459 results about "Image domain" patented technology

The invention discloses a deep forgery detection method based on a visual language model, and relates to the field of image forensics. The deep forgery detection method based on the visual language model aims to combine multi-source information to improve the discrimination capability of the model on a real image and a generated image. The method comprises the following steps: firstly, extracting image features through an image encoder of a pre-trained CLIP model; meanwhile, a frequency domain enhanced counterfeit perception adapter is embedded in the image encoder to mine potential anomalies of counterfeit images in the image domain and the frequency domain. Secondly, a manual feature extraction module is provided, discriminative low-dimensional features are extracted from the four aspects of the edge, the texture, the frequency and the symmetry of the image, and the discriminative low-dimensional features are used as auxiliary information input in the forgery detection process, so that the robustness and the interpretability of the model are improved; meanwhile, the text cue words are converted into feature vectors through a text encoder of a pre-training CLIP model; and finally, the model predicts a forgery score by calculating the cosine similarity between the image features and the text features so as to realize the discrimination of the authenticity of the image. According to the method, the problem that the detection capability of the model on the cross-dataset is insufficient is effectively improved.

The invention relates to an underwater image enhancement method based on wavelet Mama, which aims at the problems of color shift, low contrast ratio and fuzzy details of an underwater image, extracts shallow layer features of an underwater low-quality image, inputs the shallow layer features into a multi-scale coding network, and performs joint enhancement on low-frequency color information and high-frequency detail features by using a wavelet Mama unit. Global semantic features are obtained by combining down-sampling layer-by-layer compression, and global modeling of color offset correction and contrast enhancement is completed; the spatial resolution is recovered through up-sampling, and reconstruction and enhancement of texture details and color information are realized in combination with jump connection and a wavelet Mama unit; and finally, the features are mapped to an image domain and fused with input image residual errors, and an enhanced underwater image with natural color, clear texture and balanced contrast is generated. According to the method, frequency domain-space domain joint feature modeling is realized through the wavelet Mama unit, and the color authenticity, the structural definition and the detail perceptibility of the underwater image are remarkably improved.

The invention relates to the technical field of constructional engineering, and discloses a concrete crack intelligent identification and analysis platform based on image and point cloud fusion, the platform operates a concrete crack intelligent identification and analysis method, and the method comprises the following steps: S1, synchronously collecting image data and point cloud data of a concrete structure in the same scene; s2, establishing a unified world coordinate system and generating a depth map corresponding to the image; s3, generating image domain crack candidates; s4, generating a depth domain crack candidate; s5, performing weighted fusion on the image domain crack candidate and the depth domain crack candidate to generate a fusion crack response; s6, extracting a crack skeleton; s7, obtaining a crack three-dimensional model; and S8, selecting an optimal view angle to trigger re-acquisition of the crack area. Through a closed-loop feedback mechanism, an optimal view angle is selected for re-acquisition by calculating a comprehensive utility value after preliminary acquisition, so that information insufficiency caused by illumination, angle or sparse data is effectively made up.

The invention relates to a remote sensing image domain generalization semantic segmentation method based on multi-scale instance decoupling, which relates to the technical field of remote sensing image interpretation and comprises the steps of multi-scale instance coding, domain invariant instance decoupling and domain generalization semantic decoding. Image features in a segmented backbone network are used as input, and a multi-scale instance coding module effectively senses feature responses of cross-domain instances through deep convolution, multi-scale cooperation and multi-path sensing and is not affected by scale differences, spatial deformation and noise; designing a domain invariant instance decoupling module to decouple the cross-domain style from the image representation; and finally, the decoupled features are input into a domain generalization semantic decoding module so as to predict a segmented image on a target domain which is not seen, and the segmentation method can generalize various remote sensing images which are not seen so as to obtain better segmentation capability.

The invention relates to the technical field of computer vision and artificial intelligence, and discloses a computer vision-based hundred-million-pixel panoramic situation awareness method and system, and the method comprises the steps: synchronously collecting original local field-of-view images through multiple cameras; independently executing target detection and semantic segmentation in an original image domain; constructing local-to-global coordinate mapping based on the calibration parameters; projecting the target features to a global coordinate system and detecting a cross-view-field target; extracting multi-view-field texture, edge and color features, and performing weighted fusion according to a visible area; according to the method, through identification preposition and semantic level feature fusion, geometric splicing distortion is avoided, the cross-boundary identification precision is improved, the calculation load is reduced, target continuous tracking and situation real-time output in a high-density scene are realized, and the method has high engineering suitability and industrial popularization value.

