Superfast superresolution

Šroubek, Filip; Kamenicky, Jan; Milanfar, Peyman

doi:10.1109/icip.2011.6115633

Cited by 9 publications

(14 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This paper aims at reducing the computational cost of these SR methods by proposing a new approach handling the decimation and blurring operators simultaneously by exploring their intrinsic properties in the frequency domain. It is interesting to note that similar properties were explored in [22] and [23] for multi-frame SR. However, the implementation of the matrix inversions proposed in [22] and [23] are less efficient than those proposed in this work, as it will be demonstrated in the complexity analysis conducted in Section III.…”

Section: Introductionmentioning

confidence: 90%

“…It is interesting to note that similar properties were explored in [22] and [23] for multi-frame SR. However, the implementation of the matrix inversions proposed in [22] and [23] are less efficient than those proposed in this work, as it will be demonstrated in the complexity analysis conducted in Section III. More precisely, this paper derives a closed-form expression of the solution associated with the ℓ 2 -penalized least-squares SR problem, when the observed LR image is assumed to be a noisy, subsampled and blurred version of the HR image with a spatially invariant blur.…”

Section: Introductionmentioning

confidence: 90%

See 1 more Smart Citation

Fast Single Image Super-Resolution Using a New Analytical Solution for <inline-formula> <tex-math notation="LaTeX">$\ell _{2}$ </tex-math> </inline-formula>–<inline-formula> <tex-math notation="LaTeX">$\ell _{2}$ </tex-math> </inline-formula> Problems

Zhao

Wei

Basarab

et al. 2016

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

This paper addresses the problem of single image super-resolution (SR), which consists of recovering a high-resolution image from its blurred, decimated, and noisy version. The existing algorithms for single image SR use different strategies to handle the decimation and blurring operators. In addition to the traditional first-order gradient methods, recent techniques investigate splitting-based methods dividing the SR problem into up-sampling and deconvolution steps that can be easily solved. Instead of following this splitting strategy, we propose to deal with the decimation and blurring operators simultaneously by taking advantage of their particular properties in the frequency domain, leading to a new fast SR approach. Specifically, an analytical solution is derived and implemented efficiently for the Gaussian prior or any other regularization that can be formulated into an l2 -regularized quadratic model, i.e., an l2 - l2 optimization problem. The flexibility of the proposed SR scheme is shown through the use of various priors/regularizations, ranging from generic image priors to learning-based approaches. In the case of non-Gaussian priors, we show how the analytical solution derived from the Gaussian case can be embedded into traditional splitting frameworks, allowing the computation cost of existing algorithms to be decreased significantly. Simulation results conducted on several images with different priors illustrate the effectiveness of our fast SR approach compared with existing techniques.

show abstract

Section: Introductionmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 90%

Fast Single Image Super-Resolution Using a New Analytical Solution for <inline-formula> <tex-math notation="LaTeX">$\ell _{2}$ </tex-math> </inline-formula>–<inline-formula> <tex-math notation="LaTeX">$\ell _{2}$ </tex-math> </inline-formula> Problems

Zhao

Wei

Basarab

et al. 2016

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

show abstract

“…The matrix A is typically a product of downsampling (e.g., decimation) and blurring operators. The most frequently used restoration method is given by Tikhonov's regularization model, see, e.g., [4], [7], [14],…”

Section: Notation and Prior Workmentioning

confidence: 99%

“…Signal (image) processing often uses total variation due to its remarkable ability to preserve contours/edges of signals. The total variation term R(x) approximates ∇x 1 ; see, e.g., [7], [11], [14], [15]. Total variation filtering analogs can be set up in a framework of graph-based signal processing, e.g., by setting R(x) = x T L(g)x, where L(g) is a graph Laplacian matrix guided by a signal g, e.g., [16], that we define next.…”

Section: Notation and Prior Workmentioning

confidence: 99%

Signal reconstruction via operator guiding

Knyazev

Malyshev

2017

2017 International Conference on Sampling Theory and Applications (SampTA)

View full text Add to dashboard Cite

Signal reconstruction from a sample using an orthogonal projector onto a guiding subspace is theoretically well justified, but may be difficult to practically implement. We propose more general guiding operators, which increase signal components in the guiding subspace relative to those in a complementary subspace, e.g., iterative low-pass edge-preserving filters for super-resolution of images. Two examples of super-resolution illustrate our technology: a no-flash RGB photo guided using a high resolution flash RGB photo, and a depth image guided using a high resolution RGB photo.Comment: 5 pages, 8 figures. To appear in Proceedings of SampTA 2017: Sampling Theory and Applications, 12th International Conference, July 3-7, 2017, Tallinn, Estoni

show abstract

“…Recently, variable-splitting techniques have been used in numerous image processing applications (e.g., [13,14]). The objective of variable-splitting approaches is to introduce hidden variables to the original optimization problem, in order to simplify optimization of the functionals (e.g., [13]), or utilize some existing state-of-the-art algorithms (e.g., [15]).…”

Section: Overview Of Variable-splitting Techniquesmentioning

confidence: 99%

Compressive sampling with unknown blurring function: Application to passive millimeter-wave imaging

Amizic

Spinoulas

Molina

et al. 2012

2012 19th IEEE International Conference on Image Processing

View full text Add to dashboard Cite

We propose a novel blind image deconvolution (BID) regularization framework for compressive passive millimeter-wave (PMMW) imaging systems. The proposed framework is based on the variablesplitting optimization technique, which allows us to utilize existing compressive sensing reconstruction algorithms in compressive BID problems. In addition, a non-convex lp quasi-norm with 0 < p < 1 is employed as a regularization term for the image, while a simultaneous auto-regressive (SAR) regularization term is utilized for the blur. Furthermore, the proposed framework is very general and it can be easily adapted to other state-of-the-art BID approaches that utilize different image/blur regularization terms. Experimental results, obtained with simulations using a synthetic image and real PMMW images, show the advantage of the proposed approach compared to existing ones.

show abstract

Superfast superresolution

Cited by 9 publications

References 10 publications

Fast Single Image Super-Resolution Using a New Analytical Solution for <inline-formula> <tex-math notation="LaTeX">$\ell _{2}$ </tex-math> </inline-formula>–<inline-formula> <tex-math notation="LaTeX">$\ell _{2}$ </tex-math> </inline-formula> Problems

Fast Single Image Super-Resolution Using a New Analytical Solution for <inline-formula> <tex-math notation="LaTeX">$\ell _{2}$ </tex-math> </inline-formula>–<inline-formula> <tex-math notation="LaTeX">$\ell _{2}$ </tex-math> </inline-formula> Problems

Signal reconstruction via operator guiding

Compressive sampling with unknown blurring function: Application to passive millimeter-wave imaging

Contact Info

Product

Resources

About