Academia.eduAcademia.edu

Outline

Title
Abstract
Figures
Introduction
Frequency Domain Methods
Spatial Domain Methods
Summary and Comparison
Conclusion
References

A Survey on Image Reconstruction Using Super Resolution

Profile image of Vijay RajpurohitVijay Rajpurohit
https://doi.org/10.13140/RG.2.1.3926.0007
visibility

…

description

9 pages

link

1 file

Abstract

Imaging plays a key role in many diverse areas of application, such as astronomy, remote sensing, microscopy, and tomography. Owing to imperfections of measuring devices (e.g., optical degradations, limited size of sensors) and instability of the observed scene (e.g., object motion, media turbulence), acquired images can be indistinct, noisy, and may exhibit insufficient spatial and temporal resolution. Super-Resolution (SR) image reconstruction is a promising technique of digital imaging which attempts to reconstruct High Resolution (HR) imagery by fusing the partial information contained within a number of under-sampled low-resolution (LR) images of that scene during the image reconstruction process. Super-resolution image reconstruction involves up-sampling of under-sampled images thereby filtering out distortions such as noise and blur. In comparison to various image enhancement techniques, super-resolution image reconstruction technique not only improves the quality of under-sa...

Figures (5)
From the above three principles, the aliased LR signals can be decomposed into the unaliased HR signal as shown in Figure below ae Let x(¢1,42 ) denote a continuous HR image and X(w1,w2 ) be its CFT. The global translations, which are the only
From the above three principles, the aliased LR signals can be decomposed into the unaliased HR signal as shown in Figure below ae Let x(¢1,42 ) denote a continuous HR image and X(w1,w2 ) be its CFT. The global translations, which are the only
where a is the regularization parameter. Finally, it can be shown that the estimate defined in (6) is equal to a MAP estimate if we use the Gaussian prior in (12).
where a is the regularization parameter. Finally, it can be shown that the estimate defined in (6) is equal to a MAP estimate if we use the Gaussian prior in (12).
3. POCS SR results (a) by bilinear interpolation and by POCS after (b) 10 iterations, (c) 30 iterations, and (d) 50 iterations. GILGE (M7 IU Mer atitiiio, (U7 YU MOeIaGlUito, GHU (U7 UY MOeIativiiio. The validity map allows robust reconstruction in the presence of registration errors, and the segmentation map enables object-based SR reconstruction. In [37] a POCS- based SR reconstruction method where a continuous image formation model is improved to allow for higher order interpolation methods was proposed by Patti and Altunbasak. In this work, they assume a continuous scene within an HR sensor area is not constant. They also modify the constraint set to reduce the ringing artifact in the vicinity of edges. A set theoretic regularization approach similar to POCS formulation was investigated by Tom and Katsaggelos [38]. Using ellipsoidal constraint sets, they find the SR estimate which is the centroid of a bounding ellipsoid (set intersection). The advantage of POCS is that it is simple, and it utilizes the powerful spatial domain observation model. It also allows a convenient inclusion of a priori information. These methods have the disadvantages of nonuniqueness of solution, slow convergence, and a high computational cost. ML-POCS Hybrid Reconstruction Approach The ML-POCS hybrid reconstruction approach finds SR estimates by minimizing the ML (or MAP) cost functional while constraining the solution within certain sets. Earlier efforts for this formulation are found in the work by Schultz and Stevenson [31] where MAP optimization is performed while projections-based constraint is also utilized. Here, the constraint set ensures that the down-sampled version of the HR image matched the reference frame of the LR sequence. Elad and Feuer proposed a general hybrid SR image reconstruction algorithm which combines the benefits of the stochastic approaches and the POCS approach. The simplicity of the ML (or MAP) and the nonellipsoid constraints used in POCS are utilized simultaneously by defining a new convex
3. POCS SR results (a) by bilinear interpolation and by POCS after (b) 10 iterations, (c) 30 iterations, and (d) 50 iterations. GILGE (M7 IU Mer atitiiio, (U7 YU MOeIaGlUito, GHU (U7 UY MOeIativiiio. The validity map allows robust reconstruction in the presence of registration errors, and the segmentation map enables object-based SR reconstruction. In [37] a POCS- based SR reconstruction method where a continuous image formation model is improved to allow for higher order interpolation methods was proposed by Patti and Altunbasak. In this work, they assume a continuous scene within an HR sensor area is not constant. They also modify the constraint set to reduce the ringing artifact in the vicinity of edges. A set theoretic regularization approach similar to POCS formulation was investigated by Tom and Katsaggelos [38]. Using ellipsoidal constraint sets, they find the SR estimate which is the centroid of a bounding ellipsoid (set intersection). The advantage of POCS is that it is simple, and it utilizes the powerful spatial domain observation model. It also allows a convenient inclusion of a priori information. These methods have the disadvantages of nonuniqueness of solution, slow convergence, and a high computational cost. ML-POCS Hybrid Reconstruction Approach The ML-POCS hybrid reconstruction approach finds SR estimates by minimizing the ML (or MAP) cost functional while constraining the solution within certain sets. Earlier efforts for this formulation are found in the work by Schultz and Stevenson [31] where MAP optimization is performed while projections-based constraint is also utilized. Here, the constraint set ensures that the down-sampled version of the HR image matched the reference frame of the LR sequence. Elad and Feuer proposed a general hybrid SR image reconstruction algorithm which combines the benefits of the stochastic approaches and the POCS approach. The simplicity of the ML (or MAP) and the nonellipsoid constraints used in POCS are utilized simultaneously by defining a new convex
A general comparison of frequency and spatial domain SR reconstructions methods is presented in Table 1. Table 1. Frequency vs. spatial domain SR Spatial domain SR_ reconstruction methods, though computationally more expensive, and more complex than their frequency domain counterparts, offer important advantages in terms of flexibility. Two powerful classes of
A general comparison of frequency and spatial domain SR reconstructions methods is presented in Table 1. Table 1. Frequency vs. spatial domain SR Spatial domain SR_ reconstruction methods, though computationally more expensive, and more complex than their frequency domain counterparts, offer important advantages in terms of flexibility. Two powerful classes of
spatial domain methods; the Bayesian (MAP) approach and the set theoretic POCS methods are compared in Table 2. Three research areas promise improved SR methods:
spatial domain methods; the Bayesian (MAP) approach and the set theoretic POCS methods are compared in Table 2. Three research areas promise improved SR methods:

