When dehazing underwater images,the patch-by-patch dark channel prior(DCP) method is frequently used.After the DCP-based processing,there are still some drawbacks,such as patch artifacts,and these artifacts will serio...When dehazing underwater images,the patch-by-patch dark channel prior(DCP) method is frequently used.After the DCP-based processing,there are still some drawbacks,such as patch artifacts,and these artifacts will seriously affect the subjective quality of some challenging images.To remove the patch artifacts from the DCP-guided enhancement mechanism,this paper proposes a coordinated underwater dark channel prior(CUDCP) method.The proposed method considers the characteristics of the red-green-blue channels with different attenuation situations,and thus the attenuation ratios of the red-green-blue channels are adaptively coordinated in diverse images.The requirement for color restoration is then assessed by an evaluation criterion,and the color restoration is carried out by using the compensated gray world(CGW) theory,which further coordinates the intensity of various red-green-blue channels.Our method next applies a patch-division average filter in accordance with the sub-patch classification.On the typical dataset,the enhanced images of our CUDCP method have higher average underwater image quality measure(UIQM) scores(about 2.274 8) when compared with the original images and those of some state-of-the-art enhancement methods,while the computational cost of CUDCP(about 88.618 8 s) is slightly higher than that of the original DCP(about 87.493 8 s).The experimental results demonstrate that in comparison to state-of-the-art enhancement methods,the proposed method can significantly reduce patch artifacts in challenging image enhancement,while maintaining the objective quality of such underwater images,and also enhancing their subjective quality at a reasonable computational cost.展开更多
The nonlocal means( NLM) has been widely used in image processing. In this paper,we introduce a modified weight function for NLM denoising, which will compute the nonlocal similarities among the pre-processing pixel p...The nonlocal means( NLM) has been widely used in image processing. In this paper,we introduce a modified weight function for NLM denoising, which will compute the nonlocal similarities among the pre-processing pixel patches instead of the commonly used similarity measure based on noisy observations. By the law of large number,the norm for the pre-processing pixel patches is closer to the norm of the original clean pixel patches,so the proposed weight functions are more optimized and the selected similar patches are more accurate. Experimental results indicate the proposed algorithm achieves better restored results compared to the classical NLM's method.展开更多
Sparse representation has attracted extensive attention and performed well on image super-resolution（SR） in the last decade. However, many current image SR methods face the contradiction of detail recovery and artif...Sparse representation has attracted extensive attention and performed well on image super-resolution（SR） in the last decade. However, many current image SR methods face the contradiction of detail recovery and artifact suppression. We propose a multi-resolution dictionary learning（MRDL） model to solve this contradiction, and give a fast single image SR method based on the MRDL model. To obtain the MRDL model, we first extract multi-scale patches by using our proposed adaptive patch partition method（APPM）. The APPM divides images into patches of different sizes according to their detail richness. Then, the multiresolution dictionary pairs, which contain structural primitives of various resolutions, can be trained from these multi-scale patches.Owing to the MRDL strategy, our SR algorithm not only recovers details well, with less jag and noise, but also significantly improves the computational efficiency. Experimental results validate that our algorithm performs better than other SR methods in evaluation metrics and visual perception.展开更多
A super-resolution reconstruction algorithm is proposed. The algorithm is based on the idea of the sparse representation of signals, by using the fact that the sparsest representation of a sig- nal is unique as the co...A super-resolution reconstruction algorithm is proposed. The algorithm is based on the idea of the sparse representation of signals, by using the fact that the sparsest representation of a sig- nal is unique as the constraint of the patched-based reconstruction, and compensating residual errors of the reconstruction results both locally and globally to solve the distortion problem in patch-based reconstruction algorithms. Three reconstruction algorithms are compared. The results show that the images reconstructed with the new algorithm have the best quality.展开更多
A simple and effective image inpainting method is proposed in this paper, which is proved to be suitable for different kinds of target regions with shapes from little scraps to large unseemly objects in a wide range o...A simple and effective image inpainting method is proposed in this paper, which is proved to be suitable for different kinds of target regions with shapes from little scraps to large unseemly objects in a wide range of images. It is an important improvement upon the traditional image inpainting techniques. By introducing a new bijeetive-mapping term into the matching cost function, the artificial repetition problem in the final inpainting image is practically solved. In addition, by adopting an inpainting error map, not only the target pixels are refined gradually during the inpainting process but also the overlapped target patches are combined more seamlessly than previous method. Finally, the inpainting time is dramatically decreased by using a new acceleration method in the matching process.展开更多
