Abstract: The present application discloses a text image super-resolution method based on text assistance, including: obtaining a low-resolution text image to be reconstructed; inputting the low-resolution text image into a pre-trained text image super-resolution model, and determining a reconstructed text image based on an output of the text image super-resolution model; a method of constructing and training the text image super-resolution model includes: obtaining a text image dataset; and training the pre-constructed text image super-resolution model by using the text image dataset to obtain the trained text image super-resolution model. Compared to other ordinary super-resolution models, this text image super-resolution model fuses the text sequence features with the image texture features, and fully exploits and utilizes the text information in the low-resolution image, which can help to improve the quality of reconstructed text image.

Abstract: A method for processing low resolution degraded image, a system, a storage medium, and a device therefor are provided. The present disclosure adopts a dual branch processing model, which includes an image restoration branch and an image super-resolution branch. At the same time, a fusion module is used to fuse and learn image features from the two domains, thereby improving the problem of error accumulation and high computational cost caused by the two-stage processing method.

Abstract: The present application discloses a face image restoration method, a system, a storage medium and a device, the restoration model adopted in the above method starts from structured information of a face, generates a structured face graph based on features of the to-be-restored face image, and restores face image by the structured face graph and the decoder, which can solve the problem that it is difficult to capture and learn structured information based on the traditional convolution operation, and improve the indicators of the face restoration and enriches the visualization effect.

Abstract: A method for image motion deblurring, an apparatus, an electronic device and medium therefor are provided. The method includes obtaining a motion-blurred image to be deblurred; inputting the obtained blurred image into a pre-constructed and pre-trained image motion deblur model based on a multi-scale feature fusion module and a local channel information interaction module to obtain a clear image; wherein, the image motion deblur model is obtained through extracting characteristic information of different spatial scales and frequencies through the multi-scale feature fusion module for feature fusion, and exchanging local channel information of the fused feature map with local channel information in an one-dimensional convolution manner through the local channel information interaction module, and then training a dataset with a objective of minimizing a loss function based on adversarial loss and content loss.

Abstract: The present application discloses a text image super-resolution method based on text assistance, including: obtaining a low-resolution text image to be reconstructed; inputting the low-resolution text image into a pre-trained text image super-resolution model, and determining a reconstructed text image based on an output of the text image super-resolution model; a method of constructing and training the text image super-resolution model includes: obtaining a text image dataset; and training the pre-constructed text image super-resolution model by using the text image dataset to obtain the trained text image super-resolution model. Compared to other ordinary super-resolution models, this text image super-resolution model fuses the text sequence features with the image texture features, and fully exploits and utilizes the text information in the low-resolution image, which can help to improve the quality of reconstructed text image.