Haze caused by atmospheric scattering and absorption would severely affect scene visibility of an image. Thus, image dehazing for haze removal has been widely studied in the literature. Within a hazy image, haze is not confined in a small local patch/position, while widely diffusing in a whole image. Under this circumstance, global context is a crucial factor in the success of dehazing, which was seldom investigated in existing dehazing algorithms. In the literature, the global context (GC) block has been designed to learn point-wise long-range dependencies of an image for global context modeling; however, patch-wise long-range dependencies were ignored. To image dehazing, patch-wise long-range dependencies should be highlighted to cooperate with patch-wise operations of image dehazing. In this paper, we first extend the point-wise GC into a Pyramid Global Context (PGC), which is a multi-scale GC, after undergoing the pyramid pooling. Thus, patch-wise long-range dependencies can be explored by the PGC. Then, the proposed PGC is plugged into a U-Net, getting an attentive U-Net. Further, the attentive U-Net is optimized by importing ResNet's shortcut connection and dilated convolution. Thus, the finalized dehazing model can explore both long-range and patch-wise context dependencies for global context modeling, which is crucial for image dehazing. The extensive experiments on synthetic databases and real-world hazy images demonstrate the superiority of our model over other representative state-of-the-art models from both quantitative and qualitative comparisons.

MingantU SpEctral Radioheliograph (MUSER) is a radio synthesis imaging array dedicated to observe the sun, operating on multiple frequencies from decimeter to centimeter range. In the experimental observations MUSER produces about 2.55 Tera Bytes data daily, which will be processed offline to get the solar radio images. In this paper the current data processing and imaging procedure for MUSER will be discussed in details. Some algorithms are presented to solve the problems in the imaging with MUSER.

Mingantu Ultrawide Spectral Radioheliograph (MUSER) is an aperture synthesis telescope consisting of a group of small antennas to image the Sun. Each two antennas form a baseline contributing a Fourier sampling point for each time of imaging. Both amplitude and phase of a baseline form a time sequence in a time interval. Normally, amplitude/phase should vary linearly in a short time interval. However, many reasons would result in errors of baselines, damaging image quality. This work makes the first attempt to auto-flag baselines so as to delete bad baselines and improve image quality. Inspired by the big success of long short-term memory (LSTM) for time series analysis, we believe that LSTM could accomplish auto-flagging (a binary classification task) of baselines even better by treating them as time sequences. Thus, LSTM is employed to learn the representation of the phase of each baseline for classification, where the interaction and connection within a phase sequence are explored, benefiting classification. The experimental results demonstrate that LSTM can well capture the characteristics of the phase of a baseline, and thus achieves better classification.

The ionosphere is a region over earth's atmosphere which is ionized by solar radiation. It plays an important part in atmospheric electricity and forms the inner edge of the magnetosphere, furthermore, it has practical importance for its effects on radio propagation to earth. Accordingly, its cyclic changes and disturbances could influence communication, navigation, radar seriously. Total Electron Content (TEC) is an important parameter reflecting ionospheric significant characteristic. We can analyse ionospheric disturbance and cyclic change by investigating TEC. Now, the time sequence of TEC and relevant parameters is available for our research. In addition, Bidirectional long short-term memory (Bi-LSTM) is believed to have potential in time sequence processing. Hence, we investigate TEC forecast by referring to Bi-LSTM in this work. Experimental results demonstrate that Bi-LSTM can capture the cyclic change feature of TEC, and perform better than LSTM and multi-LSTM for TEC forecast.

