15 results about "Face hallucination" patented technology

The invention discloses a face super-resolution method and system based on a dual guide diffusion model, and the method comprises the steps: collecting low-resolution non-front face images and high-resolution front face images, and constructing a training data pair; after the low-resolution non-front face image is preliminarily restored, a rough front face image is obtained; mapping a face image of a pixel space to an implicit space, enabling a diffusion model to calculate in the implicit space, pre-training an unconditional diffusion model, taking a training result as an initialization parameter of the diffusion model, and freezing an encoder of a denoising network; extracting face priori features from the rough front face image, and capturing spatial and semantic correlation between the face priori features and de-noising features through a hybrid cross attention mechanism; face identity coding information is extracted, and a denoising network is embedded; and initializing a Gaussian noise map, carrying out continuous iterative denoising by using the trained diffusion model, mapping a denoising result in an implicit space to a pixel space, and finally reconstructing a high-resolution front face image.

The invention discloses a facial image super-resolution method based on facial structure prior fusion network guidance. The method comprises the following steps: constructing a facial image super-resolution network based on facial structure prior; preprocessing the public data set, and dividing the data set into a training set and a test set; training a face image super-resolution network based on face structure prior guidance; and super-resolution is carried out on the images in the test set. A face image super-resolution model based on a generative adversarial network is constructed, a generator part is composed of a rough super-resolution module, a key point heat map extraction module, a fine super-resolution encoder and a fine super-resolution decoder, and a discriminator is composed of a convolutional layer and a maximum pooling layer. According to the face super-resolution model constructed by the method, the problems of detail missing and low trueness of a result image under a relatively large magnification factor in an existing face super-resolution method are solved, so that a high-resolution face image with high trueness and a maintained structure can be reconstructed by the method.

The invention provides a face super-resolution method and system based on a double-branch dynamic collaborative network, and relates to the technical field of computer vision face image processing. The method comprises the following steps: extracting shallow layer features from a low-resolution face image through a first convolutional layer; performing global feature extraction on the shallow features through a plurality of multi-path attention aggregation modules to obtain face global features; local feature extraction is carried out on the shallow layer features through a plurality of multi-scale dynamic calibration modules, and face local features are obtained; splicing the face global features and the face local features through a dynamic feature fusion module to obtain fusion features; and performing reconstruction operation on the shallow features and the fusion features through a reconstruction module to obtain a super-resolution face reconstruction image. By fusing features of different hierarchical stages and different branches of the network, the capability of restoring fine facial details is improved.

The invention belongs to the technical field of image super-resolution reconstruction, and discloses a face super-resolution method and system based on frequency domain self-calibration feature enhancement, and the method comprises the steps: firstly constructing multi-scale input through interpolation; then extracting deep features by using an encoder comprising a CNN-KAN hybrid module (MCKM), and fusing originally input face features to enhance multi-scale information mining; then, a fast Fourier adjustment module (FFAB) is used to realize optimization of face detail information in a frequency domain; and finally, dynamically fusing the multi-level features through a decoder and outputting a multi-scale reconstructed image. Meanwhile, a loss joint optimization model is reconstructed in combination with a multi-scale spatial domain and a frequency domain. According to the method, feature representation is enhanced through the MCKM, the model is optimized in the frequency domain by means of the FFAB, and the encoder-decoder features are collaboratively optimized by using the adaptive fusion module (AFM), so that the detail reduction precision and the structure fidelity of face image reconstruction are remarkably improved while the calculation efficiency is kept.

The invention relates to the technical field of image processing and deep learning, and discloses a progressive face super-resolution method and system based on art priori knowledge, and the system comprises an image coding module which is used for carrying out the multi-scale feature extraction of an inputted low-resolution face image, and outputting potential features; the structure learning module is used for guiding to realize basic form restoration; the detail completion module is used for realizing texture recovery; the mask fusion module is used for realizing coarse and fine granularity feature integration; and the super-resolution decoder is used for receiving the fusion features and reconstructing a high-resolution face image. According to the method, the fidelity of facial key features can be effectively enhanced, the overall image definition is improved, the consistency and naturalness of local features are ensured, the unreal and unstable phenomena caused by dependence on speculation generation in the prior art are remarkably improved, and therefore the reconstruction quality and application reliability of the face image are improved.

