Skip to content
2000
Volume 18, Issue 3
  • ISSN: 2352-0965
  • E-ISSN: 2352-0973

Abstract

A review and analysis of digital image restoration are provided in this work. The goal of image restoration is to enhance the quality of an image by understanding the physical process that created it. The purpose of picture restoration is to cover up or correct flaws that lower an image's quality. Motion blur, noise, and difficulty focusing the camera are just a few examples of how degradation can manifest itself. When there is motion blur, for example, it is possible to “undo” the blurring function and return the image to its previous state. The best course of action when noise distorts an image is to fix the damage it causes. In contrast to image enhancement, which focuses more on highlighting or extracting picture features than on restoring degradations, image restoration restores degraded images. While the mathematical representation of enhancement criteria is challenging, image restoration difficulties may be properly quantified. Restoration of images began in the 1950s. Application areas for image restoration include consumer photography, legal investigations, filmmaking and rivalries, image and video decoding, and scientific research. Image reconstruction in radio astronomy, radar imaging, and tomography is the principal area of use. This study proposal explores various image restoration methods and discusses the value of image restoration techniques.

Loading

Article metrics loading...

/content/journals/raeeng/10.2174/0123520965275894231130114411
2024-01-25
2025-05-29
Loading full text...

Full text loading...

References

  1. RaniS. JindalS. KaurB. A brief review on image restoration techniques.Int. J. Comput. Appl.201615012303310.5120/ijca2016911623
    [Google Scholar]
  2. WangW. YuanX. Recent advances in image dehazingIEEE/CAA J. Autom. Sin.201743410436
    [Google Scholar]
  3. SaxenaG. BhadauriaS.S. An efficient single image haze removal algorithm for computer vision applications.Multimedia Tools Appl.20207937-38282392826310.1007/s11042‑020‑09421‑4
    [Google Scholar]
  4. BanhamM.R. KatsaggelosA.K. Digital image restoration.IEEE Signal Process. Mag.1997142244110.1109/79.581363
    [Google Scholar]
  5. JainP. TyagiV. Spatial and frequency domain filters for restoration of noisy images.IETE J. Educ.201354210811610.1080/09747338.2013.10876113
    [Google Scholar]
  6. KongL. DongJ. GeJ. LiM. PanJ. Efficient frequency domain-based transformers for high-quality image deblurringConference on Computer Vision and Pattern Recognition (CVPR)202310.1109/CVPR52729.2023.00570
    [Google Scholar]
  7. VtH. YsP. YbP. Study of image restoration techniques for remote sensing images in agriculture field.Int. J. Mach. Intell.20113313814110.9735/0975‑2927.3.3.138‑141
    [Google Scholar]
  8. SunP. HouM. LyuS. WangW. LiS. MaoJ. LiS. Enhancement and restoration of scratched murals based on hyperspectral imaging-A case study of murals in the Baoguang Hall of Qutan Temple, Qinghai, China.Sensors20222224978010.3390/s22249780 36560152
    [Google Scholar]
  9. ZhuQ. MaiJ. ShaoL. Single image dehazing using color attenuation priorProceedings of the British Machine Vision Conference201410.5244/C.28.114
    [Google Scholar]
  10. NgoD. LeeG.D. KangB. Improved color attenuation prior for single-image haze removal.Appl. Sci.2019919401110.3390/app9194011
    [Google Scholar]
  11. KansalI. KasanaS.S. Improved color attenuation prior based image de-fogging technique.Multimedia Tools Appl.20207917-18120691209110.1007/s11042‑019‑08240‑6
    [Google Scholar]