In this paper, a novel Magnetic Resonance (MR) reconstruction framework which combines image-wise and patch-wise sparse prior is proposed. For addressing, a truncated beta-Bernoulli process is firstly employed to enfo...In this paper, a novel Magnetic Resonance (MR) reconstruction framework which combines image-wise and patch-wise sparse prior is proposed. For addressing, a truncated beta-Bernoulli process is firstly employed to enforce sparsity on overlapping image patches emphasizing local structures. Due to its properties, beta-Bernoulli process can adaptive infer the sparsity (number of non-zero coefficients) of each patch, an appropriate dictionary, and the noise variance simultaneously, which are prerequisite for iterative image reconstruction. Secondly, a General Gaussian Distribution (GGD) prior is introduced to engage image-wise sparsity for wavelet coefficients, which can be then estimated by a threshold denoising algorithm. Finally, MR image is reconstructed by patch-wise estimation, image-wise estimation and under-sampled k-space data with least square data fitting. Experimental results have demonstrated that proposed approach exhibits excellent reconstruction performance. Moreover, if the image is full of similar low-dimensional-structures, proposed algorithm has dramatically improved Peak Signal to Noise Ratio (PSNR) 7～9 dB, with comparisons to other state-of-art compressive sampling methods.展开更多
A broadband microstrip patch antenna was analyzed and designed.Full wave analysis method(FWAM) was employed to show that a stacked microstrip dual patch antenna(SMDPA) might have a much wider bandwidth than that of ...A broadband microstrip patch antenna was analyzed and designed.Full wave analysis method(FWAM) was employed to show that a stacked microstrip dual patch antenna(SMDPA) might have a much wider bandwidth than that of the ordinanry uni patch one.By means of discrete complex image theory(DCIT),the Sommerfeld integrals (SI) involved were accurately calculated at a speed several hundred times faster than numerical integration method(NIM).The feeding structure of the SMDPA was then improved and the bandwidth was extended to about 22% or more for voltage standing wave ratio (VSWR)s≤2 Finally,a matching network was constructed to obtain a bandwidth of about 25% for s≤1.5.展开更多
基金supported by the Graduate Student Innovation Fund of Donghua University (No.GSIF-DH-M-2022011)the National Natural Science Foundation of China (No.62001099)。
文摘When dehazing underwater images,the patch-by-patch dark channel prior(DCP) method is frequently used.After the DCP-based processing,there are still some drawbacks,such as patch artifacts,and these artifacts will seriously affect the subjective quality of some challenging images.To remove the patch artifacts from the DCP-guided enhancement mechanism,this paper proposes a coordinated underwater dark channel prior(CUDCP) method.The proposed method considers the characteristics of the red-green-blue channels with different attenuation situations,and thus the attenuation ratios of the red-green-blue channels are adaptively coordinated in diverse images.The requirement for color restoration is then assessed by an evaluation criterion,and the color restoration is carried out by using the compensated gray world(CGW) theory,which further coordinates the intensity of various red-green-blue channels.Our method next applies a patch-division average filter in accordance with the sub-patch classification.On the typical dataset,the enhanced images of our CUDCP method have higher average underwater image quality measure(UIQM) scores(about 2.274 8) when compared with the original images and those of some state-of-the-art enhancement methods,while the computational cost of CUDCP(about 88.618 8 s) is slightly higher than that of the original DCP(about 87.493 8 s).The experimental results demonstrate that in comparison to state-of-the-art enhancement methods,the proposed method can significantly reduce patch artifacts in challenging image enhancement,while maintaining the objective quality of such underwater images,and also enhancing their subjective quality at a reasonable computational cost.
基金National Natural Science Foundations of China(Nos.U1504603,61301229)Key Scientific Research Project of Colleges and Universities in Henan Province,China(Nos.18A120002,19A110014)
文摘The nonlocal means( NLM) has been widely used in image processing. In this paper,we introduce a modified weight function for NLM denoising, which will compute the nonlocal similarities among the pre-processing pixel patches instead of the commonly used similarity measure based on noisy observations. By the law of large number,the norm for the pre-processing pixel patches is closer to the norm of the original clean pixel patches,so the proposed weight functions are more optimized and the selected similar patches are more accurate. Experimental results indicate the proposed algorithm achieves better restored results compared to the classical NLM's method.
