The invention discloses a face image super-resolution reconstruction method based on a discriminable attribute constraint generative adversarial network, and belongs to the field of digital images/video signal processing. The method comprises the following steps: firstly, designing a processing flow of face detailed information enhancement; secondly, designing a network structure according to theflow, and acquiring an HR image from an LR image through the network; and lastly, performing face verification accuracy evaluation on the HR image through a face recognition network. Through adoptionof the method, enhancement including LR face image detailed information can be completed, and the accuracy of face verification is increased. Secondly, the generative network completes compensation ofimage high-frequency information firstly, then completes image amplification by subpixel convolution, and finally completes stepwise image amplification through a cascade structure, thereby completing enhancement of image detailed information. An attribute constraint module are trained together with a perception module and an adversarial model in order to perform fine adjustment of the performance of a network reconstructed image. Finally, a reconstructed image of the generative network is input into a face verification network, so that the accuracy of face verification is increased.

Face image super-resolution reconstruction method based on discriminable attribute constraint generative adversarial network

The invention provides an image inpainting method and system based on antagonistic generation neural network, which comprises the following steps: firstly, constructing a self-encoder convolutional neural network (including encoder and encoding discriminator), a decoder (generator) convolutional neural network, a discriminator convolutional neural network, a global discriminator, and a local discriminator; Then, different loss functions are constructed for the five networks, and the whole network is trained by step-by-step training. Finally, when the network training is completed, the defect image is put into the network for repairing, and the result graph generated by the decoder (generator) is the final repairing result graph. The invention has the advantages that: the potential constraint of the image is kept and the image is thinned; an end-to-end image restoration network is implemented. The dependence of the repair network on the missing position mask information of the image iseliminated. The robustness in practical application is improved.

Image inpainting method and system based on antagonistic generation neural network

The invention discloses a license plate super-resolution processing method and system based on deep learning. The method includes the following steps: acquiring a series of images containing license plate information from an original compressed surveillance video; tracking a target of interest in the images; selecting a plurality of points of interest, intercepting images with the points of interest as the center, and registering the images; and using a deep learning training base to get a corresponding deep network weight, and carrying out super-resolution processing on the multiple frames of images after registration with use of the deep network weight to get a clear high-definition license plate I. The intrinsic relationship between degraded characters and clear characters in a highly compressed surveillance video is learned through joint optimization using a super-resolution deep network model and a gradient guide network model. Super resolution and a de-blocking effect are achieved through a common deep network. The resolution of degraded character is improved, and the block effect is removed. The problem that the license plate characters in a highly compressed surveillance video cannot be seen clearly is solved.

License plate super-resolution processing method and system based on deep learning

The aim of operation chain detection for a given manipulated image is to reveal the operations involved and the order in which they were applied, which is significant for image processing and multimedia forensics. Currently, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">all existing approaches simply treat image operation chain detection as a classification problem and consider only chains of at most two operations. Considering the complex interplay between operations and the exponentially increasing solution space, detecting longer operation chains is extremely challenging. To address this issue, in this work, we devise a new methodology for image operation chain detection. Different from existing approaches based on classification modeling, we strategically conduct operation chain detection within a machine translation framework. Specifically, the chain in our work is modeled as a sentence in a target language, with each possible operation represented by a word in that language. When executing chain detection, we propose first transforming the input image into a sentence in a latent source language from the learned deep features. Then, we propose translating the latent language into the target language within a machine translation framework and finally decoding all operations, arranged in order. Besides, a chain inversion strategy and a bi-directional modeling mechanism are developed to improve the detection performance. We further design a weighted cross-entropy loss to alleviate the problems presented by imbalance among chain lengths and chain categories. Our method can detect operation chains containing up to seven operations and obtains very promising results in various scenarios for the detection of both short and long chains.