Related papers

A practical approach to super-resolution
Peyman Milanfar

Theoretical and practical limitations usually constrain the achievable resolution of any imaging device. Super-Resolution (SR) methods are developed through the years to go beyond this limit by acquiring and fusing several low-resolution (LR) images of the same scene, producing a high-resolution (HR) image. The early works on SR, although occasionally mathematically optimal for particular models of data and noise, produced poor results when applied to real images. In this paper, we discuss two of the main issues related to designing a practical SR system, namely reconstruction accuracy and computational efficiency. Reconstruction accuracy refers to the problem of designing a robust SR method applicable to images from different imaging systems. We study a general framework for optimal reconstruction of images from grayscale, color, or color filtered (CFA) cameras. The performance of our proposed method is boosted by using powerful priors and is robust to both measurement (e.g. CCD re...

View PDFchevron_right
Spatio-Temporal Super-Resolution Reconstruction of Remote Sensing Data
Igor Yanovsky

2021

There are many problems in existing reconstruction-based super-resolution algorithms, such as the lack of texture-feature representation and of high-frequency details. Multi-scale detail enhancement can produce more texture information and high-frequency information. Therefore, super-resolution reconstruction of remote-sensing images based on adaptive multi-scale detail enhancement (AMDE-SR) is proposed in this paper. First, the information entropy of each remote-sensing image is calculated, and the image with the maximum entropy value is regarded as the reference image. Subsequently, spatio-temporal remote-sensing images are processed using phase normalization, which is to reduce the time phase difference of image data and enhance the complementarity of information. The multi-scale image information is then decomposed using the L 0 gradient minimization model, and the non-redundant information is processed by difference calculation and expanding non-redundant layers and the redundant layer by the iterative back-projection (IBP) technique. The different-scale non-redundant information is adaptive-weighted and fused using cross-entropy. Finally, a nonlinear texture-detail-enhancement function is built to improve the scope of small details, and the peak signal-to-noise ratio (PSNR) is used as an iterative constraint. Ultimately, high-resolution remote-sensing images with abundant texture information are obtained by iterative optimization. Real results show an average gain in entropy of up to 0.42 dB for an up-scaling of 2 and a significant promotion gain in enhancement measure evaluation for an up-scaling of 2. The experimental results show that the performance of the AMED-SR method is better than existing super-resolution reconstruction methods in terms of visual and accuracy improvements.

