中国科学院机构知识库网格系统: 检索

Illumination Guided Attentive Wavelet Network for Low-Light Image Enhancement 期刊论文 OAI收割
IEEE TRANSACTIONS ON MULTIMEDIA, 2023, 卷号: 25, 页码: 6258-6271
作者: Xu, Jingzhao; Yuan, Mengke; Yan, Dong-Ming; Wu, Tieru

|

收藏 | 浏览/下载：11/0 | 提交时间：2024/02/22

Lighting Wavelet transforms Image enhancement Frequency modulation Wavelet coefficients Noise reduction Discrete wavelet transforms Attention mechanism illumination guidance low-light image enhancement wavelet transform

Image coding using wavelet-based compressive sampling (EI CONFERENCE) 会议论文 OAI收割
2012 5th International Symposium on Computational Intelligence and Design, ISCID 2012, October 28, 2012 - October 29, 2012, Hangzhou, China
作者: Li J.; Li J.; Li J.

收藏 | 浏览/下载：43/0 | 提交时间：2013/03/25

In this paper we proposed a novel coding scheme is proposed using wavelet-based CS framework for nature image. First two-dimension discrete wavelet transform (DWT) is applied to a nature image for sparse representation. After multi-scale DWT the low-frequency sub-band and high-frequency sub-bands are re-sampled separately. According to the statistical dependences among DWT coefficients we allocate different measurements to low- and high-frequency component. Then the measurements samples can be quantized. The quantize samples are entropy coded and forward correct coding (FEC). Finally the compressed streams are transmitted. At the decoder one can simply reconstruct the image via l1 minimization. Experimental results show that the proposed wavelet-based CS scheme achieves better compression performance against the relevant existing solutions.

Multi-focus image fusion algorithm based on adaptive PCNN and wavelet transform (EI CONFERENCE) 会议论文 OAI收割
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications, May 24, 2011 - May 26, 2011, Beijing, China
Wu Z.-G.; Wang M.-J.; Han G.-L.

收藏 | 浏览/下载：75/0 | 提交时间：2013/03/25

Being an efficient method of information fusion image fusion has been used in many fields such as machine vision medical diagnosis military applications and remote sensing.In this paper Pulse Coupled Neural Network (PCNN) is introduced in this research field for its interesting properties in image processing including segmentation target recognition et al. and a novel algorithm based on PCNN and Wavelet Transform for Multi-focus image fusion is proposed. First the two original images are decomposed by wavelet transform. Then based on the PCNN a fusion rule in the Wavelet domain is given. This algorithm uses the wavelet coefficient in each frequency domain as the linking strength so that its value can be chosen adaptively. Wavelet coefficients map to the range of image gray-scale. The output threshold function attenuates to minimum gray over time. Then all pixels of image get the ignition. So the output of PCNN in each iteration time is ignition wavelet coefficients of threshold strength in different time. At this moment the sequences of ignition of wavelet coefficients represent ignition timing of each neuron. The ignition timing of PCNN in each neuron is mapped to corresponding image gray-scale range which is a picture of ignition timing mapping. Then it can judge the targets in the neuron are obvious features or not obvious. The fusion coefficients are decided by the compare-selection operator with the firing time gradient maps and the fusion image is reconstructed by wavelet inverse transform. Furthermore by this algorithm the threshold adjusting constant is estimated by appointed iteration number. Furthermore In order to sufficient reflect order of the firing time the threshold adjusting constant is estimated by appointed iteration number. So after the iteration achieved each of the wavelet coefficient is activated. In order to verify the effectiveness of proposed rules the experiments upon Multi-focus image are done. Moreover comparative results of evaluating fusion quality are listed. The experimental results show that the method can effectively enhance the edge details and improve the spatial resolution of the image. 2011 SPIE.

Image compression algorithm of high-speed SPIHT for aerial applications (EI CONFERENCE) 会议论文 OAI收割
2011 IEEE 3rd International Conference on Communication Software and Networks, ICCSN 2011, May 27, 2011 - May 29, 2011, Xi'an, China
作者: Zhang K.; Zhang K.; Zhang K.; Zhang K.

收藏 | 浏览/下载：38/0 | 提交时间：2013/03/25

SPIHT and NLS (Not List SPIHT) are efficient compression algorithms but the algorithms application is limited by the shortcomings of the poor error resistance and slow compression speed in the aviation and other areas requiring high-speed compression. In this paper the error resilience and the compression speed are improved. The remote sensing images are decomposed by Le Gall5/3 wavelet and wavelet coefficients are indexed scanned and allocated by the means of family blocks. The bit-plane importance is predicted by bitwise OR so the N bit-planes can be encoded at the same time. Compared with the SPIHT algorithm this modified algorithm is easy implemented by hardware and the compression speed is improved. The PSNR of reconstructed images encoded by high-speed SPIHT is slightly lower than SPIHT at rate 1bpp but the speed is 4.5-6 times faster than SPIHT encoding process. The algorithm meets the high speed and reliability requirements of aerial applications. 2011 IEEE.

Image compression based on contourlet and no lists SPIHT (EI CONFERENCE) 会议论文 OAI收割
2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, CMCE 2010, August 24, 2010 - August 26, 2010, Changchun, China
作者: Zhang S.