文摘Sparse representation has attracted extensive attention and performed well on image super-resolution（SR） in the last decade. However, many current image SR methods face the contradiction of detail recovery and artifact suppression. We propose a multi-resolution dictionary learning（MRDL） model to solve this contradiction, and give a fast single image SR method based on the MRDL model. To obtain the MRDL model, we first extract multi-scale patches by using our proposed adaptive patch partition method（APPM）. The APPM divides images into patches of different sizes according to their detail richness. Then, the multiresolution dictionary pairs, which contain structural primitives of various resolutions, can be trained from these multi-scale patches.Owing to the MRDL strategy, our SR algorithm not only recovers details well, with less jag and noise, but also significantly improves the computational efficiency. Experimental results validate that our algorithm performs better than other SR methods in evaluation metrics and visual perception.
基金Supported by the Basic Research Foundation of Beijing Institute of Technology(3050012211105)
文摘A super-resolution reconstruction algorithm is proposed. The algorithm is based on the idea of the sparse representation of signals, by using the fact that the sparsest representation of a sig- nal is unique as the constraint of the patched-based reconstruction, and compensating residual errors of the reconstruction results both locally and globally to solve the distortion problem in patch-based reconstruction algorithms. Three reconstruction algorithms are compared. The results show that the images reconstructed with the new algorithm have the best quality.
基金Supported by the National Natural Science Foundation of China （No. 60403044, No. 60373070） and partly funded by Microsoft Research Asia： Project 2004-Image-01.
文摘A simple and effective image inpainting method is proposed in this paper, which is proved to be suitable for different kinds of target regions with shapes from little scraps to large unseemly objects in a wide range of images. It is an important improvement upon the traditional image inpainting techniques. By introducing a new bijeetive-mapping term into the matching cost function, the artificial repetition problem in the final inpainting image is practically solved. In addition, by adopting an inpainting error map, not only the target pixels are refined gradually during the inpainting process but also the overlapped target patches are combined more seamlessly than previous method. Finally, the inpainting time is dramatically decreased by using a new acceleration method in the matching process.
基金Supported by the National Natural Science Foundation of China (No. 30900328, 61172179)the Fundamental Research Funds for the Central Universities (No.2011121051)the Natural Science Foundation of Fujian Province of China (No. 2012J05160)
文摘In this paper, a novel Magnetic Resonance (MR) reconstruction framework which combines image-wise and patch-wise sparse prior is proposed. For addressing, a truncated beta-Bernoulli process is firstly employed to enforce sparsity on overlapping image patches emphasizing local structures. Due to its properties, beta-Bernoulli process can adaptive infer the sparsity (number of non-zero coefficients) of each patch, an appropriate dictionary, and the noise variance simultaneously, which are prerequisite for iterative image reconstruction. Secondly, a General Gaussian Distribution (GGD) prior is introduced to engage image-wise sparsity for wavelet coefficients, which can be then estimated by a threshold denoising algorithm. Finally, MR image is reconstructed by patch-wise estimation, image-wise estimation and under-sampled k-space data with least square data fitting. Experimental results have demonstrated that proposed approach exhibits excellent reconstruction performance. Moreover, if the image is full of similar low-dimensional-structures, proposed algorithm has dramatically improved Peak Signal to Noise Ratio (PSNR) 7～9 dB, with comparisons to other state-of-art compressive sampling methods.
文摘A broadband microstrip patch antenna was analyzed and designed.Full wave analysis method(FWAM) was employed to show that a stacked microstrip dual patch antenna(SMDPA) might have a much wider bandwidth than that of the ordinanry uni patch one.By means of discrete complex image theory(DCIT),the Sommerfeld integrals (SI) involved were accurately calculated at a speed several hundred times faster than numerical integration method(NIM).The feeding structure of the SMDPA was then improved and the bandwidth was extended to about 22% or more for voltage standing wave ratio (VSWR)s≤2 Finally,a matching network was constructed to obtain a bandwidth of about 25% for s≤1.5.