View PDFchevron_right
Analysis and characterization of super-resolution reconstruction methods (Proceedings Paper)
Filippo Stanco, Giuseppe Messina

2003

View PDFchevron_right
IMAGE RECONSTRUCTION WITH SUPER RESOLUTION
IJRCAR JOURNAL

Subject identification from surveillance imagery has become an important task for forensic investigation. Good quality images of the subjects are essential for the surveillance footage to be useful. However, surveillance videos are of low resolution due to data storage requirements. In addition, subjects typically occupy a small portion of a camera's field of view. Faces, which are of primary interest, occupy an even smaller array of pixels. For reliable face recognition from surveillance video, there is a need to generate higher resolution images of the subject's face from low-resolution video. Super-resolution image reconstruction is a signal processing based approach that aims to reconstruct a high-resolution image by combining a number of low-resolution images. Super-resolution imaging (SR) is a class of techniques that enhance the resolution of an imaging system. Super-resolution image reconstruction is a signal processing based approach that aims to reconstruct a high-resolution image by combining a number of low-resolution images. Super-resolution improves image fidelity, and hence should improve the ability to distinguish between faces and consequently automatic face recognition accuracy.

View PDFchevron_right
Analysis and characterization of super-resolution reconstruction methods
Filippo Stanco

proceedings of SPIE …, 2003

View PDFchevron_right
Multi-frame super-resolution: A survey
Mahmoud Khattab

IEEE, 2018

One of the primary measurements of image quality is image resolution. High-resolution images are often required and desired for most of applications as they embody supplementary information. However, the best utilization of image sensors and optical technologies to increase the image pixel density is usually restrictive and overpriced. Therefore, the effective use of image processing techniques for acquiring a high-resolution image generated from low-resolution images is an inexpensive and powerful solution. This kind of image improvement is named image super-resolution. This paper undertakes to investigate the current super-resolution approaches adopted to generate a highresolution image. Furthermore, it highlights the strengths and the limitations of these approaches. More to the point, several image quality metrics are discussed to measure the similarity between the reconstructed image and the original image.

View PDFchevron_right
Advances and Challenges in Super-Resolution
sudhir bhosale

Super-Resolution reconstruction produces one or a set of high-resolution images from a sequence of low-resolution frames. This article reviews a variety of Super-Resolution methods proposed in the last 20 years, and provides some insight into, and a summary of, our recent contributions to the general Super-Resolution problem. In the process, a detailed study of several very important aspects of Super-Resolution, often ignored in the literature, is presented. Specifically, we discuss robustness, treatment of color, and dynamic operation modes. Novel methods for addressing these issues are accompanied by experimental results on simulated and real data. Finally, some future challenges in Super-Resolution are outlined and discussed.

View PDFchevron_right
A Survey on Super-Resolution Methods for Image Reconstruction
Shahram Javadi

International Journal of Computer Applications, 2014

Today, in many applications of Machine vision, image Super-Resolution is preferred. Super-Resolution is estimation of a high-resolution image from an image or several low resolution images. Popular techniques in the field of enhancing images can be used to remove noise or blurring. In this paper, an overview of super resolution methods has been presented. Types of resolution methods have been used so far can be divided into three groups as frequency-domain methods, spatial domain methods and techniques can be classified as the wavelet domain. Super-resolution methods in different domains have different characteristics and comparison between these methods is usually done using a special index in one domain. In this paper, we will introduce these indexes and review best techniques used in all three domains.

View PDFchevron_right
A Review on Super Resolution Techniques
International Journal of Advance Research in Computer Science and Management Studies [IJARCSMS] ijarcsms.com

In review paper [4], authors Huahua Chen, Baolin Jiang, Weiqiang Chen have demonstrated that a super-resolution based on image patches structure. This method have not only has better quality but less consuming time than Yang [11] method.