  12. XieS. ZhengX. ChenY. XieL. LiuJ. ZhangY. YanJ. ZhuH. HuY. Artifact removal using Improved GoogLeNet for sparse-view CT reconstruction.Sci. Rep.201881670010.1038/s41598‑018‑25153‑w 29712978
    [Google Scholar]
  13. FesslerJ. Model-based image reconstruction for MRI.IEEE Signal Process. Mag.2010274818910.1109/MSP.2010.936726 21135916
    [Google Scholar]
  14. Al-AmeenZ. SulongG. GaparM.D. JoharM.D. Reducing the Gaussian blur artifact from CT medical images by employing a combination of sharpening filters and iterative deblurring algorithms.J. Theor. Appl. Inf. Technol.20124613136
    [Google Scholar]
  15. JiangD. ZhuangD. HuangY. FuJ. Survey of multispectral image fusion techniques in remote sensing applications. Image Fusion and Its Applications.InTech2011
    [Google Scholar]
  16. SabuA. VishwanathN. An improved visibility restoration of single haze images for security surveillance systemsOnline International Conference on Green Engineering and Technologies (IC-GET)Coimbatore, India, 19-19 Nov201610.1109/GET.2016.7916635
    [Google Scholar]
  17. GoilkarS.S. YadavD.M. Defocused image restoration using Wiener and inverse filter in context of security applicationAIP Conf. Proc.20222576103001310.1063/5.0106020
    [Google Scholar]
  18. WanY. WeiW. ZhengQ. Study on the aerial image degradation caused by satellite swing and its correction5th International Conference on Computer Science and Network Technology (ICCSNT)Changchun, China, 10-11 Dec201610.1109/ICCSNT.2016.8070268
    [Google Scholar]
  19. DaubechiesI. TeschkeG. Variational image restoration by means of wavelets: Simultaneous decomposition, deblurring, and denoising.Appl. Comput. Harmon. Anal.200519111610.1016/j.acha.2004.12.004
    [Google Scholar]
  20. MarnissiY. ZhengY. ChouzenouxE. PesquetJ.C. A variational bayesian approach for image restoration—application to image deblurring with Poisson–Gaussian noise.IEEE Trans. Comput. Imaging20173472273710.1109/TCI.2017.2700203
    [Google Scholar]
  21. QureshiM.A. DericheM. BeghdadiA. AminA. A critical survey of state-of-the-art image inpainting quality assessment metrics.J. Vis. Commun. Image Represent.20174917719110.1016/j.jvcir.2017.09.006
    [Google Scholar]
  22. ParkE. SimJ.Y. Underwater image restoration using geodesic color distance and complete image formation model.IEEE Access2020815791815793010.1109/ACCESS.2020.3019767
    [Google Scholar]
  23. SonogashiraM. FunatomiT. IiyamaM. MinohM. A variational Bayesian approach to multiframe image restoration.IEEE Trans. Image Process.20172652163217810.1109/TIP.2017.2678171 28287969
    [Google Scholar]
  24. ZhaoS. ZhangL. ShenY. ZhouY. RefineDNet: A weakly supervised refinement framework for single image dehazing.IEEE Trans. Image Proc.2021303391340410.1109/TIP.2021.3060873 33651690
    [Google Scholar]
  25. KansalI. KasanaS.S. Minimum preserving subsampling-based fast image de-fogging.J. Mod. Opt.201865182103212310.1080/09500340.2018.1499976
    [Google Scholar]
  26. GernsheimH. The 150th anniversary of photography.Hist. Photogr.1977113810.1080/03087298.1977.10442876
    [Google Scholar]
  27. TroxellK.B. RestorationVs. Troxell, Restoration Vs. Preservation Museum Views(Doctoral dissertation).1860
    [Google Scholar]
  28. MasaniP.R. Image restoration by the method of least squares.Josa196753297303
    [Google Scholar]
  29. HelstromC.W. Image restoration by the method of least squares.J. Opt. Soc. Am.196757329710.1364/JOSA.57.000297
    [Google Scholar]
  30. GilesJ.W. Image reconstruction from a Fraunhofer X-ray hologram with visible light.J. Opt. Soc. Am.1969599117910.1364/JOSA.59.001179
    [Google Scholar]
  31. RinoC.L. Bandlimited image restoration by linear mean-square estimation.J. Opt. Soc. Am.196959554710.1364/JOSA.59.000547
