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A coarse-to-fine framework for learned color enhancement with non-local attention

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arXiv 2019年

作者： Shan, Chaowei Zhang, Zhizheng Chen, Zhibo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

Automatic color enhancement is aimed to adaptively adjust photos to expected styles and tones. For current learned methods in this field, global harmonious perception and local details are hard to be well-considered in a single model simultaneously. To address this problem, we propose a coarse-tofine framework with non-local attention for color enhancement in this paper. Within our framework, we propose to divide enhancement process into channel-wise enhancement and pixel-wise refinement performed by two cascaded Convolutional Neural Networks (CNNs). In channel-wise enhancement, our model predicts a global linear mapping for RGB channels of input images to perform global style adjustment. In pixel-wise refinement, we learn a refining mapping using residual learning for local adjustment. Further, we adopt a non-local attention block to capture the long-range dependencies from global information for subsequent fine-grained local refinement. We evaluate our proposed framework on the commonly using benchmark and conduct sufficient experiments to demonstrate each technical component within it. Copyright © 2019, The Authors. All rights reserved.

关键词： Pixels

No-reference light field image quality assessment based on micro-lens image

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arXiv 2019年

作者： Luo, Ziyuan Zhou, Wei Shi, Likun Chen, Zhibo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

Light field image quality assessment (LF-IQA) plays a significant role due to its guidance to Light Field (LF) contents acquisition, processing and application. The LF can be represented as 4-D signal, and its quality depends on both angular consistency and spatial quality. However, few existing LF-IQA methods concentrate on effects caused by angular inconsistency. Especially, no-reference methods lack effective utilization of 2-D angular information. In this paper, we focus on measuring the 2-D angular consistency for LF-IQA. The Micro-Lens Image (MLI) refers to the angular domain of the LF image, which can simultaneously record the angular information in both horizontal and vertical directions. Since the MLI contains 2-D angular information, we propose a No-Reference Light Field image Quality assessment model based on MLI (LF-QMLI). Specifically, we first utilize Global Entropy Distribution (GED) and Uniform Local Binary Pattern descriptor (ULBP) to extract features from the MLI, and then pool them together to measure angular consistency. In addition, the information entropy of Sub-Aperture Image (SAI) is adopted to measure spatial quality. Extensive experimental results show that LF-QMLI achieves the state-of-the-art performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Image quality

A STUDY ON THE FREQUENCY AND AZIMUTH COHERENCE OF HIGH-RESOLUTION SAR IMAGE

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A STUDY ON THE FREQUENCY AND AZIMUTH COHERENCE OF HIGH-RESOL...

IEEE International geoscience and Remote Sensing Symposium

作者： Wenji Xing Xiaolan Qiu Chibiao Ding The Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing China

High-resolution SAR has large transmitting bandwidth and wide synthetic aperture. How to understand and take advantage of the variation characteristics of SAR scattering characteristics with angle and frequency is a topic that worth studying. This article establishes a coherence matrix of sub-band and sub-aperture SAR images, and analyzes its ability to classify scattering mechanism. Experiments are conducted using the TerraSAR-X high-resolution data of different scenarios, and some meaningful results are got, which may provide some support to the analysis and application of high-resolution SAR data.

关键词： Coherence Entropy Azimuth Radar polarimetry Synthetic aperture radar Image resolution

A COOPERATIVE MULTITEMPORAL SEGMENTATION METHOD FOR SAR AND OPTICAL IMAGES CHANGE DETECTION

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A COOPERATIVE MULTITEMPORAL SEGMENTATION METHOD FOR SAR AND ...

IEEE International geoscience and Remote Sensing Symposium

作者： Ling Wan Yuming Xiang Hongjian You Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing China

This paper proposes an extension version of our previous work MS-CC to achieve optical and SAR images change detection. The proposed method introduces a cooperative multitemporal segmentation, whose merging process considers the heterogeneity of SAR and optical images as parallel information, making sure that the multitemporal information can be fully utilized without interfering with each other. Then, the change detection strategy based on compound classification is carried out on the segmentation results, obtaining the multi-scale change detection maps. Experimental validation is conducted with GoaFen3 and Google Earth data.

