11 results about "Super resolution image reconstruction" patented technology

The application relates to the technical field of image enhancement, in particular to a mine environment super-resolution image reconstruction method based on vibration induction for mine monitoring, which comprises collecting vibration data and image data and performing pretreatment; the processed vibration data sequence is input into a double-layer LSTM network, vibration data is analyzed by using the LSTM, vibration characteristics at the next moment are predicted, and dynamic compensation parameters are generated; the processed image is input into a convolutional neural network constructed by using a blueprint separable convolution, a residual attention module and a coordinate attention mechanism, and multi-scale feature extraction is performed; time alignment of the vibration data and the image data is performed, corresponding compensation parameters are generated, and meanwhile, spatial alignment and fusion of the two are performed to obtain fusion features; based on the fusion features, super-resolution reconstruction is performed on the image to generate a high-resolution image. Through technical fusion innovation, the recognition quality of image quality in a complex vibration scene is significantly enhanced.

The application provides a face super-resolution reconstruction method based on progressive training and face semantic segmentation, which mainly comprises the following steps: a light and efficient face super-resolution network is proposed, the network mainly comprises a residual aggregation module and three up-sampling modules; a progressive training method is used to make the model develop in three stages in one iteration training, and the trained model can perform two-fold, four-fold and eight-fold super-resolution image reconstruction on a low-resolution face image; a face semantic segmentation network is used to obtain face prior information, and a face segmentation loss is added in the model training to assist the network in generating a more realistic face structure. The application is improved based on the RFDN network, is suitable for super-low resolution face image input, can output three kinds of high-resolution reconstruction images with different magnification, and solves some defects of traditional models.

This invention provides an image super-resolution reconstruction method based on multi-scale residual feature fusion, comprising: preprocessing the image to obtain high- and low-resolution image pairs; constructing a multi-scale feature extraction module based on depthwise separable convolution to extract features from the preprocessed high- and low-resolution image pairs and output a feature map; constructing a residual feature fusion module to perform residual feature fusion processing on the output feature map; constructing an enhanced attention module to process the feature map after residual feature fusion processing; S5. Upsampling the feature map using an adaptive upsampling module to generate a super-resolution image; constructing a loss function module and processing the super-resolution image; S7. Constructing a super-resolution image reconstruction model based on multi-scale residual feature fusion and inputting the super-resolution image into the super-resolution reconstruction model for training; S8. Inputting the image to be processed into the super-resolution image reconstruction model based on multi-scale residual feature fusion for processing.

The present disclosure discloses a method and a system for wavelet domain-based normalizing flow super-resolution image reconstruction. The method includes constructing a training set and a normalizing flow model, wherein the normalizing flow model includes a plurality of levels, each of the plurality of levels including a squeeze layer, two types of conditional mapping layers, a split layer, an activation standard layer, and a quick response (QR) layer; determining a stable normalizing flow model through a wavelet transform, a reconstructed QR layer, and a T-distribution based on the normalizing flow model; determining a wavelet domain-based normalizing flow super-resolution model by adding a refinement layer based on the stable normalizing flow model; training the wavelet domain-based normalizing flow super-resolution model based on the training set; and reconstructing a super-resolution image based on a trained normalizing flow super-resolution model.

According to implementations of the subject matter described herein, a solution is proposed for super-resolution image reconstructing. According to the solution, an input image with first resolution is obtained. An invertible neural network is trained using the input image, wherein the invertible neural network is configured to generate an intermediate image with second resolution and first high-frequency information based on the input image, the second resolution being lower than the first resolution. Subsequently, an output image with third resolution is generated based on the input image and second high-frequency information by using an inverse network of the trained invertible neural network, the second high-frequency information conforming to a predetermined distribution, and the third resolution being higher than the first resolution. The solution can effectively process a low-resolution image obtained by an unknown downsampling method, thereby obtaining a high-quality and high-resolution image.