In practice, images are distorted by more than one distortion. For image quality assessment (IQA), existing machine learning (ML)-based methods generally establish a unified model for all the distortion types, or each model is trained independently for each distortion type, which is therefore distortion aware. In distortion-aware methods, the common features among different distortions are not exploited. In addition, there are fewer training samples for each model training task, which may result in overfitting. To address these problems, we propose a multi-task learning framework to train multiple IQA models together, where each model is for each distortion type; however, all the training samples are associated with each model training task. Thus, the common features among different distortion types and the said underlying relatedness among all the learning tasks are exploited, which would benefit the generalization ability of trained models and prevent overfitting possibly. In addition, pairwise image quality ranking instead of image quality rating is optimized in our learning task, which is fundamentally departed from traditional ML-based IQA methods toward better performance. The experimental results confirm that the proposed multi-task rank-learning-based IQA metric is prominent against all state-of-the-art nonreference IQA approaches.

In solar radio observation, the visualization of data is very important since it can more intuitively and clearly deliver interest information of solar radio activities to astronomers. As to visualization, we highly expect good visual quality of images/videos in favor of the discovery of solar radio events recorded by observation data. The existing imaging system cannot guarantee good visual quality of solar radio data visualization. In this paper, an image quality enhancement algorithm is developed to improve solar radio extreme ultraviolet (EUV) images from Solar Dynamics Observatory (SDO). Firstly, the guided filter is employed to smooth image, which outputs an image with good skeleton and edges. Since the fine structures of solar radio activities are embedded in high frequency components of a solar radio image, we propose a novel structure preserving filtering to amplify the different signal of original input image subtracting smoothed one. Afterwards, fusing the amplified details and smoothed one together, the final enhanced image is generated. The experimental results prove that the image quality is significantly improved by using the proposed image quality enhancement algorithm.

HEVC is an emerging new standard for digital video compression, which is regarded as a successor to H.264/AVC standard. It still belongs to block-based hybrid video coding framework. The block patterns range from 4×4 to 64×64 blocks, and DCT is extended from 4×4 to 32×32. 2D-DCT for image is performed along the vertical and horizontal directions, so it is good at the energy compaction of residual block with vertical or horizontal edges. However, the edges are usually neither horizontal nor vertical for most cases, such as neither vertical nor horizontal intra prediction, so directional transform was explored in the past several years. In this paper, a directional transform adaptive to image content is proposed. Firstly, the prediction residual blocks are collected from coding a number of video sequences with plenty of image content. Secondly, for each kind of image content, the residual blocks are clustered to form the given number of clusters. Thirdly, each cluster contributes a transform basis after Singular Value Decomposition (SVD). The experimental results in terms of PSNR gains demonstrate the efficiency of the proposed algorithm with the comparison with the standard HM software.

In this paper, we propose a novel no-reference image quality assessment method for the retargeted image based on the pairwise rank learning approach. Each retargeted image needs to be first represented as a feature vector, which not only captures the image characteristics but also is sensitive to distortions during the retargeting process. As such, we investigate and examine different image representations for their abilities depicting the perceptual quality of retargeted image. Based on the image representations, we resort to the pairwise rank learning approach to discriminate the perceptual quality between the retargeted image pairs. Experimental results demonstrate that the proposed method can effectively depict the perceptual quality of the retargeted image, which can even perform comparably with the full-reference quality assessment methods.

In this paper, we extend the free-energy principle to video quality assessment (VQA) by incorporating with the recent psychophysical study on human visual speed perception (HVSP). A novel video quality metric, namely the free-energy principle inspired video quality metric (FePVQ), is therefore developed and applied to perceptual video coding optimization. The free-energy principle suggests that the human visual system (HVS) can actively predict “orderly” information and avoid “disorderly” information for image perception. Basically, “orderly” is associated with the skeletons and edges of objects, and “disorderly” mostly concerns textures in images. Based on this principle, an image is separated into orderly and disorderly regions, and processed differently in image quality assessment. For videos, visual attention, or fixation, is associated with the objects with significant motion according to HVSP, resulting in a motion strength factor in the FePVQ so that the free-energy principle is extended into spatio-temporal domain for VQA. In addition, we investigate the application of the FePVQ in perceptual rate distortion optimization (RDO). For this purpose, the FePVQ is realized with low computational cost by using the relative total variation model and the block-wise motion vectors of video coding to simulate the free-energy principle and the HVSP, respectively. The experimental results indicate that the proposed FePVQ is highly consistent with the HVS perception. The linear correlation coefficient and Spearman's rank-order correlation coefficient are up to 0.8324 and 0.8281 on the LIVE video database. Better perceptual quality of encoded video sequences is achieved by FePVQ-motivated RDO in video coding.