The present invention provides a face super-resolution method and system based on a meta-learning optimization network. The meta-learning optimization network connects the intrinsic features of multiple degradation processes learned in the training phase and uses them in the unknown degradation process of the test, thereby solving the technical problem that the face image reconstruction performance of the current face super-resolution reconstruction algorithm has certain limitations when the image degradation type is unknown, and improving the reconstruction performance of the face image. The present invention uses meta-learning to quickly learn the characteristics of a new task, so that the network can achieve good super-resolution performance on the new test task through empirical learning of the training task. The present invention can address the problem of reduced super-resolution performance of face images when the degradation process is unknown, and generate higher quality high-resolution face images.

The application discloses a kind of based on multi-level supervision attention mechanism super network and random erasing data enhancement's occluded face image super resolution method, comprising: face region cutting, random erasing face content and size adjustment are carried out to public face dataset, construct low-resolution occluded face / high-resolution unoccluded face image pair dataset, and multiple data enhancements are carried out to training set;Multi-level supervision attention mechanism face image super resolution network based on face structure priori guide is constructed;Multi-level supervision attention mechanism face image super resolution network based on face structure priori guide is trained on training set;Low-resolution occluded face image in test set is carried out super resolution.This application overcomes the existing face super resolution method in processing accompanying occlusion input low-resolution face image when result image structure distortion, the problem of detail accompanying artificial artifact, can reconstruct structure complete, accurate, high-resolution face image with high fidelity.

The application discloses a non-aligned face image super-resolution method based on a layered structure perception space conversion network, and comprises the following steps: performing random angle rotation, face region cutting and size adjustment on a public face dataset, constructing a non-aligned low-resolution face / aligned high-resolution face image pair dataset, and dividing a training set and a test set; constructing a non-aligned face image super-resolution model based on a layered structure perception space conversion network; training the non-aligned face image super-resolution model based on the layered structure perception space conversion network on the training set; and performing super-resolution on non-aligned low-resolution face images in the test set. The non-aligned face image super-resolution model constructed by the application can overcome the problems of structure distortion and details accompanying artificial artifacts of a generated face image under the influence of a non-aligned factor in an existing face super-resolution method, so that the application can reconstruct an aligned and high-fidelity high-resolution face image.

The present invention relates to the fields of image processing and deep learning technology, and discloses a progressive facial super-resolution method and system based on artistic prior knowledge. The system comprises: an image encoding module for extracting multi-scale features from an input low-resolution facial image and outputting latent features; a structure learning module for guiding basic morphological restoration; a detail completion module for texture restoration; a mask fusion module for integrating coarse and fine granularity features; and a super-resolution decoder for receiving the fused features and reconstructing a high-resolution facial image. The present invention can effectively enhance the fidelity of key facial features, ensuring the consistency and naturalness of local features while improving overall image clarity. This significantly improves the unreality and instability caused by prior art techniques that rely on speculative generation, thereby improving the reconstruction quality and application reliability of facial images.

The present invention discloses a face super-resolution method and system based on a dual guided diffusion model. The method comprises the following steps: collecting low-resolution non-frontal face images and high-resolution frontal face images to construct training data pairs; preliminarily restoring the low-resolution non-frontal face images to obtain rough frontal face images; mapping the face images in pixel space to implicit space, enabling a diffusion model to be calculated in the implicit space, pre-training the unconditional diffusion model, using the training results as initialization parameters of the diffusion model, and freezing the encoder of a denoising network; extracting facial prior features from the rough frontal face images, and capturing the spatial and semantic correlations between the facial prior features and denoising features through a hybrid cross-attention mechanism; extracting facial identity coding information and embedding it into a denoising network; initializing a Gaussian noise map, iteratively denoising using the trained diffusion model, mapping the denoising results in the implicit space to the pixel space, and finally reconstructing a high-resolution frontal face image.

