- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
Infrared Image Reconstruction Based on Archimedes Spiral Measurement Matrix
摘要： It is a new research direction to realize infrared (IR) image reconstruction using compressed sensing (CS) theory. In the field of CS, the construction of measurement matrix is very principal. At present, the types of measurement matrices are mainly random and deterministic. The random measurement matrix can well satisfy the property of measurement matrix, but needs a large amount of storage space and has an inconvenient in hardware implementation. Therefore, a deterministic measurement matrix construction method is proposed for IR image reconstruction in this paper. Firstly, a series of points are collected on Archimedes spiral to construct a definite sequence; then the initial measurement matrix is constructed; finally, the deterministic measurement matrix is obtained according to the required sampling rate. Simulation results show that the IR image could be reconstructed by the measured values obtained through the proposed measurement matrix. Moreover, the proposed measurement matrix has better reconstruction performance compared with the Gaussian and Bernoulli random measurement matrices.
关键词： deterministic measurement matrices,compressed sensing (CS),infrared image reconstruction
更新于2025-09-23 15:23:52
-
[IEEE 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA) - Coimbatore (2018.3.29-2018.3.31)] 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA) - Image Reconstruction Through Compressed Sensing Technique Using Gaussian Filteration
摘要： Image Processing is becoming as the one of the most emerging field in the research area, as we know in today's life, lot of people are using these images somewhere in their daily life. In this paper, i have developed the satellite images in which we can have even more clear view than the input taken image by using compressive reconstruction technique. After passing through some stages of the processing, we have used a gaussian filteration so that we can have a very good output image at an end. By taking various references through different sites I got a hope of developing a normal satellite taken image into clearly processed output image through the basis pursuit reconstruction and by doing filteration after it.
关键词： gaussian filter,compressed sensing,gaussian matrix,basis pursuit
更新于2025-09-23 15:23:52
-
Space-time variant weighted regularization in compressed sensing cardiac cine MRI
摘要： Purpose: To analyze the impact on image quality and motion fidelity of a motion-weighted space-time variant regularization term in compressed sensing cardiac cine MRI. Methods: k-t SPARSE-SENSE with temporal total variation (tTV) is used as the base reconstruction algorithm. Motion in the dynamic image is estimated by means of a robust registration technique for non-rigid motion. The resulting deformation fields are used to leverage the regularization term. The results are compared with standard k-t SPARSE-SENSE with tTV regularization as well as with an improved version of this algorithm that makes use of tTV and temporal Fast Fourier Transform regularization in x-f domain. Results: the proposed method with space-time variant regularization provides higher motion fidelity and image quality than the two previously reported methods. Difference images between undersampled reconstruction and fully sampled reference images show less systematic errors with the proposed approach. Conclusions: usage of a space-time variant regularization offers reconstructions with better image quality than the state of the art approaches used for comparison.
关键词： cine cardiac MRI,space-time variant regularization,k-t SPARSE-SENSE,compressed sensing
更新于2025-09-23 15:22:29
-
[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Reduction of Poisson Noise in Coded Exposure Photography
摘要： Coded exposure photography (CEP), originally proposed by Raskar et al., has been known as one of the promising techniques for motion deblurring. In this area, much efforts have been made for designing a fluttered shutter sequence to shape the spectrum of a uniformly motion-blurred image into an invertible one. Since the duty cycle of the fluttered shutters proposed thus far is generally low, the number of photons entering into an image sensor is reduced, which leads to a large Poisson noise in a low lighting condition. In the existing design techniques for the fluttered shutter, an increase of the duty cycle leads to a failure in the motion deblurring due to the singularities in the Fourier domain. To overcome the difficulty, this paper proposes a new motion deblurring framework using a higher duty-cycle fluttered shutter and a compressed sensing technique. The experimental results given in this paper demonstrate that the proposed technique is advantageous over a conventional one, in particular in a low lighting condition.
关键词： coded exposure photography,fluttered shutter,compressed sensing,image recovery,motion deblurring
更新于2025-09-23 15:22:29
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - An Iterative Adaptive Reweighted Norm Minimization Sparsity Autofocus Algorithm via Bayesian Recovery for Array SAR Imaging
摘要： The influence of phase error in echo signal is rarely considered or corrected by most classical compressed sensing (CS) algorithms, and reduces the quality of imaging results. In order to improve the quality of array synthetic aperture radar (ASAR) imaging, a new CS algorithm called Iterative Adaptive Reweighted Norm Minimization Sparsity Autofocus algorithm via Bayesian Recovery (IARNSABR) was proposed in this paper. Based on the principle of Bayesian Recovery, the iterative adaptive reweighted norm minimization method has been used in the process of reconstruction. The theoretical model and the process of imaging of IARNSABR has been established. And the proposed algorithm can correct the influence of phase error more effectively, and has stronger ability of eliminating the false targets. Through simulation and experiment results, IARNSABR can achieve higher quality imaging than SAFBRIM.