    [Google Scholar]
  32. MacAdamD.P. Digital image restoration by constrained deconvolution.J. Opt. Soc. Am.19706012161710.1364/JOSA.60.001617
    [Google Scholar]
  33. KatsaggelosA. Iterative image restoration algorithms. Digital Signal Processing Fundamentals.CRC Press200912010.1201/9781420046076‑c34
    [Google Scholar]
  34. SchechnerY.Y. NarasimhanS.G. NayarS.K. Instant dehazing of images using polarizationIEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001Kauai, HI, USA, 08-14 Dec2005
    [Google Scholar]
  35. KondoK. IchiokaY. SuzukiT. Image restoration by Wiener filtering in the presence of signal-dependent noise.Appl. Opt.19771692554255810.1364/AO.16.002554 20168967
    [Google Scholar]
  36. StarkH. OskouiP. High-resolution image recovery from image-plane arrays, using convex projections.J. Opt. Soc. Am. A Opt. Image Sci. Vis.19896111715172610.1364/JOSAA.6.001715 2585170
    [Google Scholar]
  37. BesagJ. YorkJ. MolliA. Bayesian image restoration, with two applications in spatial statistics.Ann. Inst. Stat. Math.199143112010.1007/BF00116466
    [Google Scholar]
  38. JeffsB.D. JeffsB.D. Adaptive image restoration using a generalized Gaussian model for unknown noise.IEEE Trans. Image Process.19954101451145610.1109/83.465110 18291976
    [Google Scholar]
  39. KundurD. HatzinakosD. Blind image deconvolution revisited.IEEE Signal Process. Mag.1996136616310.1109/79.543976
    [Google Scholar]
  40. HeK. SunJ. TangX. Guided image filtering.IEEE Trans. Pattern Anal. Mach. Intell.20133561397140910.1109/TPAMI.2012.213 23599054
    [Google Scholar]
  41. DurairajD.C. KrishnaM.C. MurugesanR. A neural network approach for image reconstruction in electron magnetic resonance tomography.Comput. Biol. Med.200737101492150110.1016/j.compbiomed.2007.01.010 17362904
    [Google Scholar]
  42. KwakD.K. RyuJ.K. A study on the dynamic image-based dark channel prior and smoke detection using deep learning.J. Electr. Eng. Technol.202217158158910.1007/s42835‑021‑00880‑9
    [Google Scholar]
  43. GaldranA. Image dehazing by artificial multiple-exposure image fusion.Signal Proc.201814913514710.1016/j.sigpro.2018.03.008
    [Google Scholar]
  44. SethiR. InduS. Fusion of underwater image enhancement and restoration.Int. J. Pattern Recognit. Artif. Intell.2020343205400710.1142/S0218001420540075
    [Google Scholar]
  45. HouG. ZhaoX. PanZ. YangH. TanL. LiJ. Benchmarking underwater image enhancement and restoration, and beyond.IEEE Access2020812207812209110.1109/ACCESS.2020.3006359
    [Google Scholar]
  46. FeiX. MiaoJ. ZhaoY. HuangW. YuR. Total variation regularized low-rank model with directional information for hyperspectral image restoration.IEEE Access20219841568416910.1109/ACCESS.2021.3087916
    [Google Scholar]
  47. LiY. Lsdir: A large scale dataset for image restorationProceedings of the IEEE/CVF Conference on Computer Vision and Pattern RecognitionVancouver, BC, Canada, 17-24 June, 202, 17751787.10.1109/CVPRW59228.2023.00178
    [Google Scholar]
  48. GuiJ. CongX. CaoY. RenW. ZhangJ. ZhangJ. CaoJ. TaoD. A comprehensive survey and taxonomy on single image dehazing based on deep learning.ACM Comput. Surv.20235513s13710.1145/3576918
    [Google Scholar]
  49. JaisuryaR.S. MukherjeeS. Attention-based single image dehazing using improved cycleGANInternational Joint Conference on Neural Networks (IJCNN)Padua, Italy, 18-23 July202218.10.1109/IJCNN55064.2022.9892628
    [Google Scholar]
  50. HeX. JiW. XieJ. Unsupervised haze removal for aerial imagery based on asymmetric contrastive CycleGAN.IEEE Access202210673166732810.1109/ACCESS.2022.3186004
    [Google Scholar]
