In low-light image enhancement,prevailing Retinex-based methods often struggle with precise illumina-tion estimation and brightness modulation.This can result in issues such as halo artifacts,blurred edges,and diminis...In low-light image enhancement,prevailing Retinex-based methods often struggle with precise illumina-tion estimation and brightness modulation.This can result in issues such as halo artifacts,blurred edges,and diminished details in bright regions,particularly under non-uniform illumination conditions.We propose an innovative approach that refines low-light images by leveraging an in-depth awareness of local content within the image.By introducing multi-scale effective guided filtering,our method surpasses the limitations of traditional isotropic filters,such as Gaussian filters,in handling non-uniform illumination.It dynamically adjusts regularization parameters in response to local image characteristics and significantly integrates edge perception across different scales.This balanced approach achieves a harmonious blend of smoothing and detail preservation,enabling more accurate illumination estimation.Additionally,we have designed an adaptive gamma correction function that dynamically adjusts the brightness value based on local pixel intensity,further balancing enhancement effects across different brightness levels in the image.Experimental results demonstrate the effectiveness of our proposed method for non-uniform illumination images across various scenarios.It exhibits superior quality and objective evaluation scores compared to existing methods.Our method effectively addresses potential issues that existing methods encounter when processing non-uniform illumination images,producing enhanced images with precise details and natural,vivid colors.展开更多
Medical image segmentation plays an important role in clinical diagnosis,quantitative analysis,and treatment process.Since 2015,U-Net-based approaches have been widely used formedical image segmentation.The purpose of...Medical image segmentation plays an important role in clinical diagnosis,quantitative analysis,and treatment process.Since 2015,U-Net-based approaches have been widely used formedical image segmentation.The purpose of the U-Net expansive path is to map low-resolution encoder feature maps to full input resolution feature maps.However,the consecutive deconvolution and convolutional operations in the expansive path lead to the loss of some high-level information.More high-level information can make the segmentationmore accurate.In this paper,we propose MU-Net,a novel,multi-path upsampling convolution network to retain more high-level information.The MU-Net mainly consists of three parts:contracting path,skip connection,and multi-expansive paths.The proposed MU-Net architecture is evaluated based on three different medical imaging datasets.Our experiments show that MU-Net improves the segmentation performance of U-Net-based methods on different datasets.At the same time,the computational efficiency is significantly improved by reducing the number of parameters by more than half.展开更多
Background Owing to the limitations of the working principle of three-dimensional(3D) scanning equipment, the point clouds obtained by 3D scanning are usually sparse and unevenly distributed. Method In this paper, we ...Background Owing to the limitations of the working principle of three-dimensional(3D) scanning equipment, the point clouds obtained by 3D scanning are usually sparse and unevenly distributed. Method In this paper, we propose a new generative adversarial network(GAN) that extends PU-GAN for upsampling of point clouds. Its core architecture aims to replace the traditional self-attention(SA) module with an implicit Laplacian offset attention(OA) module and to aggregate the adjacency features using a multiscale offset attention(MSOA)module, which adaptively adjusts the receptive field to learn various structural features. Finally, residual links are added to create our residual multiscale offset attention(RMSOA) module, which utilizes multiscale structural relationships to generate finer details. Result The results of several experiments show that our method outperforms existing methods and is highly robust.展开更多
A content-aware multi-step prediction control(CAMPC)algorithm is proposed to determine the bitrate of 360-degree videos,aim⁃ing to enhance the quality of experience(QoE)of users and reduce the cost of video content pr...A content-aware multi-step prediction control(CAMPC)algorithm is proposed to determine the bitrate of 360-degree videos,aim⁃ing to enhance the quality of experience(QoE)of users and reduce the cost of video content providers(VCP).The CAMPC algorithm first em⁃ploys a neural network to generate the content richness and combines it with the current field of view(FOV)to accurately predict the probability distribution of tiles being viewed.Then,for the tiles in the predicted viewport which directly affect QoE,the CAMPC algorithm utilizes a multi-step prediction for future system states,and accordingly selects the bitrates of multiple subsequent steps,instead of an instantaneous state.Meanwhile,it controls the buffer occupancy to eliminate the impact of prediction errors.We implement CAMPC on players by building a 360-degree video streaming platform and evaluating other advanced adaptive bitrate(ABR)rules through the real network.Experimental results show that CAMPC can save 83.5%of bandwidth resources compared with the scheme that completely transmits the tiles outside the viewport with the Dynamic Adaptive Streaming over HTTP(DASH)protocol.Besides,the proposed method can improve the system utility by 62.7%and 27.6%compared with the DASH official and viewport-based rules,respectively.展开更多
A simple and effective content-aware image resizing method is proposed based on the row / column merging and improved importance diffusion,which preserves the important regions in an image as well as the global visual...A simple and effective content-aware image resizing method is proposed based on the row / column merging and improved importance diffusion,which preserves the important regions in an image as well as the global visual effect. By repeatedly merging two rows / columns into one row / column or inserting a new row /column between two rows / columns, this method realizes image-resolution reduction and expansion. The importance of the merged row / column is promoted and diffused to four rows / columns around the merged one,which is to avoid the unwanted image distortions resulted from excessively merging of un-important regions. In addition,the proposed method introduces the direction of gradient vector in the low-pass filter to reduce the interference caused by complex texture background and protect important content better. Furthermore,according to human mechanics principles,different weights are given to the row and column direction components of gradient vectors which can obtain better global visual effect. Experimented results show that the proposed method satisfied in not only visual effect but also objective evaluation.展开更多
文摘In low-light image enhancement,prevailing Retinex-based methods often struggle with precise illumina-tion estimation and brightness modulation.This can result in issues such as halo artifacts,blurred edges,and diminished details in bright regions,particularly under non-uniform illumination conditions.We propose an innovative approach that refines low-light images by leveraging an in-depth awareness of local content within the image.By introducing multi-scale effective guided filtering,our method surpasses the limitations of traditional isotropic filters,such as Gaussian filters,in handling non-uniform illumination.It dynamically adjusts regularization parameters in response to local image characteristics and significantly integrates edge perception across different scales.This balanced approach achieves a harmonious blend of smoothing and detail preservation,enabling more accurate illumination estimation.Additionally,we have designed an adaptive gamma correction function that dynamically adjusts the brightness value based on local pixel intensity,further balancing enhancement effects across different brightness levels in the image.Experimental results demonstrate the effectiveness of our proposed method for non-uniform illumination images across various scenarios.It exhibits superior quality and objective evaluation scores compared to existing methods.Our method effectively addresses potential issues that existing methods encounter when processing non-uniform illumination images,producing enhanced images with precise details and natural,vivid colors.