收藏 | 浏览/下载：45/0 | 提交时间：2013/03/25

The volume of raw image data captured by the high resolution camera is extremely huge. Thus the efficient image compression method should be used to decrease the bit rate. The image compression method based on wavelet is used more widely nowadays. However two dimensional wavelet is only the tensor product of the one dimensional wavelet whose support region of basis function is extended from interval to square. Contourlet is an image multiscale geometric analysis tool which could represent image sparsely and has strong capability of nonlinear approximation. The basis function of contourlet is multidirectional and anisotropic. Nevertheless contourlet is redundant. So the non-redundant Wavelet Based Contourlet Transform (WBCT) is used in this paper. The SPIHT algorithm is very efficient way to coding the significant coefficients. And the improved no lists SPIHT is more easy to implemented by hardware. Image compression method based on the combination of both wavelet based contourlet transform and no lists SPIHT coding is proposed in the paper. Experiment shows that compared to wavelet based scheme the contourlet scheme can reserve the texture of the image. For barbara test image when coding at low bit rate the PSNR can improve about 0.2dB. 2010 IEEE.

Destriping method using lifting wavelet transform of remote sensing image (EI CONFERENCE) 会议论文 OAI收割
2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, CMCE 2010, August 24, 2010 - August 26, 2010, Changchun, China
作者: He B.

收藏 | 浏览/下载：30/0 | 提交时间：2013/03/25

Based on the characteristic of striping noise in remote sensing images a new destriping noise technique for the improved multi-threshold method using lifting wavelet transform applied to remote sensing imagery is presented in this letter. Have used the lifting wavelet decomposition algorithm the thresholds are determined by corresponding wavelet coefficients in every scale. Remote sensing imagery is so large that the algorithm must be fast and effective. The lifting wavelet transform is easily realized and inexpensive in computer time and storage space compared with the traditional wavelet transform. We also compare the method with some traditional destriping methods both by visual inspection and by appropriate indexes of quality of the denoised images. From the comparison we can see that the adaptive threshold method can preserve the spectral characteristic of the images while effectively remove striping noise and it did better than the existed ones. 2010 IEEE.

A new image fusion algorithm based on wavelet transform (EI CONFERENCE) 会议论文 OAI收割
2010 3rd International Conference on Advanced Computer Theory and Engineering, ICACTE 2010, August 20, 2010 - August 22, 2010, Chengdu, China
作者: He X.; Zhang Y.; Zhang L.-G.; Zhang L.-G.

收藏 | 浏览/下载：13/0 | 提交时间：2013/03/25

A new image fusion algorithm based on lifting wavelet transform is presented in this paper. The source images are decomposed using lifting wavelet transform respectively. Aiming at the coefficients of low frequency and high frequency this algorithm choose a different rule to fuse the image. To the low frequency the spatial frequency based on the neighborhood add consistency check is elected as the fusion guide. And the absolute maximum based on detail coefficients is selected as the guide to the high frequency. After that the fused image is obtained by using inverse lifting wavelet transform. Taking the ratio space frequency error and the mean gradient as criterions experimental results demonstrate that the algorithm is very effective. 2010 IEEE.

A MLP-PNN neural network for CCD image super-resolution in wavelet packet domain (EI CONFERENCE) 会议论文 OAI收割
2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008, October 12, 2008 - October 14, 2008, Dalian, China
Zhao X.; Fu D.; Zhai L.

收藏 | 浏览/下载：68/0 | 提交时间：2013/03/25

Image super-resolution methods process an input image sequence of a scene to obtain a still image with increased resolution. Classical approaches to this problem involve complex iterative minimization procedures typically with high computational costs. In this paper is proposed a novel algorithm for super-resolution that enables a substantial decrease in computer load. First decompose and reconstruct the image by wavelet packet. Before constructing the image use neural network in place of other rebuilding method to reconstruct the coefficients in the wavelet packet domain. Second probabilistic neural network architecture is used to perform a scattered-point interpolation of the image sequence data in the wavelet packet domain. The network kernel function is optimally determined for this problem by a MLP-PNN (Multi Layer Perceptron - Probabilistic Neural Network) trained on synthetic data. Network parameters dependent on the sequence noise level. This super-sampled image is spatially Altered to correct finite pixel size effects to yield the final high-resolution estimate. This method can decrease the calculation cost and get perfect PSNR. Results are presented showing the quality of the proposed method. 2008 IEEE.

High speed, low memory image coding using zero blocks of wavelet coefficients (EI CONFERENCE) 会议论文 OAI收割
4th International Conference on Image and Graphics, ICIG 2007, August 22, 2007 - August 24, 2007, Chengdu, China
作者: Zhang T.; Yang W.-G.

收藏 | 浏览/下载：31/0 | 提交时间：2013/03/25

We propose a new high speed low memory SPECK image coder based on the form of block-unit states called LBUS-SPECK which uses the form of block-unit states and the form of block exponents to substitute LIS and LSP of SPECK. A form of block-unit states is introduced to store the significance flag of coefficients and the value of the block-unit exponents. A form of block exponents is introduced to store the block exponents which are used to represent the max coefficient in a decomposed block. A new block depth-finding strategy is developed for searching insignificant sets at sorting stage. Experimental results show that the obtained PSNR values for the decoded images are very close to those of SPECK algorithm and the memory consumption is reduced by 9 times the encoding timing is saved about 15%. Comparing with SPECK algorithm this algorithm not only has better performance of the decoded images but also is easy to implement especially it provides an efficient way for hardware implementation of wavelet embedded block coding. 2007 IEEE.

Texture extraction of high resolution remote sensing image based on the characteristic of image wavelet coefficients - art. no. 67900F 会议论文 OAI收割
Remote Sensing and Gis Data Processing and Applications; and Innovative Multispectral Technology and Applications, Pts 1 and 2, Bellingham
Liu, Huichan; He, Guojin

收藏 | 浏览/下载：14/0 | 提交时间：2014/12/07

generalized Gaussian density wavelet coefficients Kullback-Leibler distance texture characterization