  51. HuA. XieZ. XuY. XieM. WuL. QiuQ. Unsupervised haze removal for high-resolution optical remote-sensing images based on improved generative adversarial networks.Remote Sens.20201224416210.3390/rs12244162
    [Google Scholar]
  52. ZhaoK. ZhouL. GaoS. WangX. WangY. ZhaoX. WangH. LiuK. ZhuY. YeH. Study of low-dose PET image recovery using supervised learning with CycleGAN.PLoS One2020159e023845510.1371/journal.pone.0238455 32886683
    [Google Scholar]
  53. HuangM. MuZ. ZengH. HuangH. A novel approach for interest point detection via Laplacian-of-Bilateral filter.J. Sens.201520151910.1155/2015/685154
    [Google Scholar]
  54. MalikS. SoundararajanR. A low light natural image statistical model for joint contrast enhancement and denoising.Signal Process. Image Commun.20219911643311643310.1016/j.image.2021.116433
    [Google Scholar]
  55. ChauhanV. Reduction of noise in restoration of images using mean and median filtering techniques.Int. J. Res. Appl. Sci. Eng. Technol.20219930131310.22214/ijraset.2021.37965
    [Google Scholar]
  56. ChanR.H. ChenK. A multilevel algorithm for simultaneously denoising and deblurring images.SIAM J. Sci. Comput.20103221043106310.1137/080741410
    [Google Scholar]
  57. MourabitI.E. RhabiM.E. HakimA. LaghribA. MoreauE. A new denoising model for multi-frame super-resolution image reconstruction.Signal Proc.2017132516510.1016/j.sigpro.2016.09.014
    [Google Scholar]
  58. WangY. XuZ. YangY. WangX. HeJ. RenT. LiuJ. Deblurring microscopic image by integrated convolutional neural network.Precis. Eng.202382445110.1016/j.precisioneng.2023.03.005
    [Google Scholar]
  59. YangC. FengH. XuZ. ChenY. LiQ. Image deblurring utilizing inertial sensors and a short-long-short exposure strategy.IEEE Trans. Image Process.2020294614462610.1109/TIP.2020.2973499 32086209
    [Google Scholar]
  60. LiangC.-H. ChenY.-A. LiuY.-C. HsuW.H. Raw Image DeblurringarXiv20202020
    [Google Scholar]
  61. HuihuiY. DaoliangL. YingyiC. A state-of-the-art review of image motion deblurring techniques in precision agriculture.Heliyon202396e1733210.1016/j.heliyon.2023.e17332 37416671
    [Google Scholar]
  62. YangX. WangX. WangN. GaoX. SRDN: A unified super-resolution and motion deblurring network for space image restoration.IEEE Trans. Geosci. Remote Sens.20226011110.1109/TGRS.2021.3131264
    [Google Scholar]
  63. SwethaP. Dehazing using fast guided image filtering.IJITEE2019811S14
    [Google Scholar]
  64. WangB. WangY. SuiX. LiuY. ChenQ. Gradient domain weighted guided image filtering.Signal Image Video Process.202319
    [Google Scholar]
  65. WangQ. CaiC. ZhangW. LiP. XinB. Adaptive color correction and detail restoration for underwater image enhancement.Appl. Opt.2022616C46C5410.1364/AO.433558 35200997
    [Google Scholar]
  66. NingY. JinY. PengY. YanJ. Underwater image restoration based on adaptive parameter optimization of the physical model.Opt. Express20233113211722119110.1364/OE.492293 37381223
    [Google Scholar]
  67. ReevesS.J. Fast image restoration without boundary artifacts.IEEE Trans. Image Process.200514101448145310.1109/TIP.2005.854474 16238051
    [Google Scholar]
  68. SongQ. XiongR. FanX. LiuD. WuF. HuangT. GaoW. Compressed image restoration via artifacts-free PCA basis learning and adaptive sparse modeling.IEEE Trans. Image Process.2020297399741310.1109/TIP.2020.3002452
    [Google Scholar]
  69. YouJaehee BovikA.C. BovikA.C. Referenceless prediction of perceptual fog density and perceptual image defogging.IEEE Trans. Image Process.201524113888390110.1109/TIP.2015.2456502 26186784
    [Google Scholar]
