<div style="text-align:justify;"> Polar codes using successive-cancellation decoding always suffer from high latency for its serial nature. Fast simplified successive-cancellation decoding algorithm im...<div style="text-align:justify;"> Polar codes using successive-cancellation decoding always suffer from high latency for its serial nature. Fast simplified successive-cancellation decoding algorithm improves the situation in theoretically but not performs well as expected in practical for the workload of nodes identification and the existence of many short blocks. Meanwhile, Neural network (NN) based decoders have appeared as potential candidates to replace conventional decoders for polar codes. But the exponentially increasing training complexity with information bits is unacceptable which means it is only suitable for short codes. In this paper, we present an improvement that increases decoding efficiency without degrading the error-correction performance. The long polar codes are divided into several sub-blocks, some of which can be decoded adopting fast maximum likelihood decoding method and the remained parts are replaced by several short codes NN decoders. The result shows that time steps the proposed algorithm need only equal to 79.8% of fast simplified successive-cancellation decoders require. Moreover, it has up to 21.2 times faster than successive-cancellation decoding algorithm. More importantly, the proposed algorithm decreases the hardness when applying in some degree. </div>展开更多
<div style="text-align:justify;"> <p style="text-align:justify;background:white;"> <span style="font-size:10.0pt;font-family:" color:black;"="">This artic...<div style="text-align:justify;"> <p style="text-align:justify;background:white;"> <span style="font-size:10.0pt;font-family:" color:black;"="">This article has been retracted to straighten the academic record. In making this decision the Editorial Board follows COPE's </span><span><a href="http://publicationethics.org/files/retraction%20guidelines.pdf"><span style="font-size:10.0pt;font-family:;" "="">Retraction Guidelines</span></a></span><span style="font-size:10.0pt;font-family:" color:black;"="">. The aim is to promote the circulation of scientific research by offering an ideal research publication platform with due consideration of internationally accepted standards on publication ethics. The Editorial Board would like to extend its sincere apologies for any inconvenience this retraction may have caused.</span><span style="font-size:10.0pt;font-family:" color:black;"=""></span> </p> <p style="text-align:justify;background:white;"> <span style="font-size:10.0pt;font-family:" color:black;"="">Please see the </span><span><a href="https://www.scirp.org/journal/paperinformation.aspx?paperid=101825"><span style="font-size:10.0pt;font-family:;" "="">article page</span></a></span><span style="font-size:10.0pt;font-family:" color:black;"=""> for more details. </span><span><a href="https://www.scirp.org/pdf/opj_2020072814494052.pdf"><span style="font-size:10.0pt;font-family:;" "="">The full retraction notice</span></a></span><span style="font-size:10.0pt;font-family:" color:black;"=""> in PDF is preceding the original paper which is marked "RETRACTED". </span> </p> <br /> </div>展开更多
Current image inpainting models are primarily designed to achieve a large receptive field(RF)using refinement networks to incorporate different scales.However,these models fail to adapt the use of different RFs to the...Current image inpainting models are primarily designed to achieve a large receptive field(RF)using refinement networks to incorporate different scales.However,these models fail to adapt the use of different RFs to the specific patterns of image damage,resulting in artifacts and semantic information confusion in repaired images.To address the problems of artifacts and semantic information confusion,inspired by different sensitivities of different RFs to inpainting the same image damaged patterns,this study proposes an image inpainting method based on multiple receptive fields(MRFs)and dynamic matching of damaged patterns.First,the parallel filter banks are used to extract the MRF feature groups.Second,the features are dynamically weighted and screened,guided by the mask image,to construct a relationship that adaptively matches the most relevant RF to each specific damaged pattern.A fast Fourier convolution based decoder is used to enhance the fusion of global contextual features during the reconstruction of high dimensional features into low dimensional images.Comparative experimental results show that the proposed method achieves better subjective and objective inpainting results on three public datasets:Paris StreetView,CelebA-HQ,and Places2.展开更多
文摘<div style="text-align:justify;"> Polar codes using successive-cancellation decoding always suffer from high latency for its serial nature. Fast simplified successive-cancellation decoding algorithm improves the situation in theoretically but not performs well as expected in practical for the workload of nodes identification and the existence of many short blocks. Meanwhile, Neural network (NN) based decoders have appeared as potential candidates to replace conventional decoders for polar codes. But the exponentially increasing training complexity with information bits is unacceptable which means it is only suitable for short codes. In this paper, we present an improvement that increases decoding efficiency without degrading the error-correction performance. The long polar codes are divided into several sub-blocks, some of which can be decoded adopting fast maximum likelihood decoding method and the remained parts are replaced by several short codes NN decoders. The result shows that time steps the proposed algorithm need only equal to 79.8% of fast simplified successive-cancellation decoders require. Moreover, it has up to 21.2 times faster than successive-cancellation decoding algorithm. More importantly, the proposed algorithm decreases the hardness when applying in some degree. </div>
文摘<div style="text-align:justify;"> <p style="text-align:justify;background:white;"> <span style="font-size:10.0pt;font-family:" color:black;"="">This article has been retracted to straighten the academic record. In making this decision the Editorial Board follows COPE's </span><span><a href="http://publicationethics.org/files/retraction%20guidelines.pdf"><span style="font-size:10.0pt;font-family:;" "="">Retraction Guidelines</span></a></span><span style="font-size:10.0pt;font-family:" color:black;"="">. The aim is to promote the circulation of scientific research by offering an ideal research publication platform with due consideration of internationally accepted standards on publication ethics. The Editorial Board would like to extend its sincere apologies for any inconvenience this retraction may have caused.</span><span style="font-size:10.0pt;font-family:" color:black;"=""></span> </p> <p style="text-align:justify;background:white;"> <span style="font-size:10.0pt;font-family:" color:black;"="">Please see the </span><span><a href="https://www.scirp.org/journal/paperinformation.aspx?paperid=101825"><span style="font-size:10.0pt;font-family:;" "="">article page</span></a></span><span style="font-size:10.0pt;font-family:" color:black;"=""> for more details. </span><span><a href="https://www.scirp.org/pdf/opj_2020072814494052.pdf"><span style="font-size:10.0pt;font-family:;" "="">The full retraction notice</span></a></span><span style="font-size:10.0pt;font-family:" color:black;"=""> in PDF is preceding the original paper which is marked "RETRACTED". </span> </p> <br /> </div>
基金The National Natural Science Foundation of China(No.62261032)the Central Government Guiding Funds for Local Scienceand Technology Development Program(No.25ZYJA026).
文摘Current image inpainting models are primarily designed to achieve a large receptive field(RF)using refinement networks to incorporate different scales.However,these models fail to adapt the use of different RFs to the specific patterns of image damage,resulting in artifacts and semantic information confusion in repaired images.To address the problems of artifacts and semantic information confusion,inspired by different sensitivities of different RFs to inpainting the same image damaged patterns,this study proposes an image inpainting method based on multiple receptive fields(MRFs)and dynamic matching of damaged patterns.First,the parallel filter banks are used to extract the MRF feature groups.Second,the features are dynamically weighted and screened,guided by the mask image,to construct a relationship that adaptively matches the most relevant RF to each specific damaged pattern.A fast Fourier convolution based decoder is used to enhance the fusion of global contextual features during the reconstruction of high dimensional features into low dimensional images.Comparative experimental results show that the proposed method achieves better subjective and objective inpainting results on three public datasets:Paris StreetView,CelebA-HQ,and Places2.