基金The authors received Sichuan Science and Technology Program(No.18YYJC1917)funding for this study.
文摘Medical image segmentation plays an important role in clinical diagnosis,quantitative analysis,and treatment process.Since 2015,U-Net-based approaches have been widely used formedical image segmentation.The purpose of the U-Net expansive path is to map low-resolution encoder feature maps to full input resolution feature maps.However,the consecutive deconvolution and convolutional operations in the expansive path lead to the loss of some high-level information.More high-level information can make the segmentationmore accurate.In this paper,we propose MU-Net,a novel,multi-path upsampling convolution network to retain more high-level information.The MU-Net mainly consists of three parts:contracting path,skip connection,and multi-expansive paths.The proposed MU-Net architecture is evaluated based on three different medical imaging datasets.Our experiments show that MU-Net improves the segmentation performance of U-Net-based methods on different datasets.At the same time,the computational efficiency is significantly improved by reducing the number of parameters by more than half.
基金Supported by the National Natural Science Foundation of China (61901308)。
文摘Background Owing to the limitations of the working principle of three-dimensional(3D) scanning equipment, the point clouds obtained by 3D scanning are usually sparse and unevenly distributed. Method In this paper, we propose a new generative adversarial network(GAN) that extends PU-GAN for upsampling of point clouds. Its core architecture aims to replace the traditional self-attention(SA) module with an implicit Laplacian offset attention(OA) module and to aggregate the adjacency features using a multiscale offset attention(MSOA)module, which adaptively adjusts the receptive field to learn various structural features. Finally, residual links are added to create our residual multiscale offset attention(RMSOA) module, which utilizes multiscale structural relationships to generate finer details. Result The results of several experiments show that our method outperforms existing methods and is highly robust.
基金supported in part by ZTE Corporation under Grant No.2021420118000065.
文摘A content-aware multi-step prediction control(CAMPC)algorithm is proposed to determine the bitrate of 360-degree videos,aim⁃ing to enhance the quality of experience(QoE)of users and reduce the cost of video content providers(VCP).The CAMPC algorithm first em⁃ploys a neural network to generate the content richness and combines it with the current field of view(FOV)to accurately predict the probability distribution of tiles being viewed.Then,for the tiles in the predicted viewport which directly affect QoE,the CAMPC algorithm utilizes a multi-step prediction for future system states,and accordingly selects the bitrates of multiple subsequent steps,instead of an instantaneous state.Meanwhile,it controls the buffer occupancy to eliminate the impact of prediction errors.We implement CAMPC on players by building a 360-degree video streaming platform and evaluating other advanced adaptive bitrate(ABR)rules through the real network.Experimental results show that CAMPC can save 83.5%of bandwidth resources compared with the scheme that completely transmits the tiles outside the viewport with the Dynamic Adaptive Streaming over HTTP(DASH)protocol.Besides,the proposed method can improve the system utility by 62.7%and 27.6%compared with the DASH official and viewport-based rules,respectively.
基金Sponsored by the Natural Science Foundation of China(Grant No.61371099)the Heilongjiang Province Programs for Science and Technology Development(Grant No.GC12A305)
文摘A simple and effective content-aware image resizing method is proposed based on the row / column merging and improved importance diffusion,which preserves the important regions in an image as well as the global visual effect. By repeatedly merging two rows / columns into one row / column or inserting a new row /column between two rows / columns, this method realizes image-resolution reduction and expansion. The importance of the merged row / column is promoted and diffused to four rows / columns around the merged one,which is to avoid the unwanted image distortions resulted from excessively merging of un-important regions. In addition,the proposed method introduces the direction of gradient vector in the low-pass filter to reduce the interference caused by complex texture background and protect important content better. Furthermore,according to human mechanics principles,different weights are given to the row and column direction components of gradient vectors which can obtain better global visual effect. Experimented results show that the proposed method satisfied in not only visual effect but also objective evaluation.