This application provides a method, apparatus, device, and storage medium for super-resolution image reconstruction. The method includes: inputting the APS image to be processed and the corresponding event image into a feature encoding network for feature extraction, and outputting shared features; inputting the shared features into a feature separation module of a feature separation and aggregation network for feature separation, obtaining shared features of the APS image and shared features of the event image, and then inputting them into a feature aggregation module of the feature separation and aggregation network for aggregation, outputting aggregated features; inputting the aggregated features into an image decoding network for feature decoding, and outputting a super-resolution APS image corresponding to the APS image to be processed. By implementing the scheme of this application, combining event camera signal-guided super-resolution image reconstruction, and learning the shared features of the APS image and event image based on the feature separation and aggregation network, abnormal noise can be effectively suppressed, and the clarity of texture and edges in the super-resolution image reconstruction result can be improved.

The present application relates to super-resolution image reconstruction technology, and discloses a mobile terminal image super-resolution method based on prompt calibration and multi-scale cascade, which comprises the following steps: acquiring a low-resolution image, using a blueprint separation convolution to perform shallow feature extraction on the low-resolution image to obtain an initial feature map; performing down-sampling on the initial feature map, and inputting the down-sampled feature map into a feature extraction network composed of a plurality of RMVP modules in cascade to perform deep feature extraction; performing up-sampling on the deep features output by the feature extraction network; performing feature fusion on the up-sampled features and the initial feature map through a jump connection; performing image reconstruction on the fused features to output a high-resolution image. The mobile terminal image super-resolution method based on prompt calibration and multi-scale cascade can achieve high image super-resolution performance while maintaining a lightweight structure to meet the real-time inference requirements of mobile terminals.

This invention discloses a super-resolution image reconstruction method and system for radiative transfer simulation. The method includes: constructing input features and labels through strict spatiotemporal pairing and multi-source data fusion; employing a residual learning network based on Unet++ to learn the residual from the coarse-resolution baseline brightness temperature to the pixel-level resolution target brightness temperature. The network undergoes multi-task learning modification and is trained within an effective mask using a combined loss function of SmoothL1 and SSIM to simultaneously constrain amplitude and structural errors; finally, systematic biases are eliminated through validation set-driven linear piecewise calibration. This invention overcomes the problems of over-smoothing, physical inconsistencies, and low computational efficiency inherent in traditional interpolation methods for radiative transfer simulation, achieving high-precision and high-efficiency super-resolution reconstruction and meeting the needs of near-real-time quantitative inversion of massive amounts of spaceborne data.

This invention relates to the field of image processing technology and discloses a method and system for super-resolution reconstruction of medical images. The method includes: constructing a multimodal medical image dataset comprising high-resolution images and corresponding low-resolution image pairs; decoupling and modeling organ structure and noise features using frequency domain analysis and Gaussian mixture models to establish a multimodal organ template library; performing organ identification and frequency domain feature matching on the input image, and retrieving the optimal noise prior from the multimodal organ template library; and injecting a noise prior guiding term during the reverse denoising process of the diffusion model to achieve anatomically adaptive super-resolution image reconstruction. This invention, through the deep integration of multimodal data-driven approaches, frequency domain statistical modeling, and diffusion models, achieves end-to-end optimization of medical image super-resolution reconstruction from noise decoupling to structurally adaptive super-resolution image reconstruction, improving the accuracy, efficiency, and robustness of medical image super-resolution reconstruction, and is applicable to complex clinical scenarios.

According to implementations of the present disclosure, a scheme for super-resolution image reconstruction is proposed. According to the scheme, an input image having a first resolution is obtained. A reversible neural network is trained with the input image, wherein the reversible neural network is configured to generate an intermediate image having a second resolution and first high-frequency information based on the input image, and the second resolution is lower than the first resolution. Subsequently, an output image having a third resolution is generated based on the input image and second high-frequency information subject to a predetermined distribution by using an inverse network of the trained reversible neural network, wherein the third resolution is higher than the first resolution. The scheme can effectively process a low-resolution image obtained by an unknown downsampling method, thereby obtaining a high-quality high-resolution image.