The invention provides a magnetic resonance image reconstruction method and device, a model training method and device, equipment and a medium. The method comprises the following steps: inputting acquired to-be-reconstructed under-sampling k-space data into a pre-constructed image reconstruction model, and carrying out continuous k-space reconstruction processing for multiple times to obtain a magnetic resonance reconstruction image; wherein the k-space reconstruction processing in the image reconstruction model comprises the steps of performing frequency domain restoration on input k-space data based on a Transformer encoder introduced with symmetric weight, performing image domain signal restoration on the k-space data after frequency domain restoration, and performing data consistency processing on the k-space data after image domain signal restoration. According to the method, the image reconstruction model is constructed in an end-to-end expansion training optimization mode, so that the frequency domain and image domain information of the undersampled data can be fully utilized, the accuracy of image reconstruction is effectively improved, the computing resource overhead and the time cost can be effectively reduced, and the high efficiency of the reconstruction process is ensured.

The invention discloses a privacy protection constrained passive unsupervised graph domain adaptive mechanical fault diagnosis method and device, and the method comprises the steps: inputting a mechanical vibration signal into a pre-trained mechanical fault diagnosis model, and outputting a diagnosis result; the mechanical fault diagnosis model is obtained by training a passive domain multi-scale image domain adaptive network by adopting training data, and the passive domain multi-scale image domain adaptive network comprises a multi-scale feature extractor module, a topological graph convolution module and a feature classifier module which are connected in sequence; the training process comprises two stages of performing pre-training by using source domain data and performing unsupervised domain adaptive training by using target domain data, and a loss function of the training process comprises a label smooth cross entropy loss function for the pre-training process. And the unsupervised domain adaptive loss function used for the unsupervised domain adaptive training process comprises neighbor clustering loss, regularization loss and label prediction diversity loss. The objective of the invention is to realize complete representation and generalization of fault features by deeply mining topological structure information, and optimize decision boundaries to significantly improve inter-domain alignment precision in a passive domain adaptive process, thereby solving the problem of cross-working condition diagnosis of mechanical equipment under data privacy protection constraints.

The invention relates to a remote sensing image domain adaptive classification method based on high-order prototype guidance. The method comprises the following steps; constructing a cross-scene remote sensing image classification data set; constructing a high-order category prototype generation network based on feature interaction; constructing a high-order causal prompt generation network based on prompt learning; and training and testing a remote sensing image domain adaptive classification method based on high-order prototype guidance. Compared with the prior art, through the cross-scene remote sensing image classification network, the problem that in an existing remote sensing image classification task, due to the fact that style differences exist between different satellite remote sensing images, a source domain training model is difficult to migrate to a target domain data set is solved. In addition, key causal features are decoupled by adopting a prompt learning method based on high-order prototype guidance, so that an innovative method for effectively migrating a model trained on source domain satellite data to target domain satellite data is realized.

The invention discloses a hyperbolic space open set semantic segmentation method based on image language supervision. The method comprises the following steps: constructing a hyperbolic space training framework of an image domain and a language domain, and training the hyperbolic space training framework; constructing an open scene semantic segmentation model according to the training framework of the image domain and the language domain; and inputting a to-be-segmented image into the trained open scene semantic segmentation model to obtain a semantic segmentation result. According to the method, the characteristic that the hyperbolic space can encode a hierarchical structure is utilized, and an image and text encoder of a large-scale language vision model is directly migrated from an image-level classification level to a pixel-level classification level in a manner of finely adjusting the large-scale language vision model in the hyperbolic space; on the basis that the open scene capability is reserved, the large-scale language vision model has the semantic segmentation capability.

The invention relates to the technical field of image classification, and discloses a hyperspectral image domain generalization classification method, system and device and a medium. The method comprises the following steps: acquiring a plurality of hyperspectral images of different ground feature types; obtaining a mean value and a variance of each hyperspectral image in a channel dimension, performing random disruption, and determining a spectral variation parameter of the hyperspectral image according to the mean value and the variance before and after random disruption so as to generate a corresponding spectral variation image; separating the center and the background of each hyperspectral image to obtain respective center image and background image, randomly disorganizing the center image and the background image, and generating a corresponding spatial variation image by combining the disorganized center image and background image of each hyperspectral image; a hyperspectral image classification model is obtained by training an enhanced hyperspectral image obtained by fusing a plurality of corresponding spectral variation images and spatial variation images, so that ground feature classification is performed on a to-be-classified hyperspectral image, and the domain generalization performance of the hyperspectral image classification model is improved.