关键词： Compressed Sensing,Sparse autofocus,Iterative Reweighted Adaptive Norm Minimization,ASAR
更新于2025-09-23 15:22:29
-
Two-Dimensional Compressed Sensing Using Two-Dimensional Random Permutation for Image Encryption-then-Compression Applications
摘要： Block compressed sensing with random permutation (BCS-RP) has been shown to be very effective for image Encryption-then-Compression (ETC) applications. However, in the BCS-RP scheme, the statistical information of the blocks is disclosed, because the encryption is conducted within each small block of the image. To solve this problem, a two-dimension compressed sensing (2DCS) with 2D random permutation (2DRP) strategy for image ETC applications is proposed in this letter, where the 2DRP strategy is used for encrypting the image and the 2DCS scheme is used for compressing the encrypted image. Compared with the BCS-RP scheme, the proposed approach has two benefits. Firstly, it offers better security. Secondly, it obtains a significant gain of peak signal-to-noise ratio (PSNR) of the reconstructed-images.
关键词： image encryption,image compression,two-dimension compressed sensing,Encryption-then-Compression,two-dimension random permutation
更新于2025-09-23 15:22:29
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - The Recovery Algorithm of Saturated Sar Raw Data Based on Compressed Sensing
摘要： Because of the unprediction of the scene scattering characteristic and the finite quantization bits, saturated data always exists. Saturation phenomenon leads to a non-linear distortion and interferes to the recognition of the target so that it affects the image quality. Especially when the scene scattering characteristic largely varies, it can generate false targets and degrade signal-to-noise ratio (SNR). Compressed sensing (CS), a non-linear reconstructed algorithm, is that samples in sub-Nyquist rate is used to recover the sparse signal with few non-zero elements. This paper proposes the recovery method based on the non-linear characteristic of CS to recover the saturated part of the raw data to the unsaturation state and ensure the unsaturated parts maintain the original state. Simulation results validate the proposed method.
关键词： non-linear,compressed sensing,SAR raw data,saturation,Synthetic aperture radar
更新于2025-09-23 15:22:29
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Efficient Autofocus for 3-D SAR Sparse Imaging Based on Joint Criterion Optimization
摘要： This paper presents an efficient sparse autofocusing algorithm for 3-D SAR imaging based on joint criterion optimization. Exploiting by the least square (LS) regularization sparse recovery technique, an autofocus model combined with minimum mean square error criterion and maximum sharpness criterion, is constructed for 3-D SAR sparse image formation via linear measurement expression. Moreover, the adaptive weighted factor for phase error estimation is derived. Then, a joint iterative estimated method is introduced to efficiency estimate the phase errors. Numerical simulation and experimental results are provided to demonstrate the effectiveness of the proposed algorithm with different types of phase error.
关键词： 3-D SAR,phase error,compressed sensing,sparse imaging,maximum sharpness autofocus
更新于2025-09-23 15:22:29
-
Comparison of basis functions and q-space sampling schemes for robust compressed sensing reconstruction accelerating diffusion spectrum imaging
摘要： Time constraints placed on magnetic resonance imaging often restrict the application of advanced diffusion MRI (dMRI) protocols in clinical practice and in high throughput research studies. Therefore, acquisition strategies for accelerated dMRI have been investigated to allow for the collection of versatile and high quality imaging data, even if stringent scan time limits are imposed. Diffusion spectrum imaging (DSI), an advanced acquisition strategy that allows for a high resolution of intra-voxel microstructure, can be sufficiently accelerated by means of compressed sensing (CS) theory. CS theory describes a framework for the efficient collection of fewer samples of a data set than conventionally required followed by robust reconstruction to recover the full data set from sparse measurements. For an accurate recovery of DSI data, a suitable acquisition scheme for sparse q-space sampling and the sensing and sparsifying bases for CS reconstruction need to be selected. In this work we explore three different types of q-space undersampling schemes and two frameworks for CS reconstruction based on either Fourier or SHORE basis functions. After CS recovery, diffusion and microstructural parameters and orientational information are estimated from the reconstructed data by means of state-of-the-art processing techniques for dMRI analysis. By means of simulation, diffusion phantom and in vivo DSI data, an isotropic distribution of q-space samples was found to be optimal for sparse DSI. The CS reconstruction results indicate superior performance of Fourier-based CS-DSI compared to the SHORE-based approach. Based on these findings we outline an experimental design for accelerated DSI and robust CS reconstruction of the sparse measurements that is suitable for the application within time-limited studies.
关键词： diffusion MRI,sparse acquisition,q-space undersampling,microstructure,compressed sensing,basis functions,diffusion spectrum imaging
更新于2025-09-23 15:22:29
-
SparseCast: Hybrid Digital-Analog Wireless Image Transmission Exploiting Frequency Domain Sparsity
摘要： A hybrid digital-analog wireless image transmission scheme, called SparseCast, is introduced, which provides graceful degradation with channel quality. SparseCast achieves improved end-to-end reconstruction quality while reducing the bandwidth requirement by exploiting frequency domain sparsity through compressed sensing. The proposed algorithm produces a linear relationship between the channel signal-to-noise ratio (CSNR) and peak signal-to-noise ratio (PSNR), without requiring the channel state knowledge at the transmitter. This is particularly attractive when transmitting to multiple receivers or over unknown time-varying channels, as the receiver PSNR depends on the experienced channel quality, and is not bottlenecked by the worst channel. SparseCast is benchmarked against two alternative algorithms: SoftCast and BCS-SPL. Our findings show that the proposed algorithm outperforms SoftCast by approximately 3.1 dB and BCS-SPL by 14.8 dB.
关键词： hybrid digital-analog,compressed sensing,channel signal-to-noise ratio,frequency domain sparsity,peak signal-to-noise ratio,wireless image transmission
更新于2025-09-23 15:21:21