37results about How to "Improve super-resolution" patented technology

The invention discloses a VHF (very high frequency) frequency-range intracloud lightning detecting and positioning system, which comprises a central treatment station and a plurality of detection and treatment sub-stations arranged in different positions, wherein the detection and treatment sub-stations and the central treatment station are accessed into a public wide area network in a wired or wireless mode; and through the wide area network, on one hand, obtained intracloud lightning detection data is uploaded to the central treatment station by the detection and treatment sub-stations, andon the other hand, the working state of each detection and treatment sub-station is remotely monitored by the central treatment station. The system can be used for precisely estimating the incidence directions of lightning radiation signals of a plurality of simultaneously occurring lightning radiation sources or the same lightning radiation source arriving at the detection sub-stations through different propagation paths, the arriving time difference of the lightning signals between any two detection and treatment sub-stations is calculated at the central treatment station (TDOA estimation),and the spatial position of an intracloud lightning radiation source is determined by DOA and TDOA information.

The invention relates to a face super-resolution reconstruction method based on an identity priori generative adversarial network. The face super-resolution reconstruction method comprises the following steps: firstly, reading an original face picture data set; then, utilizing a face image-identity label pair to train a face feature extraction network; thirdly, reading the high-resolution face image for bicubic interpolation down-sampling to obtain a high-resolution face image-low-resolution face image pair for model training; fourthly, inputting the low-resolution face image into a generatornetwork to generate a super-resolution face image; respectively inputting the high-resolution face image and the super-resolution face image into a trained face feature extraction network, and extracting identity prior features of the high-resolution face image and the super-resolution face image; and inputting the high-resolution face image, the super-resolution image and the corresponding identity prior features into a discriminator network, calculating a supervised adversarial loss function by using the output of the discriminator network, and training a generative adversarial network by using error back propagation.

A method for detecting small objects in a structured road based on deep learning comprises the steps that image data, containing the small objects, on the real structured road are collected, and the positions and the category information of the small objects in the structured road are marked through a manual method; Constructing a deep convolutional neural network suitable for small object detection in the structured road and a corresponding loss function; Inputting the acquired image and the labeled data into the convolutional neural network constructed in the previous step, updating the parameter value in the neural network according to the loss value between the output value and the target value, and finally obtaining an ideal network parameter. The invention provides a brand new network structure for the problem that the current neural network is poor in small object detection. On the premise that the calculated amount is not increased basically, the performance of small object detection is greatly improved, and the method can be conveniently deployed in an existing intelligent driving system, so that an intelligent driving automobile can detect dangerous objects on a road in along distance and respond in time, and the safety in the driving process is improved.

The invention provides a high-efficiency super-resolution imaging device and method with regional management, and aims at solving the problem that an existing super-resolution imaging device and method is long in imaging time and low in resolution. The imaging device comprises an imaging lens group, a high-resolution detector, an image storage module, an image pre-processing module, an image super-resolution reconstruction module and an image output display module connected successively, wherein the image super-resolution reconstruction module is composed of an image regional management sub-module and a dictionary training and regional reconstruction sub-module. The imaging method comprises the following steps of obtaining optical signals of a practical scene; obtaining low-resolution images; storing the low-resolution images; preprocessing the images; carrying out regional image management; training dictionaries and reconstructing super-resolution areas, and splicing images of the reconstructed super-resolution sub-regions. The imaging device and method can be used to improve the imaging resolution effectively and shorten the imaging time, and applied to the fields including video monitoring, satellite remote-sensing imaging and medical imaging.

The invention belongs to the technical field of computer vision. In order to provide a video super resolution acquisition method which can be widely applied and can be used for obtaining high-quality videos, the technical scheme adopted by the invention is that a video super resolution method for a self-adaption-based superpixel-oriented autoregression model is characterized by comprising the following steps of dividing a video frame into key frames and non-key frames; performing super resolution on the key frames by adopting a sparse regression and natural image pair-based method, performing the super resolution on the non-key frames by adopting the video super resolution method for the self-adaption-based superpixel-oriented autoregression model by combining adjacent nearest key frames, and synthesizing the obtained super-resolution key frames and super-resolution non-key frames into a super-resolution video. The video super resolution method provided by the invention is mainly used for video processing.

Image super-resolution (SR) generally enhance the resolution of images. One of SR's main challenge is discovering mappings between low-resolution (LR) and high-resolution (HR) image patches. The invention learns patch upscaling projection matrices from a training set of images. Input images are divided into overlapping patches, which are normalized and transformed to a defined orientation. Different transformations can be recognized and dealt with by using a simple 2D-projection. The transformed patches are clustered, and cluster specific upscaling projection matrices and corresponding cluster centroids determined during training are applied to obtain upscaled patches. The upscaled patches are assembled to an upscaled image.

The invention belongs to the field of a computer vision technology. In order to provide a video super-resolution acquiring method capable of being widely applied and capable of acquiring a high-quality video, the invention adopts the technical scheme that a video super-resolution method based on feature-oriented variational optical flow is characterized in that video frames are divided into key frames and non-key frames; super-resolution is carried out on the key frames by a method based on sparse regression and natural image pairing; and super-resolution is carried out on the non-key frames by the method based on the feature-oriented variational optical flow in combination with the previous most neighboring key frame, and the super-resolution non-key frames and the super-resolution non-key frames are integrated into a super-resolution video. The video super-resolution method is mainly applied in video processing.

