Image restoration using a neural network

Zhou, Yingsheng; Chellappa, Rama; Vaid, Akhil; Jenkins, B. Keith

doi:10.1109/29.1641

Cited by 373 publications

(139 citation statements)

References 17 publications

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“…Comparing these two expressions by mapping neuron to pixel and extending the generic clusters in (13) The parameter has a unity value as the space-invariance of the PSF causes the intercluster synaptic strength to be as strong as the intracluster synaptic strength. These results agree with the weight structure of other network-based algorithm [23], [27], [30]. The regularization parameter of the pixels in the th cluster for the th recursion is given by (23) where is the average local variance of the th cluster, and , are the lower and upper regularization thresholds given by (24) (25) , , , and are the lower and upper regularization values in the beginning and final recursion.…”

Section: B Optimization Of Image-domain Cost Function As Hcm Energy supporting

confidence: 77%

“…These results agree with the weight structure of other network-based algorithm [23], [27], [30]. The regularization parameter of the pixels in the th cluster for the th recursion is given by (23) where is the average local variance of the th cluster, and , are the lower and upper regularization thresholds given by (24) (25) , , , and are the lower and upper regularization values in the beginning and final recursion. We adopt a logarithmic-based regularization function, as suggested in [27], as it is commonly used to model the nonlinear transformation of human perception.…”

Section: B Optimization Of Image-domain Cost Function As Hcm Energy supporting

confidence: 77%

“…The combination of optimal support and one-hop updating reduces the time complexity of our algorithm to per iteration. In contrast, other network-based restorations [23], [24] require a time complexity of per iteration. Therefore, the technique provides a considerable computational advantage over traditional network-based methods.…”

Section: Cluster Formation and Restorationmentioning

confidence: 99%

“…A cost function consisting of a data fidelity measure, image, and blur-domain regularization terms, and the new soft blur estimation error is proposed as (2) where and are the diagonal image and blur-domain regularization matrices, and are standard Laplacian highpass operators [23], [27], and is the soft-decision error between the computed blur and parametric estimate . When the PSF or is space-invariant, assumes the structure of block Toeplitz matrix.…”

Section: Alternating Minimizationmentioning

confidence: 99%

See 3 more Smart Citations

A recursive soft-decision approach to blind image deconvolution

Yap

Guan

Liu

2003

IEEE Trans. Signal Process.

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Abstract-This paper presents a new approach to blind image deconvolution based on soft-decision blur identification and hierarchical neural networks. Traditional blind algorithms require a hard-decision on whether the blur satisfies a parametric form before their formulations. As the blurring function is usually unknown a priori, this precondition inhibits the incorporation of parametric blur knowledge domain into the restoration schemes. The new technique addresses this difficulty by providing a continual soft-decision blur adaptation with respect to the best-fit parametric structure throughout deconvolution. The approach integrates the knowledge of well-known blur models without compromising its flexibility in restoring images degraded by nonstandard blurs. An optimization scheme is developed where a new cost function is projected and minimized with respect to the image and blur domains. A nested neural network, called the hierarchical cluster model is employed to provide an adaptive, perception-based restoration. Its sparse synaptic connections are instrumental in reducing the computational cost of restoration. Conjugate gradient optimization is adopted to identify the blur due to its computational efficiency. The approach is shown experimentally to be effective in restoring images degraded by different blurs.

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Section: B Optimization Of Image-domain Cost Function As Hcm Energy supporting

confidence: 77%

Section: B Optimization Of Image-domain Cost Function As Hcm Energy supporting

confidence: 77%

Section: Cluster Formation and Restorationmentioning

confidence: 99%

Section: Alternating Minimizationmentioning

confidence: 99%

See 2 more Smart Citations

A recursive soft-decision approach to blind image deconvolution

Yap

Guan

Liu

2003

IEEE Trans. Signal Process.

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“…Specifically, we present a low-complexity detector/decoder for large MIMO systems, including V-BLAST and high-rate non-orthogonal STBCs [13]. The proposed detector has its roots in past work on Hopfield neural network (HNN) based algorithms for image restoration [14], [15], which are meant to handle large digital images. HNN based image restoration algorithms in [15] are applied to multiuser detection (MUD) in CDMA systems on AWGN channels in [15].…”

mentioning

confidence: 99%

Large MIMO Systems: A Low-Complexity Detector at High Spectral Efficiencies

Mohammed

Vardhan

Chockalingam

et al. 2008

2008 IEEE International Conference on Communications

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Abstract-We consider large MIMO systems, where by 'large' we mean number of transmit and receive antennas of the order of tens to hundreds. Such large MIMO systems will be of immense interest because of the very high spectral efficiencies possible in such systems. We present a low-complexity detector which achieves uncoded near-exponential diversity performance for hundreds of antennas in V-BLAST (i.e., achieves near SISO AWGN performance in a large MIMO fading environment) with an average per-bit complexity of just O(NtNr), where Nt and Nr denote the number of transmit and receive antennas, respectively. With an outer turbo code, the proposed detector achieves good coded bit error performance as well. For example, in a 600 transmit and 600 receive antennas V-BLAST system with a high spectral efficiency of 200 bps/Hz (using BPSK and rate-1/3 turbo code), our simulation results show that the proposed detector performs close to within about 4.6 dB of the theoretical capacity. We also adopt the proposed detector for the low-complexity decoding of high-rate non-orthogonal spacetime block codes (STBC) from division algebras (DA). We have decoded the 16 × 16 full-rate STBC from DA using the proposed detector and show that it performs close to within about 5.5 dB of the capacity using 4-QAM and rate-3/4 turbo code at a spectral efficiency of 24 bps/Hz. The practical feasibility of the proposed high-performance low-complexity detector could trigger wide interest in the implementation of large MIMO systems. Keywords - I. INTRODUCTION MIMO techniques offer transmit diversity and high data rates through the use of multiple transmit antennas [1]-[5]. A key component of a MIMO system is the MIMO detector at the receiver, which, in practice, is often the bottleneck for the overall performance and complexity. MIMO detectors including sphere decoder and several of its variants [6]-[10] achieve near-maximum likelihood (ML) performance at the cost of high complexity. Other well known detectors including ZF (zero forcing), MMSE (minimum mean square error), and ZF-SIC (ZF with successive interference cancellation) detectors [3] are attractive from a complexity view point, but achieve relatively poor performance. For example, the ZF-SIC detector (i.e., the well known V-BLAST detector with ordering [11]) does not achieve the full diversity in the system. The MMSE-SIC detector has been shown to achieve optimal performance [3]. However, the order of per-bit complexity involved in MMSE-SIC and ZF-SIC detectors is cubic in number of antennas. Even reduced complexity detectors (e.g., [12]) are prohibitively complex for large number of antennas of the order of hundreds. With small number of antennas, the high capacity potential of MIMO is not fully exploited. A key issue with using large number of antennas, however, is the high detection complexities involved. Our focus in this paper is on large MIMO systems, where by 'large' we mean number of transmit and receive antennas of the order of tens to hundreds. Such large MIMO systems

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A new approach for ME image restoration based on CNN

Zhao

1999

Int. J. Circ. Theor. Appl.

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Image restoration using a neural network

Cited by 373 publications

References 17 publications

A recursive soft-decision approach to blind image deconvolution

A recursive soft-decision approach to blind image deconvolution

Large MIMO Systems: A Low-Complexity Detector at High Spectral Efficiencies

A new approach for ME image restoration based on CNN

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