Motion compensation is a fundamental technology in video coding to remove the temporal redundancy between video frames. To further improve the coding efficiency, sub-pel motion compensation has been utilized, which re...
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With the needs of quality assessment for massive GF-3 polarimetric data, a method based on common distribution targets has been proposed by Sha Jiang. However, it needs manually selection of those woodlands, and canno...
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With the needs of quality assessment for massive GF-3 polarimetric data, a method based on common distribution targets has been proposed by Sha Jiang. However, it needs manually selection of those woodlands, and cannot be performed automatically. In this paper, an automated GF-3 full-polarization SAR data quality assessment method is conducted using a classic Convolution Neural Network (VGG-16). The network is pre-trained by Radarsat-2 PolSAR data and then trained by selected typical GF-3 scenes. It is supposed to learn the features of the targets, which satisfies the azimuthal symmetry and backscatter reciprocity and fulfills the quality assessment work. Several typical GF-3 strips data are used to test the method. Experiments show that the network can predict the plots of targets from a new scene under the interference of polarimetric distortion and noise. And, the quality assessment results by the network are consistent with the manual assessment results, which shows the effectiveness of the method.
Existing blind image quality assessment (BIQA) methods are mostly designed in a disposable way and cannot evolve with unseen distortions adaptively, which greatly limits the deployment and application of BIQA models i...
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In this article, we present a novel non-local video denoising scheme using low-rank representation and total variation regularization. The proposed scheme attempts to make full use of the intrinsic properties that the...
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ISBN:
(纸本)9781479934331
In this article, we present a novel non-local video denoising scheme using low-rank representation and total variation regularization. The proposed scheme attempts to make full use of the intrinsic properties that the grouping similar patches not only lie in a low-rank subspace but are also sparse in total variation (TV) domain. For a group of similar patches, we formulate video denoising problem into a concise model that combines nuclear norm, TV regularization and l_1 norm. The experiments demonstrate that the proposed scheme is capable of handling multi-type noise including dense Gaussian noise and random-valued sparse noise, while maintaining the texture information meantime. The results show that our scheme achieves noticeable performance improvement over the state-of-the-art video denoising methods.
In High Efficiency Video Coding (HEVC), excellent rate-distortion (RD) performance is achieved in part by having a flexible quadtree coding unit (CU) partition and a large number of intra-prediction modes. Such an exc...
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Convolutional neural networks (CNNs) are powerful and have achieved state-of-the-art performance in many visual recognition tasks. Despite their impressive performance, CNNs are still unable to remain invariant while ...
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Convolutional neural networks (CNNs) are powerful and have achieved state-of-the-art performance in many visual recognition tasks. Despite their impressive performance, CNNs are still unable to remain invariant while some spatial transformations are applied on images. Herein, we propose representation-consistent neural networks to solve this problem. By introducing consistent losses between the representations in different layers of transformed images, the recognition performance of transformed images is significantly improved. This model not only learns to map from the transformed images to the pre-defined labels but each layer also learns to generate invariant representations when the input images are transformed. All the characteristics of transformation invariance are embedded in the model, which means that no extra parameters or computations are introduced in the well-trained model. Comparative experiments demonstrate the superiority of our model when learning invariance to rotation, translation, and scaling on large-scale image recognition and retrieval tasks.
Objective quality assessment of stereoscopic panoramic images becomes a challenging problem owing to the rapid growth of 360-degree contents. Different from traditional 2D image quality assessment (IQA), more complex ...
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One key challenge to learning-based video compression is that motion predictive coding, a very effective tool for video compression, can hardly be trained into a neural network. In this paper we propose the concept of...
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We study the video super-resolution (SR) problem for facilitating video analytics tasks, e.g. action recognition, instead of for visual quality. The popular action recognition methods based on convolutional networks, ...
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In recent years, the Total Generalized Variation (TGV) model has received lots of attention in image processing community. Though this model can restore image with natural intensity transitions, its spatial identical ...
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In recent years, the Total Generalized Variation (TGV) model has received lots of attention in image processing community. Though this model can restore image with natural intensity transitions, its spatial identical parameter setting limits its performance. In this paper, we propose a novel Adaptive Weighted Total Generalized Variation model for image restoration. We analyze the TGV model from Bayesian Probability view and derive a novel adaptive parameter calculation scheme for it, exploiting the image's self-similarity. Experiment results on image deblurring and reconstruction show that by adapting the parameters in TGV model to image contents, the proposed model can restore image's edges and details well and achieve significant improvement over state of the art variational based models.
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