In practice, multiple types of distortions are associated with an image quality degradation process. The existing machine learning (ML) based image quality assessment (IQA) approaches generally established a unified model for all distortion types, or each model is trained independently for each distortion type by using single-task learning, which lead to the poor generalization ability of the models as applied to practical image processing. There are often the underlying cross relatedness amongst these single-task learnings in IQA, which is ignored by the previous approaches. To solve this problem, we propose a multi-task learning framework to train IQA models simultaneously across individual tasks each of which concerns one distortion type. These relatedness can be therefore exploited to improve the generalization ability of IQA models from single-task learning. In addition, pairwise image quality rank instead of image quality rating is optimized in learning task. By mapping image quality rank to image quality rating, a novel no-reference (NR) IQA approach can be derived. The experimental results confirm that the proposed Multi-task Rank Learning based IQA (MRLIQ) approach is prominent among all state-of-the-art NR-IQA approaches.

This paper proposes the first attempt to utilize multi-modal learning method for the representation learning of the solar radio spectrums. The solar radio signals sensed from differ-ent frequency channels, which present different characteristics, are regarded as different modalities. We employ a multimodal neural network to learn the representations of the solar radio spectrum, which can distinguish the differences and learn the interactions between different modalities. The original solar ra-dio spectrums are firstly pre-processed, including normalization, denoising, channel competition and etc., before being fed into the multimodal learning network. Experimental results have demon-strated that the proposed multimodal learning network can learn the representation of the solar radio spectrum more effectively, and improve the classification accuracy.

In this paper, we first propose a novel no-reference (NR) image quality assessment (IQA) method for retargeted image based on the rank learning approach. Firstly, image features for each retargeted image are extracted, which should not only represent the image characteristics but also be sensitive to the retargeted distortions. Specifically, the image feature should be able to capture the shape distortions, which are the commonly encountered distortions of the retargeted image. Based on the extracted image features, the rank learning method is employed to train a model to discriminate the perceptual quality of the retargeted image. Experimental results demonstrate that the proposed method can effectively depict the perceptual quality of the retargeted image, which can even perform comparably with the full-reference (FR) quality assessment methods.

Machine learning (ML) techniques are widely used in recent no-reference visual quality assessment (NR-VQA) metrics by training on subjective image quality databases. In these metrics, the optimization function is constructed based on L2norm of the distance between subjective image quality and predicted image quality. There are two problems in these L2norm based methods: (1) human's opinion on subjective image quality rating is not reliable at fine-scale level. A small difference between subjective image qualities represented by mean opinion scores (MOSs) of two images may not truly reflect the real quality difference between these two images, but acts as noise. The optimization process should avoid such noise. (2) Generally, human's opinion on pairwise comparison (PC) for image quality is more reliable and believable than MOS. The importance of PC is ignored during the optimization process of existing ML-based studies, which are designed based on the numerical rating system. In this paper, we introduce image quality ranking concept to establish a new optimization objective instead of L2norm optimization, and then a novel NR-VQA is constructed based on ranking learning. The proposed metric firstly suggests a reasonable training set for ML, which is ignored by existing ML-based NR-VQA. The ranking theory is adopted to build optimization function, which reflects the properties of PC over the numerical ranting system used by traditional NR-VQA. By ignoring the small difference between MOSs from two images during the optimization process, the proposed ranking-based NR-VQA can also well address the first problem from the existing related metrics. Experimental results show that the proposed ranking-based NR-VQA can obtain better performance over the state-of-the-art NR-VQA approaches.