The invention provides a video space-time super-resolution implementation method and device, and the method comprises the steps of carrying out the edge enhancement of a video frame of a video, and obtaining an edge-enhanced video frame; inputting the plurality of edge-enhanced adjacent video frames into an optical flow estimation module in pairs to obtain a bidirectional optical flow; calculating the bidirectional optical flow to obtain an estimated optical flow, and inputting the estimated optical flow and the bidirectional optical flow into a bidirectional prediction module together to obtain a predicted optical flow; calculating the prediction optical flow and the corresponding video frame to obtain an intermediate frame for time domain super-resolution, and inserting the intermediate frame into a corresponding position in the video; and performing spatial domain super-resolution processing on the intermediate frame and the corresponding video frame through a cyclic super-resolution network to obtain a plurality of reconstructed frame; and circularly executing the steps until the space-time super-resolution of the whole video is completed. The invention has the beneficial effects that space-time joint super-resolution can be effectively carried out on the video, and the visual quality of the video is improved.

The invention discloses an image enhancement method for diabetic renal lesion classification and electronic equipment, the enhancement method comprises the steps of contrast enhancement, denoising, super-resolution reconstruction and the like, and the accuracy of an image classification network on diabetic renal lesion classification is improved by improving the quality of a kidney CT image. The super-resolution reconstruction network comprises a preliminary feature extraction unit, an MDC feature extraction unit and an up-sampling unit; the MDC feature extraction unit is arranged at the downstream end of the preliminary feature extraction unit; the plurality of MDC feature extraction units are sequentially connected end to end; and the up-sampling unit is arranged at the downstream end of the MDC feature extraction unit and is used for carrying out super-resolution reconstruction on the second feature map. According to the invention, the super-division network is used for repeatedly utilizing the features, the situation that low-layer feature information gradually disappears in continuous nonlinear operation is effectively avoided, and the model obtains good balance between the feature extraction effect and the complexity.

The invention provides a method of using natural image prior-knowledge for multi-image super-resolution reconstruction. According to the method, a fields-of-experts model obtained by learning is introduced to be used as the natural image prior-knowledge in a process of using a series of low-resolution images for super-resolution reconstruction, and quality of multi-image super-resolution reconstruction is improved. Compared with prior knowledge of L1-norm, total variation and the like used by traditional Bayesian multi-image super-resolution methods, the natural image prior-knowledge used in the method is the fields-of-experts model obtained through training of an image database, and can better extract statistical features of natural scenes, and thus obtain better super-resolution reconstruction effects. The method simplifies partial processes of multi-image super-resolution, and shortens calculation time.

The invention provides a cloud fusion-oriented video super-resolution method and system, and belongs to the field of video processing, and the system comprises a restoration effect prediction module, a task dynamic scheduling module, a mobile terminal processing module, a cloud processing module and a frame fusion module. The method comprises the following steps: collecting features of a current low-resolution video frame, inputting the features into a restoration effect prediction module, and predicting a super-resolution effect of the current video frame after the current video frame passes through a bicubic interpolation method and a video restoration model based on an enhanced variable convolutional network; whether the current low-resolution video frame is unloaded to the cloud processing module for super-resolution reduction is determined through the task dynamic scheduling module; and inputting the video frame after cloud super-division and the video frame after local processing into a frame fusion module to obtain a high-definition video after super-resolution reduction. According to the method, on the premise that cloud resources are utilized, super-resolution processing of low-resolution videos is achieved, and the method has the advantages of being real-time, rapid and accurate in reduction and low in memory resource occupation.

The invention discloses a super-resolution reconstruction method based on blackboard characteristics in an online classroom scene, and the method comprises the following steps: shooting a blackboard picture through a camera built in a classroom, accurately extracting a blackboard writing region through a target detection model, and carrying out the super-resolution reconstruction of an image. Besides, for the blackboard writing content with obvious edge features, gradient constraint is introduced into the super-resolution model to enhance feature learning, and the resolution of the blackboard writing content is effectively improved, so that the class learning efficiency of students is improved. The method has the characteristics of high running speed, low cost, convenience in use and the like, and ensures that online live teaching of the campus classroom is carried out smoothly.

The invention relates to a tiny facial expression recognition method based on an E-FCNN. The method comprises the following steps that 1, a marginalized face enhancement module, an up-sampling networkmodule and a super-resolution network module based on feedback are established respectively, and the marginalized face enhancement module is used for carrying out edge extraction and enhancement on an input face; wherein the up-sampling network module is used for carrying out interpolation method up-sampling on an input image, and the super-resolution network module based on feedback is used forcarrying out super-resolution processing on the input image; 2, establishing an E-FCNN network model in combination with an marginalized face enhancement module, an up-sampling network module and a super-resolution network module based on feedback; and 3, inputting the facial expression image data of the specific size into the E-FCNN network model to obtain a corresponding facial expression recognition result. Compared with the prior art, the method has the advantages of high recognition precision, high recognition speed and the like.