关键词： Image segmentation Optical imaging Radar polarimetry Optical sensors Compounds Interference Stacking

Deep learning-based video coding: A review and a case study

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arXiv 2019年

作者： Liu, Dong Li, Yue Lin, Jianping Li, Houqiang Wu, Feng CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

The past decade has witnessed great success of deep learning technology in many disciplines, especially in computer vision and image processing. However, deep learning-based video coding remains in its infancy. This paper reviews the representative works about using deep learning for image/video coding, which has been an actively developing research area since the year of 2015. We divide the related works into two categories: new coding schemes that are built primarily upon deep networks (deep schemes), and deep network-based coding tools (deep tools) that shall be used within traditional coding schemes or together with traditional coding tools. For deep schemes, pixel probability modeling and auto-encoder are the two approaches, that can be viewed as predictive coding scheme and transform coding scheme, respectively. For deep tools, there have been several proposed techniques using deep learning to perform intra-picture prediction, inter-picture prediction, cross-channel prediction, probability distribution prediction, transform, post- or in-loop filtering, down- and up-sampling, as well as encoding optimizations. According to the newest reports, deep schemes have achieved comparable or even higher compression efficiency than the state-of-the-art traditional schemes, such as High Efficiency Video Coding (HEVC) based scheme, for image coding;deep tools have demonstrated the compression capability beyond HEVC for video coding. However, deep schemes have not yet reached the current height of HEVC for video coding, and deep tools remain largely unexplored at many aspects including the tradeoff between compression efficiency and encoding/decoding complexity, the optimization for perceptual naturalness or semantic quality, the speciality and universality, the federated design of multiple deep tools, and so on. In the hope of advocating the research of deep learning-based video coding, we present a case study of our developed prototype video codec, namely Deep Learning Vi

关键词： Video signal processing

W-net: Simultaneous segmentation of multi-anatomical retinal structures using a multi-task deep neural network

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arXiv 2020年

作者： Zhao, Hongwei Peng, Chengtao Liu, Lei Li, Bin School of Information Science and Technology University of Science and Technology of China Hefei Anhui230022 China Department of Precision Machinery and Instrumentation University of Science and Technology of China HefeiAnhui230022 China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei Anhui230026 China

Segmentation of multiple anatomical structures is of great importance in medical image analysis. In this study, we proposed a W-net to simultaneously segment both the optic disc (OD) and the exudates in retinal images based on the multi-task learning (MTL) scheme. We introduced a class-balanced loss and a multi-task weighted loss to alleviate the imbalanced problem and to improve the robustness and generalization property of the W-net. We demonstrated the effectiveness of our approach by applying five-fold cross-validation experiments on two public datasets e_ophtha_EX and DiaRetDb1. We achieved F1-score of 94.76% and 95.73% for OD segmentation, and 92.80% and 94.14% for exudates segmentation. To further prove the generalization property of the proposed method, we applied the trained model on the DRIONS-DB dataset for OD segmentation and on the MESSIDOR dataset for exudate segmentation. Our results demonstrated that by choosing the optimal weights of each task, the MTL based W-net outperformed separate models trained individually on each task. Code and pre-trained models will be available at: https://***/FundusResearch/MTL_for_OD_and_***. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

ON THE USE OF CNN FOR AUTOMATED QUALITY ASSESSMENT OF GF-3 POLARIMETRIC DATA

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ON THE USE OF CNN FOR AUTOMATED QUALITY ASSESSMENT OF GF-3 P...

IEEE International geoscience and Remote Sensing Symposium

作者： Songtao Shangguan Xiaolan Qiu Bin Lei The Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing China

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.

关键词： Quality assessment Distortion Data integrity Strips Manuals Forestry Radar polarimetry

Learned fast HEVC intra coding

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arXiv 2019年

作者： Chen, Zhibo Shi, Jun Li, Weiping CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

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 excellent RD performance is achieved at the expense of much higher computational complexity. In this paper, we propose a learned fast HEVC intra coding (LFHI) framework taking into account the comprehensive factors of fast intra coding to reach an improved configurable tradeoff between coding performance and computational complexity. First, we design a low-complex shallow asymmetric-kernel CNN (AK-CNN) to efficiently extract the local directional texture features of each block for both fast CU partition and fast intra-mode decision. Second, we introduce the concept of the minimum number of RDO candidates (MNRC) into fast mode decision, which utilizes AK-CNN to predict the minimum number of best candidates for RDO calculation to further reduce the computation of intra-mode selection. Third, an evolution optimized threshold decision (EOTD) scheme is designed to achieve configurable complexity-efficiency tradeoffs. Finally, we propose an interpolation-based prediction scheme that allows for our framework to be generalized to all quantization parameters (QPs) without the need for training the network on each QP. The experimental results demonstrate that the LFHI framework has a high degree of parallelism and achieves a much better complexity-efficiency tradeoff, achieving up to 75.2% intra-mode encoding complexity reduction with negligible rate-distortion performance degradation, superior to the existing fast intra-coding schemes. Copyright © 2019, The Authors. All rights reserved.

关键词： Signal distortion

RC-CNN: Representation-Consistent Convolutional Neural Networks for Achieving Transformation Invariance

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RC-CNN: Representation-Consistent Convolutional Neural Netwo...

IEEE International Conference on systems, Man and Cybernetics

作者： Jun Gu Anfeng He Xinmei Tian CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei Anhui China

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.

关键词： Computational modeling Feature extraction Training Image recognition Data models Task analysis Kernel