A face super-resolution method based on visual language prior art of the application comprises the following steps: step one, sending a low-resolution face image into a pre-trained visual-language large model to extract a visual-language multi-element representation; step two, constructing a visual-language prior art auxiliary face super-resolution network to fuse visual-language prior art information; step three, sending the low-resolution face image and the visual-language multi-element representation extracted in step one into the network in step two to obtain a super-resolution result and a restored high-quality face image. Compared with existing mainstream face super-resolution methods (FSRNet, DIC, SISN, SFMNet, FaceFormer and WFEN), the face image restored by the application performs better in both objective evaluation indexes and subjective visual quality.

The invention discloses a human face super-resolution method based on visual language prior, and the method comprises the following steps: 1, sending a low-resolution human face image into a pre-trained visual-language large model, and extracting visual-language multivariate representation; 2, constructing a visual language prior assisted face super-resolution network, and fusing visual language prior information; and step 3, sending the low-resolution face image and the visual language multivariate representation extracted in the step 1 into the network in the step 2 to obtain a super-resolution result, and obtaining a recovered high-quality face image. Compared with the existing mainstream face super-resolution method (FSRNet, DIC, SISN, SFMNet, FaceFormer, WFEN), the face image recovered by the method provided by the invention is better in both objective evaluation index and subjective visual quality.

The present invention discloses a human face super-resolution method based on multi-scale attention residual and equivariant mapping, firstly, the shallow features of the low-resolution human face side image are extracted through the convolution layer; then, the shallow features are input into the feature extraction subnet, and the deep features are obtained through n multi-scale attention residual modules; the deep features obtained are further input into the residual equivariant mapping module, and the deep features are combined with the residual features of the fused yaw coefficient in the deep representation feature space, and the feature vector of the side face is transformed into the same feature vector space as the front face; finally, a high-resolution human face front image is obtained through the reconstruction module. The present invention is suitable for human face recognition, and no longer overly relies on a large number of front and side face data pairs. For the input low-resolution side face image, a front face image with high definition and more realistic texture can be reconstructed.

The invention relates to a face super-resolution method based on text information guidance. The method comprises the following steps of 1, text description generation, wherein text description is generated through a multi-modal large language model; step 2, potential space coding: mapping a low-resolution face image and text description to a potential feature space, and performing compact representation on the image by using a pre-trained encoder; step 3, text information fusion: embedding the generated text description into a visual feature processing process through a method based on a cross attention mechanism to form text-visual joint representation; 4, a residual diffusion generation module: in the potential space, realizing the generation of a low-resolution image to a high-resolution image through a Markov chain of residual connection; and step 5, text perception loss optimization: optimizing image quality and semantic consistency of a generated result by minimizing a potential space recovery error and a text consistency error. Compared with other generative models, the TFSR has the minimum parameter quantity, the highest sampling efficiency and the optimal FID score.

The present invention provides a face super-resolution method and system based on a convenient alternating projection network. Combined with a convenient alternating projection unit, the network enables each high-resolution spatial feature to efficiently interact and fuse across levels, thereby improving the network's feature expression capabilities. The convenient alternating projection network proposed in the present invention achieves performance and visual quality similar to that of a non-compact model, and significantly reduces parameters and testing time, thereby achieving the function of improving the reconstruction efficiency and performance of super-resolution face images. The present invention solves the technical problems of the current non-compact face super-resolution reconstruction algorithm, such as the existence of additional redundant features, low algorithm efficiency, and certain limitations in the reconstruction performance of lightweight networks. The network efficiency and performance proposed in the present invention are superior to other latest face image super-resolution algorithms, and the generated high-resolution face images are of higher quality.