View PDFchevron_right
A STUDY ON IMAGE SUPER-RESOLUTION TECHNIQUES
IAEME Publication

Image Super-Resolution (SR) is a technique to reconstruct High-Resolution (HR) images using one or more Low-Resolution (LR) images. This paper brings about a detailed study on image Super-Resolution Techniques. Different categories of image Resolution and the process, Image Super-Resolution are well described. A detailed description of different SR approaches is given and certain relevant SR methods are explained. This paper also gives a qualitative and quantitative performance evaluation and comparison of various SR methods.

View PDFchevron_right

References (44)

  1. S.C. Park, M. K. Park, and M. G. Kang, "Super- resolution image reconstruction: a technical overview," IEEE Signal Processing Magazine, vol. 20, no. 3, pp. 21-36, 2003.
  2. Elad, M.; Feuer, A.; "Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images," Image Processing, IEEE Transactions on , vol.6, no.12, pp.1646-1658, Dec 1997.
  3. Jian Sun; Zongben Xu; Heung-Yeung Shum; , "Image super-resolution using gradient profile prior," Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on , vol., no., pp.1-8, 23-28 June 2008.
  4. J. Yang, J. Wright, T. Huang and Y. Ma "Image Super- Resolution via Sparse Representation" in IEEE Transactions on Image Processing, 2010.
  5. D. Glasner, S. Bagon, and M. Irani. "Super-resolution from a single image," In Proc. of ICCV '09. IEEE Computer Society Press, 2009.
  6. William T. Freeman, Thouis R. Jones, Egon C Pasztor, "Example-Based Super-Resolution," IEEE Computer Graphics and Applications, v.22 n.2, p.56-65, March 2002.
  7. S.Geman, Geman, D.: Stochastic relaxation, gibbs distributions, and the Bayesian restoration of images. IEEE Trans. on Pattern Analysis and Machine Intelligence 6 (1984) 721-741
  8. Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Transactions on PAMI 23 (2001) 1222-1239
  9. M.Irani, Peleg, S.: Improving resolution by image registration. CVGIP:Graphical Models and Image Processing 53 (1991) 231-239
  10. M.Irani, Peleg, S.: Motion analysis for image enhancement: Resolution, occlusion, and transparency, Journal of Visual Communication and Image Representation 4 (1993) 324-335
  11. Borman, S., Stevenson, R.: Linear models for multi- frame super-resolution restoration under non-affine registration and spatially varying PSF. In Bouman, C., Miller, E., eds.: Computational Imaging II. Volume 5299 of Proceedings of the SPIE., San Jose, CA, USA (2004) 234- 245
  12. D.Rajan, Chaudhuri, S.: Simultaneous estimation of super-resolved scene and depth map for low resolution defocused observations. IEEE Trans. on Pattern Analysis and Machine Intelligence 25 (2003) 1102-1117
  13. Joshi, M., Chaudhuri, S.: A learning based method for image super-resolution from zoomed observations. in Proc. fifth Int. Conf. on Advances in Pattern Recognition (2003) 179-182
  14. Chaudhuri, S., Joshi, M.V.: Motion-Free Super- Resolution. Springer (2004)
  15. Schultz, R.R., Stevenson, R.L.: A bayesian approach to image expansion for improved definition. IEEE transctions on Image processing 3 (1994) 233-242
  16. Capel, D., Zisserman, A.: Computer vision applied to super resolution. IEEE Signal Processing Magazine (2003) 75-86
  17. Pickup, L.C., Roberts, S.J., Zisserman, A.: A sampled texture prior for image super-resolution. Advances in Neural Information Processing Systems (2003)
  18. Freeman, W.T., Jones, T.R., Pasztor, E.C.: Example- based super-resolution. IEEE computer Graphics and Applications (2002) 56-65
  19. Baker, S., Kanade, T.: Limits on super-resolution and how to break them. In Proceedings of the 2000 IEEE Conference on Computer Vision and Pattern Recognition (2000)
  20. Lin, Z., Shum, H.Y.: Fundamental limits of reconstruction-based superresolution algorithms under local translation. IEEE Trans. on Pattern Analysis and Machine Intelligence 26 (2004) 83-97
  21. R.Y. Tsai and T.S. Huang, "Multipleframe image restoration and registration," in Advances in Computer Vision and Image Processing. Greenwich, CT: JAI Press Inc., 1984, pp. 317-339.
  22. S.P. Kim, N.K. Bose, and H.M. Valenzuela, "Recursive reconstruction of high resolution image from noisy undersampled multiframes," IEEE Trans. Acoust., Speech, Signal Processing, vol. 38, pp. 1013-1027, June 1990.
