing multi-scale features and long-distance dependence of an image to regress to an image quality score; step 3, iteratively training the network model of the no-reference image quality evaluation method based on multi-scale features and long-distance dependence; and step 4, acquiring a no-reference quality evaluation result of the image. The application is used for solving the problems of large parameter quantity and low calculation efficiency of an existing method and the problem that the existing method neglects the fusion of long-distance dependence between multi-scale features and local quality features, thereby leading to low consistency between a model prediction result and human visual perception.

The application discloses a GAN image repairing method based on stable field jump connection, embeds a stable field operator into a jump connection, uses the stable field operator to preliminarily predict damaged pixels of an encoder feature map in a generation unit, then transfers to a decoder and outputs a generated image. Then, the type of loss is refined, the loss function is redefined as an adversarial loss, a pixel reconstruction loss and a pyramid loss to further guide the training of the network to the correct direction. The method improves the effect of damaged image repairing, meets the demand of human visual perception, and is similar to the original image to a certain extent. And experiments prove that the method achieves good repairing effect on face images, natural scene images and building images.

The invention discloses a multispectral optical signal acquisition and AI intelligent analysis device and method, and belongs to the technical field of optical signal detection and artificial intelligence. A multi-spectral optical signal intelligent analysis deep network system (MSS-IDNS) is defined as a core, and the system takes a 300-1000nm multi-spectral optical signal as an input carrier and can be fused with various deep learning models. The device comprises a multispectral optical signal acquisition terminal and an AI intelligent analysis module. The method comprises the six steps of optical signal collection, data preprocessing, feature extraction, AI analysis, model iteration and intelligent response, so that intelligent analysis and biological state recognition of optical signals are completed. The method breaks through the human visual perception boundary and adapts to multiple scenes, core hardware is an existing mature industrial product, batch production can be achieved, and the method has extremely high civilian mass production potential and commercial value.

The 3D point cloud simplification method combining human visual perception characteristics belongs to the field of 3D point cloud data processing, and aims to solve the problem that the sharp increase of the amount of dense 3D point cloud data aggravates the burden of data processing, storage and transmission in the later stage, the present application provides a kind of 3D point cloud simplification method combining geometric features and human visual perception characteristics;Combined with the geometric features of point cloud, the algorithm establishes a one-way perception sharpness function and a local visibility function to complete the importance evaluation of points, and then formulates different simplification rules according to the importance of points to realize hierarchical simplification of point cloud.In addition, in order to improve the universality of the mixed feature evaluation model, a dynamic optimization strategy for the weight of each evaluation function is established, and the real-time updating of the weight value is realized based on the feature evaluation result.The experiment verifies the effectiveness of the algorithm, compared with the traditional point cloud simplification algorithm, the algorithm can maximize the retention of local details of point cloud while maintaining the overall uniformity of data.

This application discloses a display device, including a display panel, a backlight module, a controller, and a memory. The memory stores a network model and human visual perception HK effect data. The controller first acquires image data and inputs it along with the HK effect data into the network model. After extracting image feature vectors and HK feature vectors, feature fusion is performed to obtain fused features. The network model then processes the fused features to obtain target image data. Based on the target image data, backlight values ​​and pixel drive values ​​are generated respectively, thereby controlling the backlight module and the display panel to complete image display. This solution not only improves the display effect but also enhances the user experience.

This invention discloses a lightweight low-light image enhancement method based on residual dense blocks, comprising the following steps: acquiring a paired low-light image dataset; constructing a conditional generative adversarial network (GAN) model, including designing a lightweight generator network based on residual dense blocks and channel attention mechanisms, and a fully convolutional discriminator network; determining a multimodal loss function based on global similarity loss, structural similarity loss, content similarity loss, color similarity loss, and local texture loss; and training and testing the GAN model. This invention can process 400*600 low-light images at 36 frames per second on an RTX 2080 Ti graphics card. The low-light images enhanced by this invention are more consistent with human visual perception and exhibit superior performance on common image evaluation metrics such as peak signal-to-noise ratio and structural similarity.

The application discloses a physical perception fusion driven adaptive texture generation optimization method and system. The method comprises the following steps: collecting material spectral reflectance data and material surface micro texture image data, and performing pretreatment; constructing an initial physical and perception fusion driven texture generation model; adopting a cross-domain joint training strategy, training and optimizing the model through an alternating optimization scheme of physical priority-perception priority-joint fine-tuning, to obtain a physical and perception fusion driven texture generation model; and performing actual texture generation based on the obtained physical and perception fusion driven texture generation model. The method fuses physical laws and human visual perception mechanisms in depth, constructs a new generation of adaptive texture optimization framework, solves the long-standing "physical-perception" split problem in the field of high-fidelity rendering, and realizes a breakthrough in spectral modeling accuracy, rendering efficiency and visual quality.

The embodiment of the invention provides an electronic image stabilization method, which is used for processing visible light video data of an airborne photoelectric hanging bin, and comprises the following steps of: 1, acquiring video sequence basic information; step 2, feature point detection and motion estimation; step 3, performing abnormal processing on the motion model; step 4, motion trail smoothing processing; 5, image stabilization transformation and post-processing are carried out; and step 6, outputting a result. Aiming at visible light video data of an airborne photoelectric pod, various forms of jitter motion can be accurately captured and compensated through multi-scale feature point tracking and intelligent motion estimation. Compared with a traditional method, the method shows higher precision and stability when a complex motion mode is processed. Especially in scenes with slight motion blur, illumination variation or partial shielding, the method can still keep reliable image stabilization performance. The smooth processing of the motion trail enables the finally output video to eliminate uncomfortable jittering and maintain the intentional motion of the camera, thereby conforming to the visual perception characteristics of human beings.

The present invention discloses an adaptive multi-depth sub-pixel layer defect detection method, comprising the following steps: acquiring pictures of the sub-pixel layer through an image acquisition module to obtain images of different depths of field; preprocessing the acquired images based on an image preprocessing module, specifically adopting a plurality of filters to perform noise reduction and smoothing on the input images to eliminate noise interference and improve image quality; accurately extracting edge features in images of different depths of field through a defect detection module; calculating multi-dimensional feature information of the images through a feature calculation module; designing defect evaluation indicators of human visual perception based on the multi-dimensional feature information obtained above, accurately locating the clearest layer where the defects are located by adaptively adjusting the weights of each evaluation indicator and combining the depth information of the multi-depth images, thereby determining the optimal distribution position of the defects in the sub-pixel layer, and introducing a comparative analysis mechanism consistent with human visual perception to ensure that the detection results are highly consistent with human visual perception.

This invention provides a method, device, and display screen for brightness control of a MiniLED display screen based on multi-source environmental perception. By acquiring dynamic changes in ambient light and content features of the currently displayed image, the method inputs these information into a pre-defined human visual perception model to analyze perception requirements, obtaining a set of perception requirements features matching the current viewing scene. Based on this feature set, a dynamic mapping mechanism is invoked to construct a brightness mapping relationship, generating a dynamic brightness mapping curve reflecting the synergistic effect of ambient light and image content. A pre-constructed partitioned light effect model is then invoked, and inverse solving is performed based on the model and the dynamic brightness mapping curve to generate partitioned calibration driving parameters, which are then sent to the MiniLED backlight module for brightness adjustment. This invention effectively improves the accuracy of brightness control and visual comfort of the MiniLED display screen, while ensuring consistency between the actual hardware output and the target brightness.