Image Operation Chain Detection with Machine Translation Framework

The invention discloses a JPEG compressed image super-resolution reconstruction method based on a convolutional neural network (CNN). The method mainly includes the following steps of: constructing asuper-resolution reconstruction model based on the CNN for a JPEG compressed image; training the constructed CNN by using a training image; reconstructing a JPEG-compressed low-resolution image by using a trained CNN model. The constructed CNN framework comprises a decompression effect network, a resolution enhancement network and a quality enhancement network, and can be subjected to end-to-end optimization training. The method of the present invention can reduce the compression noise in the JPEG compressed image and improve the resolution of the JPEG compressed image, can be applied to the fields of image and video compression, digital multimedia communication and the like.

JPEG compressed image super-resolution reconstruction method based on convolutional neural network

The present invention provides a compressed low-resolution image restoration method based on a combined deep network, belonging to the digital image/video signal processing field. The compressed low-resolution image restoration method based on the combined deep network starts from the aspect of the coprocessing of the compression artifact and downsampling factors to complete the restoration of a degraded image with the random combination of the compression artifact and the low resolution; the network provided by the invention comprises 28 convolution layers to establish a leptosomatic network structure, according to the idea of transfer learning, a model trained in advance employs a fine tuning mode to complete the training convergence of a greatly deep network so as to solve the problems of vanishing gradients and gradient explosion; the compressed low-resolution image restoration method completes the setting of the network model parameters through feature visualization, and the relation of the end-to-end learning degeneration feature and the ideal features omits the preprocessing and postprocessing; and finally, three important fusions are completed, namely the fusion of the feature figures with the same size, the fusion of residual images and the fusion of the high-frequency information and the high-frequency initial estimation figure, and the compressed low-resolution image restoration method can solve the super-resolution restoration problem of the low-resolution image with the compression artifact.

Compressed low-resolution image restoration method based on combined deep network

Disclosed is a low-resolution face image feature super-resolution reconstruction method for identification. The method is characterized in that an algorithm is divided into two phases, i.e., an offline phase and an online phase. The offline phase comprises establishment of a training sample database, resolution robust feature extraction, consistency space conversion, P-KPLS (Piecewise Kernel Partial Least Squares) regression model training for identification, and establishment of a high-resolution face image feature database. The online phase comprises face image feature extraction, consistency space conversion, predication of high-resolution face image features, feature weight combination and identification. According to the invention, by use of a machine learning method, through learning corresponding relations between low-resolution and high-resolution face image features, the low-resolution face image features are reconstructed to the high-resolution face image features, and thus the face identification rate of low-resolution face images is improved.

Low-resolution face image feature super-resolution reconstruction method for identification

In the procedures of image acquisition, compression, and transmission, captured images usually suffer from various degradations, such as low-resolution and compression distortion. Although there have been a lot of research done on image restoration, they usually aim to deal with a single degraded factor, ignoring the correlation of different degradations. To establish a restoration framework for multiple degradations, a joint deep-network-based image restoration algorithm is proposed in this paper. The proposed convolutional neural network is composed of two stages. Firstly, a de-blocking subnet is constructed, using two cascaded neural network. Then, super-resolution is carried out by a 20-layer very deep network with skipping links. Cascading these two stages forms a novel deep network. Experimental results on the Set5, Setl4 and BSD100 benchmarks demonstrate that the proposed method can achieve better results, in terms of both the subjective and objective performances.

A joint deep-network-based image restoration algorithm for multi-degradations

The invention provides a self-adaptive deblocking effect method orienting a compressed face image, and relates to the field of image denoising. The block search area in a three-dimensional block matching filtering method is extended according to the symmetry of a face. The variance parameter (sigma) in a filter is also self-adaptively adjusted according to the positions of the eyes, the nose and the mouth and the compression quality. The face self-adaptive three-dimensional block matching filtering method can be further enhanced by aiming at the deblocking effect result in the face image according to the symmetry of the face and similar geometric structural features. Model statistics is performed on the face image, and prior position information of the face is introduced to the image deblocking effect so that the detailed information of the face image can be better maintained in filter; and different parameters are adopted for filtering for aiming at the face images under different compression ratios so that filtering is enabled to be more targeted and the algorithm can be optimized.

Self-adaptive deblocking effect method orienting compressed face image

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