  23. S.P. Kim and W.Y. Su, "Recursive high-resolution reconstruction of blurred multiframe images," IEEE Trans. Image Processing, vol. 2, pp. 534-539, Oct. 1993.
  24. N.K. Bose, H.C. Kim, and H.M. Valenzuela, "Recursive implementation of total least squares algorithm for image reconstruction from noisy, [24] undersampled multiframes," in Proc. IEEE Conf. Acoustics, Speech and Signal Processing, Minneapolis, MN, Apr. 1993, vol. 5, pp. 269-272.
  25. A.M. Tekalp, Digital Video Processing. Englewood Cliffs, NJ: Prentice Hall, 1995.
  26. S.H. Rhee and M.G. Kang, "Discrete cosine transform based regularized high-resolution image reconstruction algorithm," Opt. Eng., vol. 38, no. 8, pp. 1348-1356, Aug. 1999.
  27. A.N.Rajagopalan, Subhasis Chaudhuri and Uma Mudenagudi Depth estimation and image restoration using defocused stereo pairs IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 26 Nov 2004,
  28. M.C. Hong, M.G. Kang, and A.K. Katsaggelos, "A regularized multichannel restoration approach for globally optimal high resolution video sequence," in SPIE VCIP, vol. 3024, San Jose, CA, Feb. 1997, pp. 1306-1317.
  29. M.C. Hong, M.G. Kang, and A.K. Katsaggelos, "An iterative weighted regularized algorithm for improving the resolution of video sequences," in Proc. Int. Conf. Image Processing, vol. 2, 1997, pp. 474-477.
  30. C. A. Segall, R. Molina, A. K. Katsaggelos, and J. Mateos. Bayesian High-Resolution Reconstruction of Low- Resolution Compressed Video. Proceedings of the IEEE Internationa Conference on Image Processing, pages 25-28, October 2001.
  31. R.R. Schulz and R.L. Stevenson, "Extraction of high- resolution frames from video sequences," IEEE Trans. Image Processing, vol. 5, pp. 996-1011, June 1996.
  32. H. Stark and P. Oskoui, "High resolution image recovery from image-plane arrays, using convex projections," J. Opt. Soc. Am. A, vol. 6, pp. 1715-1726, 1989.
  33. A.M. Tekalp, M.K. Ozkan, and M.I. Sezan, "High- resolution image reconstruction from lower-resolution image sequences and space varying image restoration," in Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), San Francisco, CA., vol. 3, Mar. 1992, pp. 169- 172.
  34. H. Stark and P. Oskoui, "High resolution image recovery from image-plane arrays, using convex projections," J. Opt. Soc. Am. A, vol. 6, pp. 1715-1726, 1989.
  35. A.J. Patti, M.I. Sezan, and A.M. Tekalp, "Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time," IEEE Trans. Image Processing, vol. 6, no. 8, pp. 1064-1076, Aug. 1997.
  36. P.E. Eren, M.I. Sezan, and A.M. Tekalp, "Robust, object-based high-resolution image reconstruction from low- resolution video," IEEE Trans. Image Processing, vol. 6, no. 10, pp. 1446-1451, Oct. 1997.
  37. A.J. Patti and Y. Altunbasak, "Artifact reduction for set theoretic super resolution image reconstruction with edge adaptive constraints and higher-order interpolants," IEEE Trans. Image Processing, vol. 10, no. 1, pp. 179-186, Jan. 2001.
  38. B.C. Tom and A.K. Katsaggelos "An iterative algorithm for improving the resolution of video sequences," in Proc. 1996 SPIE Conf. Visual Communications and Image Processing, Orlando, FL, Mar. 1996, pp. 1430-1438.
  39. D. Rajan and S. Chaudhuri, "Generalized interpolation and its applications in super-resolution imaging," Image and Vision Computing, vol. 19, pp. 957-969, Nov. 2001.
  40. C. V. Jiji, M. V. Joshi, and S. Chaudhuri. Single frame Image Super Resolution through contourlet learning. EURASIP Journal on Applied Signal Processing, 2006:Article ID 73767, 11 pages, 2006.
  41. D. Rajan and S. Chaudhuri, "Generalized interpolation and its applications in super-resolution imaging," Image and Vision Computing, vol. 19, pp. 957-969, Nov. 2001.
  42. C. V. Jiji, M. V. Joshi, and S. Chaudhuri. Single frame Image Super Resolution using Learnt wavelet Coefficients. International Journal of Imaging Science and Technology, 14(3):105.112, September 2004.
  43. N. R. Shah and A. Zakhor. Multiframe spatial resolution enhancement of color video. In ICIP, volume I, pages 985-988, Lausanne, Switzerland, Sept. 1996.
  44. R. R. Schultz and R. L. Stevenson. Extraction of high resolution frames from video sequences. IEEE Trans. IP, 5(6):996-1011, June 1996.