  70. SabirA. KhurshidK. SalmanA. Segmentation-based image defogging using modified dark channel prior.EURASIP J. Image Video Process.202020201610.1186/s13640‑020‑0493‑9
    [Google Scholar]
  71. KansalI. KasanaS.S. Weighted image de-fogging using luminance dark prior.J. Mod. Opt.201764192023203410.1080/09500340.2017.1333641
    [Google Scholar]
  72. SharmaN. KumarV. SinglaS.K. Single image defogging using deep learning techniques: Past, present and future.Arch. Comput. Methods Eng.20212874449446910.1007/s11831‑021‑09541‑6
    [Google Scholar]
  73. AnanS. KhanM.I. KowsarM.M.S. DebK. DharP.K. KoshibaT. Image defogging framework using segmentation and the dark channel prior.Entropy202123328510.3390/e23030285 33652822
    [Google Scholar]
  74. KumariA. SahooS.K. ChinnaiahM.C. Fast and efficient visibility restoration technique for single image dehazing and defogging.IEEE Access20219481314814610.1109/ACCESS.2021.3068446
    [Google Scholar]
  75. ParkS. MoonB. KoS. YuS. PaikJ. Low-light image restoration using bright channel prior-based variational Retinex model.EURASIP J. Image Video Process.2017201714410.1186/s13640‑017‑0192‑3
    [Google Scholar]
  76. LuoW. DuanS. ZhengJ. Underwater image restoration and enhancement based on a fusion algorithm with color balance, contrast optimization, and histogram stretching.IEEE Access20219317923180410.1109/ACCESS.2021.3060947
    [Google Scholar]
  77. QiuY. LuY. WangY. JiangH. IDOD-YOLOV7: Image-dehazing YOLOV7 for object detection in low-light foggy traffic environments.Sensors2023233134710.3390/s23031347 36772388
    [Google Scholar]
  78. YanX. WangG. LinP. ZhangJ. WangY. FuX. Underwater image dehazing using a novel color channel based dual transmission map estimation.Multimedia Tools Appl.202312410.1007/s11042‑023‑15708‑z
    [Google Scholar]
  79. YanB. YangZ. SunH. WangC. ADE-CycleGAN: A detail enhanced image dehazing CycleGAN network.Sensors2023236329410.3390/s23063294 36992005
    [Google Scholar]
  80. ZhangX. LiX. TangZ. ZhangS. XieS. Noise removal in embedded image with bit approximation.IEEE Trans. Knowl. Data Eng.20223431359136910.1109/TKDE.2020.2992572
    [Google Scholar]
  81. WangS. HuangB. Ho WongT. HuangJ. DengH. CLGA Net: Cross layer gated attention network for image dehazing.Comput. Mater. Continua20237434667468410.32604/cmc.2023.031444
    [Google Scholar]
  82. GuiB. ZhuY. ZhenT. Adaptive single image dehazing method based on support vector machine.J. Visual Commun. Image Represent.20207010279210.1016/j.jvcir.2020.102792
    [Google Scholar]
  83. HuangK.Q. WangQ. WuZ.Y. Natural color image enhancement and evaluation algorithm based on human visual system.Comput. Vis. Image Underst.20061031526310.1016/j.cviu.2006.02.007
    [Google Scholar]
  84. ZhuH. PengX. ZhouJ.T. YangS. ChanderasekhV. LiL. LimJ-H. Singe image rain removal with unpaired information: A differentiable programming perspective.Proc. Conf. AAAI Artif. Intell.20193319332933910.1609/aaai.v33i01.33019332
    [Google Scholar]
  85. ZhouJ. ZhangD. ZhangW. Classical and state-of-the-art approaches for underwater image defogging: A comprehensive survey.Front. Inform. Technol. Elec. Eng.202021121745176910.1631/FITEE.2000190
    [Google Scholar]
  86. LeeY. ParkS. RheeE. KimB-G. JunD. Reduction of compression artifacts using a densely cascading image restoration network.Appl. Sci.20211117780310.3390/app11177803
    [Google Scholar]
  87. JieH. LiS. GangS. Squeeze-and-excitation networksProceedings of the IEEE Conference on Computer Vision and Pattern RecognitionSalt Lake City, UT, USA2018
    [Google Scholar]
  88. WooS. ParkJ. LeeJ.Y. Cbam: Convolutional block attention moduleProceedings of the European Conference on Computer Vision (ECCV)Munich, Germany2018319.
    [Google Scholar]
  89. ShawP. UszkoreitJ. VaswaniA. Self-attention with relative position representationsarXiv20182018207410.18653/v1/N18‑2074
    [Google Scholar]
  90. ZhangH. GoodfellowI. MetaxasD. Self-attention generative adversarial networksProceedings of the International Conference on Machine Learning201973547363
    [Google Scholar]
  91. ZhaoH. JiaJ. KoltunV. Exploring self-attention for image recognitionProceedings of the IEEE/CVF Conference on Computer Vision and Pattern RecognitionSeattle, WA, USA20201007610085.
    [Google Scholar]
  92. HouQ. ZhouD. FengJ. Coordinate attention for efficient mobile network designProceedings of the IEEE/CVF Conference on Computer Vision and Pattern RecognitionSeattle, WA, USA2020371313722.
    [Google Scholar]
  93. EnginD. GenA. EkenelH.K. Cycle-Dehaze: Enhanced cyclegan for single image dehazingProceedings of the IEEE Conference on Computer Vision and Pattern Recognition WorkshopsSalt Lake City, UT, USA2018825833.10.1109/CVPRW.2018.00127
    [Google Scholar]
  94. HowardA.G. ZhuM. ChenB. MobileNets: Efficient convolutional neural networks for mobile vision applicationsarXiv2017201704861
    [Google Scholar]
  95. MercatJ. GillesT. El ZoghbyN. Multi-head attention for multi-modal joint vehicle motion forecastingProceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA)Paris, France; Piscataway, NJ, USA202096389644.10.1109/ICRA40945.2020.9197340
    [Google Scholar]
  96. WangR. ZhangQ. FuC.W. Underexposed photo enhancement using deep illumination estimationProceedings of the IEEE/CVF Conference on Computer Vision and Pattern RecognitionLong Beach, CA, USA, 209, 68496857.10.1109/CVPR.2019.00701
    [Google Scholar]
  97. AncutiC.O. AncutiC. SbertM. TimofteR. Dense-haze: A benchmark for image dehazing with dense-haze and haze-free images2019 IEEE international conference on image processing (ICIP)Taipei, Taiwan, 22-25 Sep201910141018
    [Google Scholar]
  98. ZhaiL. WangY. CuiS. ZhouY. A comprehensive review of deep learning-based real-world image restoration.IEEE Access202311210492106710.1109/ACCESS.2023.3250616
    [Google Scholar]
  99. QiuJ. XieK. A GAN-based motion blurred image restoration algorithmInternational conference on software engineering and service science (ICSESS)Beijing, China, 18-20 Oct2019211215.10.1109/ICSESS47205.2019.9040717
    [Google Scholar]
  100. CaiB. XuX. JiaK. QingC. TaoD. Dehazenet: An end-to-end system for single image haze removal.IEEE Trans. Image Process.201625115187519810.1109/TIP.2016.2598681 28873058
    [Google Scholar]
  101. ZhangC. DuF. ZhangY. A brief review of image restoration techniques based on generative adversarial modelsInternational Conference on Multimedia and Ubiquitous EngineeringSingapore2019
    [Google Scholar]
  102. KoundalD. GuptaS. SinghS. Computer aided thyroid nodule detection system using medical ultrasound images.Biomed. Signal Process. Control20184011713010.1016/j.bspc.2017.08.025
    [Google Scholar]
  103. ShuklaP.K. SandhuJ.K. AhirwarA. GhaiD. MaheshwaryP. ShuklaP.K. Multiobjective genetic algorithm and convolutional neural network based COVID-19 identification in chest X-ray images.Math. Probl. Eng.202120211910.1155/2021/7804540
    [Google Scholar]
/content/journals/raeeng/10.2174/0123520965275894231130114411
Loading
/content/journals/raeeng/10.2174/0123520965275894231130114411
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test