The invention provides an unsupervised CT image denoising method and device based on multi-mode large model text prompt. The method comprises the following steps: performing preliminary denoising on an original CT image by using a pre-training coding and decoding model to generate a first denoised CT image; inputting the first de-noised CT image and a preset text into a multi-modal visual language large model to generate quality cues and detail cues about the first de-noised CT image; denoising the first denoised CT image again by using a trained generation diffusion model according to the quality cue words and the detail cue words to generate a second denoised CT image; inputting the second de-noised CT image serving as an initial de-noised CT image into a three-domain consistency iteration framework for fidelity, and obtaining a final de-noised CT image when an iteration stop condition is met; wherein the three domains comprise an image domain, a projection domain and a wavelet domain. The method can be conveniently applied to various noisy CT images by utilizing the emergence capability and the characterization capability of a large model and combining and designing a denoising frame.

The present application relates to an image processing method, apparatus, computer device, and storage medium. The method includes: inputting a first image in a first image domain into a to-be-trained image processing model to obtain a second image in a second image domain; acquiring a first image feature corresponding to the first image and a second image feature corresponding to the second image; obtaining a first model loss value based on a target feature correlation degree between the first image feature and the second image feature; acquiring a first eigenvalue distribution corresponding to the first image feature and a second eigenvalue distribution corresponding to the second image feature; calculating a distribution correlation degree between the first eigenvalue distribution and the second eigenvalue distribution, and obtaining a second model loss value based on the distribution correlation degree, where the second model loss value has a negative correlation relationship with the distribution correlation degree. Using this method can improve the image processing effect. The image processing model in the present application can be an artificial intelligence-based neural network model, and an artificial intelligence cloud service is provided based on this model.

The invention discloses an emotion feature fused bimodal forged information detection method, which comprises the following steps of: extracting emotion features, respectively extracting emotion categories of a text and an image through a large language model, respectively giving emotion intensity of the categories, and scoring the emotion; analyzing the emotion consistency of the mixed modal features of the text and the image; the hybrid expert network calculates domain probabilities to which a text mode, an image mode and a hybrid mode belong respectively through a text domain encoder, an image domain encoder and an image text hybrid feature domain encoder based on domain class processing features; the hybrid expert network learns feature information of a text mode, an image mode and a hybrid mode according to the domain information; the AdaIN network adjusts modal distribution, the AdaIN network adjusts feature distribution according to contribution of each modal, the adjusted features are fused and input into the classifier, a final classification result is obtained, and the information detection accuracy is greatly improved.

The invention provides a spectrum self-attention and cloud mask guided remote sensing image cross-modal cloud detection method. According to the method, the spectral self-attention sub-network in the remote sensing image cross-modal cloud detection network is constructed, and the dependency relationship between different spectral bands of the remote sensing image is effectively mined and utilized, so that the recognition accuracy of cloud region features is improved. According to the method, cross-modal migration from the source domain image to the target domain image is realized by using the migration sub-network of the unsupervised image domain in the remote sensing image cross-modal cloud detection network, and the multi-spectral image with the target domain features is generated, so that the defect of the source domain image in spectral information is overcome, the false detection and omission ratio is effectively reduced, and the detection efficiency is improved. And the robustness of cloud detection of the model in a complex scene is enhanced.

The invention discloses a sparse view angle 3D-DSA reconstruction method based on a three-dimensional Poisson generative model. The method comprises the following steps: obtaining pairing data of a sparse view angle 2D-DSA projection drawing and a 3D-DSA reconstruction image; extracting features of a projection image by using a projection domain encoder, then converting features of two-dimensional projection into a three-dimensional image domain according to a geometrical relationship of the cone beam CT, and then obtaining a prior image by using an image decoder; the method comprises the following steps: constructing a three-dimensional Poisson generation model, adding noise to a three-dimensional image patch in a training stage to obtain a disturbance image, outputting a network reconstruction image by taking a prior image as a condition and the three-dimensional image patch before noise addition as a target image, and calculating loss of the output image and the target image to update network parameters; the mean square error loss and the mean square error loss of the maximum intensity projection images of the three orthogonal planes are used during loss calculation; in the sampling stage, random noise is used as input, a prior image is used as a condition, noise of a noise image is continuously denoised within a limited step length, and finally a reconstructed 3D-DSA image is obtained.

The invention provides a PCB surface defect detection method and system based on double-layer SAM model collaboration. The method comprises the steps that industrial image domain features and image block features of a to-be-detected PCB image are acquired; performing global semantic segmentation on the PCB image to be detected by adopting an upstream SAM model, extracting a plurality of semantic masks, and representing the semantic masks as weighted semantic mask features; on the basis of the image block features and the weighted semantic mask features, calculating the Euclidean distance between the to-be-detected PCB image and a core memory bank constructed on the basis of the normal PCB image, and obtaining a multi-level anomaly score; fusing the multi-level anomaly scores to obtain a rough anomaly score graph; a downstream SAM model is adopted, the rough anomaly score graph is used as a prompt, and an effective segmentation area mask is obtained in combination with a normal PCB structure learned by the upstream SAM model; and based on the effective segmentation region mask, pixel-level abnormal region refinement is performed on the rough abnormal score map to obtain a fine abnormal score map for category judgment and positioning of defect detection. According to the invention, high-precision defect detection in a small sample scene is realized.

The invention relates to the technical field of medical image processing, in particular to a CT radiography intelligent imaging method and system based on DCT-GAN multi-scale fusion, and the method comprises the steps: obtaining a preprocessed target NCCT-CTA image data set; dividing according to a preset proportion based on the target NCCT-CTA image data set to obtain a training set and a verification set; the training set and the verification set are input into an initial generative adversarial network based on DCT-GAN multi-scale fusion for model training, in the training process, a virtual CTA image is generated by a generator, an input original CTA image and the virtual CTA image are discriminated in an image domain and a frequency domain subjected to discrete cosine transform processing by a discriminator, and a model is obtained. Determining a network overall loss function based on the similarity difference between the image domain and the frequency domain and the discrimination difference of the discriminator on the two domains; and inputting the preprocessed real-time NCCT image into the trained target generative adversarial network to generate a high-quality and high-resolution virtual CTA image.

The invention discloses an absolute quantitative correction method and device for medical imaging equipment, and relates to the technical field of nuclear medical imaging equipment correction. When the method is executed, the actual activity concentration of the calibration die body injected with the first known tracer activity serves as the first activity concentration; then, the calibration die body injected with the first known tracer activity is collected in medical imaging equipment, and the activity concentration in a first image domain obtained through calculation serves as second activity concentration; and finally, calculating a correction factor based on the first activity concentration and the second activity concentration, wherein the correction factor is used for correcting absolute quantification of the medical imaging equipment. Therefore, the correction factor is calculated by comparing the activity concentration calculated by the medical imaging equipment with the actual activity concentration of the calibration die body, so that the medical imaging equipment can accurately adjust the measurement result according to the correction factor during subsequent absolute quantitative analysis; the effect of improving the absolute quantification accuracy of the medical imaging equipment is achieved.

The invention discloses a medical MRI image denoising method, device, system and equipment, a medium and a product, and relates to the technical field of image enhancement. The method comprises the following steps: firstly, converting a medical MRI image signal into an image domain to obtain three-dimensional image data, then selecting three-dimensional image sub-blocks, searching M three-dimensional image similar sub-blocks which are most similar in a predefined neighborhood, and then carrying out vectorization straightening processing and superposition on the sub-blocks to obtain an original matrix Y; then assuming that an original matrix Y is equal to X + N + S and solving the minimization objective function to obtain a de-noising matrix X, reconstructing according to the de-noising matrix X to obtain new sub-blocks corresponding to the sub-blocks, and replacing the new and old sub-blocks to obtain new three-dimensional image data subjected to image de-noising. Therefore, the problem that the denoised image is blurred when the prior art is applied to MRI denoising can be solved, the quality of the medical MRI image is improved, and subsequent image analysis is facilitated.

The invention discloses a multi-contrast magnetic resonance image reconstruction method and device based on detail contour feature perception. The method comprises the following steps: acquiring a target modal initial image and an auxiliary modal initial image; constructing an iterative network formed by alternately cascading image domain reconstruction units and data consistency layers, wherein each image domain reconstruction unit comprises an encoder and a decoder; in the first iteration, target modal initial images and auxiliary modal initial images are spliced and then input, an encoder extracts shared features firstly, then global contour features and high-frequency detail features are separated in parallel, and potential features are obtained through collaborative fusion; the decoder takes the potential features as input and outputs an image domain preliminary reconstruction result; the data consistency layer transforms the preliminary result into a k space, performs consistency correction on the preliminary result and a target modal sampling point, and then inversely transforms the preliminary result back to an image domain to complete one iteration; and splicing the current output and the auxiliary modal initial image again, inputting the spliced image into a next round of iteration, and repeating the process until a preset number of times to obtain a final target modal magnetic resonance image.

The invention relates to a cone beam CT imaging scanning parameter determination method and imaging equipment, and the method comprises the steps: designing an optimization model for a microgravity environment, carrying out the optimization processing of the cone beam CT imaging scanning parameters based on a pre-constructed optimization model with the maximum imaging performance as a target, and obtaining the optimal parameters of the cone beam CT imaging. Wherein optimization problem modeling and solving are sequentially carried out in a projection domain and an image domain so as to obtain target scanning parameters meeting constraint conditions, and according to the technical scheme of the invention, aiming at factors such as scanning space and energy consumption in space, requirements of high spatial resolution, compact physical size of equipment, high image quality and low scanning dose are met, and the method and the device have the advantages of being high in efficiency and high in reliability. The method is applied to cone beam CT imaging in the space microgravity environment, and the comprehensive requirements of scientific research, health guarantee and medical service in the space environment are met.

The invention discloses a low-dose CT reconstruction method and device, and relates to the technical field of image processing, and the method comprises the steps: obtaining a low-dose CT image; the trained deep learning network is adopted to process the low-dose CT image in a projection domain, a chordal graph domain and an image domain; in the projection domain, processing the low-dose CT image to obtain updated projection data, in the chordal graph domain, processing the updated projection data to obtain updated chordal graph data, and in the image domain, processing the updated chordal graph data to obtain a reconstructed low-dose CT image; wherein the trained deep learning network takes data of a preset category as a training data set, the initial deep learning network is trained, intermediate supervision is performed in the training process, and the trained deep learning network is finely adjusted to obtain the deep learning network. According to the invention, good reconstruction of the low-dose CT image can be realized.

The invention discloses an unsupervised image conversion imaging method based on the Schrodinger bridge theory, and the method comprises the following steps: 1, constructing an image domain-domain conversion frame based on the Schrodinger bridge theory, simulating a smooth evolution path from a source domain image to a target domain image through a time condition generator, and forming antagonism training in combination with a discriminator; 2, integrating a saliency content guide constraint, a global feature consistency constraint and a contrast learning constraint in the conversion framework so as to enhance semantic content retention and detail generation capability; 3, establishing a composite loss function, and carrying out weighted combination on Schrodinger bridge path loss, adversarial loss and each auxiliary constraint loss for guiding model optimization; and 4, training a time condition generator and a discriminator through an optimization algorithm, and carrying out stable and high-fidelity target domain conversion on the source domain image by utilizing the generator after training is completed. According to the method, the stability of model training and the quality and fidelity of the generated image are remarkably improved.

The invention discloses an HDR video reconstruction method based on brightness alignment, and the method comprises the steps: carrying out the gamma correction of continuous frames of an input LDR video, generating an HDR image domain, and splicing the HDR image domain with an original frame to form an input tensor; constructing a brightness alignment network model, and generating brightness alignment features through a brightness attention module; generating detail features through a detail synthesis module; performing dynamic weighted fusion on the brightness alignment features and the detail features through an adaptive mixing layer, and performing up-sampling to obtain final alignment features; and finally, generating an HDR video frame by fusing the final alignment features. Through collaborative optimization of the brightness attention mechanism and the time domain alignment module, the problem of brightness inconsistency in a motion scene is effectively solved, and the HDR reconstruction quality is remarkably improved.

The invention provides a medical magnetic resonance image reconstruction method and system, and belongs to the technical field of image reconstruction, and the method comprises the steps: obtaining sampling coding and under-sampling k space data; processing the under-sampled k space data through inverse Fourier transform, and converting the k space data into an image domain to obtain under-sampled image data with blurring or artifacts; carrying out preliminary restoration on the undersampled image data based on sampling coding, further applying data consistency operation on the image to obtain a rough image, and taking the rough image as an image condition vector after underspace coding; acquiring a text serving as a cue word, inputting the text into a text encoder, and encoding the text into a high-dimensional semantic embedding vector by the text encoder; and inputting the high-dimensional semantic embedding vector and the image condition vector into a reconstruction model based on the correction flow to predict a full-sampling MRI image, and generating a final reconstructed MRI image.

The invention discloses a composite material defect modeling method based on physical modeling generation and meta-learning migration, which comprises the following steps: S10, starting from a forming mechanism of a fatigue crack of a composite material, establishing a morphological function of a crack morphological simulation model based on a mechanical principle for simulating a spatial evolution process of a defect; s20, embedding the morphological function as a regular term into a generator loss function of a generative adversarial network, and generating a high-simulation pseudo-defect image; s30, introducing a transfer learning mechanism, constructing a model-irrelevant meta learning training strategy, and realizing rapid learning of the model on a new task through a two-layer nested optimization strategy; s40, introducing a domain adaptive residual module in the model training process to enhance the feature alignment capability of a cross-batch image domain; and S50, training the model by using a real sample and a synthetic defect sample in a mixed manner.

The invention discloses an enhanced visual positioning method and system based on cross-domain three-dimensional Gaussian sputtering, and the method comprises the steps: generating a second image in a second image field corresponding to a first image in a first image field through an image editing model, so as to obtain an extended data set of a first image data set in the first image field; performing sputtering model fine tuning on the three-dimensional Gaussian sputtering model adaptive to the feature distribution condition of the second image field according to the corresponding first image and the second image in the extended data set; and generating a positioning image training set for the image positioning model according to the three-dimensional Gaussian sputtering model subjected to fine tuning of the sputtering model, and performing positioning model training on the image positioning model according to the positioning image training set to obtain a trained image positioning model. According to the method, the visual positioning system has higher adaptability and generalization ability, the phenomenon that the positioning precision is reduced is reduced, and the requirements for stability and high precision of cross-domain visual positioning are met.

The invention relates to the technical field of ground penetrating radar technology, in particular to a GPR reinforcing steel bar interference suppression method based on an unsupervised generative network, which comprises the following steps: S1, acquiring and preprocessing GPR data, including a first type of images including void defect echoes and reinforcing steel bar clutters and a second type of images only including void defect echoes; s2, an unsupervised generative network model is constructed, the model comprises a variational auto-encoder and a generative adversarial network (GANs), and image inter-domain mapping is realized by sharing a potential space; s3, introducing an attention module into the network model, and optimizing key information extraction and processing; and S4, training and verifying the model by using simulation data and real data, inhibiting reinforcement interference and reconstructing a cavity signal. According to the method, GPR multi-reflection interference signals generated by reinforcing steel bars are effectively eliminated, and cavity defect signals in the image are accurately reconstructed, so that radar data can be automatically explained, and the accuracy of an explaining result is improved.

Techniques are disclosed relating to exchange of images in networked computing applications. In particular, the disclosure relates to exchange of gain curves that are used to represent imaging and / or video in such applications. A gain curve may define a mathematical transformation that relates values from a source image domain to a destination image domain. The image and its associated gain curve(s) may be published to destination devices for consumption. When a destination device consumes the image, the destination device may apply a transform to source image content according to the gain curve(s) published with the image. For example, the destination device may apply a gain curve to an associated image directly, or it may derive another transform from the gain curve and additional information known to the destination device.

Disclosed are methods, systems, and other implementations, including a unified complex-valued deep learning framework (AFT-Net), which determines the k-space domain to image domain mapping for MRI reconstruction and allows incorporation of existing deep learning models. Embodiments include a computer-implemented method for reconstructing images that includes obtaining resonance (MR) k-space data resulting from a scan performed by an MRI scanner on tissue of a patient, with the MR k-space data including complex-valued data, and processing, by a complex-valued machine learning image reconstruction system, the complex-valued data of the MR k-space data to generate image data representing features of the MR k-space data. The processing may include performing data filtering operations, by one or more machine learning filter blocks implemented according to a CU-Net architecture realized using one or more convolutional neural networks (CNN) configured for complex data processing, on data that is based on the k-space data.