Related papers

A Survey : The Methods & Techniques of Super-Resolution Image Reconstruction
Ashish Semwal

2017

Super-resolution is the process of recovering a high-resolution image from multiple low-resolution images of the same scene. The key objective of super-resolution (SR) imaging is to reconstruct a higher-resolution image based on a set of images, acquired from the same scene and denoted as ‘low-resolution’ images, to overcome the limitation and/or ill-posed conditions of the image acquisition process for facilitating better content visualization and scene recognition. In this paper, we provide a comprehensive review of existing super-resolution techniques and highlight the future research challenges. This includes the formulation of an observation model and coverage of the dominant algorithm – Iterative back projection .We critique these methods and identify areas which promise performance improvements. In this paper, future directions for super-resolution algorithms are discussed. Finally results of available methods are given.

View PDFchevron_right
A Survey on Image Super- Resolution Techniques for Image Reconstruction
Josephin Nivetha

2016

Image reconstruction techniques are used to create a two dimensional and three dimensional images. For image reconstruction different methods are used such as back projection filters. Image reconstruction is commonly referd to as restoration of missing parts. Super-resolution technique aims to increase the resolution of the limits of the original image or video. It is used to extract the lost details of an image when it was up scaled. Interpolation based SR, Example based SR and Multi image based SR are the main techniques for reconstructing a super resolution image from LR image. Resolution refers to denote the number of pixels in an image and it is measured in pixel Per Inch (PPI). SR technique reduces the image’s blurring and used in many image processing applications. SR technique applied in an improvement of test images, compressed video and image enhancement, medical imaging process and satellite and aerial imaging. SR is a badly postured issue on the grounds that every LR pix...

View PDFchevron_right
A Survey on Techniques and Challenges in Image Super Resolution Reconstruction
IJCSMC Journal
View PDFchevron_right
A Study on Super-Resolution Image Reconstruction Techniques
Kathiravan S

With the rapid development of space technology and its related technologies, more and more remote sensing platforms are sent to outer space to survey our earth. Recognizing and positioning all these space objects is the basis of knowing about the space, but there are no other effective methods in space target recognition except orbit and radio signal recognition. Super-resolution image reconstruction, which is based on the image of space objects, provides an effective way of solving this problem. In this paper, the principle of super-resolution image reconstruction and several typical reconstruction methods were introduced. By comparison, Nonparametric Finite Support Restoration Techniques were analyzed in details. At last, several aspects of super-resolution image reconstruction that should be studied further more were put forward.

View PDFchevron_right
Super-resolution image reconstruction: a technical overview
Semsem Ali

IEEE Signal Processing Magazine, 2003

Stochastic regularized methods are quite advantageous in super-resolution (SR) image reconstruction problems. In the particular techniques, the SR problem is formulated by means of two terms, the datafidelity term and the regularization term. The present work examines the effect of each one of these terms on the SR reconstruction result with respect to the presence or absence of noise in the low-resolution (LR) frames. Experimentation is carried out with the widely employed L 2 , L 1 , Huber and Lorentzian estimators for the data-fidelity term. The Tikhonov and Bilateral (B) Total Variation (TV) techniques are employed for the regularization term. The extracted conclusions can, in practice, help to select an effective SR method for a given sequence of LR frames. Thus, in case that the potential methods present common data-fidelity or regularization term, and frames are noiseless, the method which employs the most robust regularization or data-fidelity term should be used. Otherwise, experimental conclusions regarding performance ranking vary with the presence of noise in frames, the noise model as well as the difference in robustness of efficiency between the rival terms. Estimators employed for the data-fidelity term or regularizations stand for the rival terms.

View PDFchevron_right
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved