A Survey on Different Image Deblurring Techniques

Vankawala, Fagun; Ganatra, Amit; Patel, Amit

doi:10.5120/20396-2697

Cited by 20 publications

(13 citation statements)

References 7 publications

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“…Moreover, Yun and Park [16] proposed a joint face image SRR and deblurring method based on GANs as well. Apart from the aforementioned studies, a number of studies, such as the survey papers on deblurring [17][18][19][20][21] and SRR [22][23][24] and the review papers on deblurring [25][26][27] and SRR [28,29], have been published to summarize and compare the existing studies. Although there have been numerous studies on deblur-SRR methods, most of them were conducted using images captured by a visible light camera.…”

Section: Related Work a Deblur-srr Methodsmentioning

confidence: 99%

Deep Learning-Based Thermal Image Reconstruction and Object Detection

et al. 2021

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Recently, thermal cameras are being widely used in various fields, such as intelligent surveillance, biometrics, and health monitoring. However, the high cost of the thermal cameras poses a challenge in terms of purchase. Additionally, thermal images have an issue pertaining to blurring caused by object movement, camera movement, and camera focus settings. There have been very few studies on image restoration centered around thermal images to address such problems. Moreover, it is important to increase the processing speed of image restoration methods to jointly conduct with methods such as action recognition and object tracking that use temporal information from thermal videos. However, no study has been conducted on simultaneously performing super-resolution reconstruction and deblurring using thermal images. Furthermore, existing studies on object detection using thermal images have errors owing to the incapability in distinguishing reflections on the surrounding ground or wall due to the heat radiated from the object. To address such issues, this study proposes a deep learning-based thermal image restoration method that simultaneously performs super-resolution reconstruction and deblurring. According to recent development of deep learning, generative adversarial network (GAN)-based methods which have ability to preserve texture details in images, and yield sharper and more plausible textures than classical feed forward encoders show success in image-to-image translation tasks. Considering the advantages of GAN, we propose a deblur-SRRGAN for thermal image reconstruction. In addition, we propose a light-weighted Mask R-CNN for object detection in the reconstructed thermal image. For the input, we employ an image processing method that converts 1-channel thermal images (often used in the existing studies) into 3-channel images. The results of the experiments conducted using self-collected databases and an open database demonstrate that our method outperforms the state-of-the-art methods. INDEX TERMS thermal image; deep learning; super-resolution reconstruction; image deblurring; object and thermal reflection detection.

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Section: Related Work a Deblur-srr Methodsmentioning

confidence: 99%

Deep Learning-Based Thermal Image Reconstruction and Object Detection

et al. 2021

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“…Blind deconvolution uses the blurred image whereas non-blind deconvolution uses the blurred image and known point spread function for the deblurring process. Blind deconvolution is more complicated and more time-consuming than non-blind deconvolution because it estimates the point spread function after each iteration [31].…”

Section: Proposed Framework and Experimental Setupmentioning

confidence: 99%

A Novel Motion Blur Resistant vSLAM Framework for Micro/Nano-UAVs

Şimşek

Bilge

2021

Drones

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Localization and mapping technologies are of great importance for all varieties of Unmanned Aerial Vehicles (UAVs) to perform their operations. In the near future, it is planned to increase the use of micro/nano-size UAVs. Such vehicles are sometimes expendable platforms, and reuse may not be possible. Compact, mounted and low-cost cameras are preferred in these UAVs due to weight, cost and size limitations. Visual simultaneous localization and mapping (vSLAM) methods are used for providing situational awareness of micro/nano-size UAVs. Fast rotational movements that occur during flight with gimbal-free, mounted cameras cause motion blur. Above a certain level of motion blur, tracking losses exist, which causes vSLAM algorithms not to operate effectively. In this study, a novel vSLAM framework is proposed that prevents the occurrence of tracking losses in micro/nano-UAVs due to the motion blur. In the proposed framework, the blur level of the frames obtained from the platform camera is determined and the frames whose focus measure score is below the threshold are restored by specific motion-deblurring methods. The major reasons of tracking losses have been analyzed with experimental studies, and vSLAM algorithms have been made durable by our studied framework. It has been observed that our framework can prevent tracking losses at 5, 10 and 20 fps processing speeds. vSLAM algorithms continue to normal operations at those processing speeds that have not been succeeded before using standard vSLAM algorithms, which can be considered as a superiority of our study.

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“…However, imperfections during the capture will degrade the image and reduce the amount of information available. It is very important to recover the degraded image to restore the information captured, so that it would improve the quality of result and the analysis would be better (Kurniawan and Kusumawardhani 2017;Vankawala, Ganatra, and Patel 2015). There are many applications for image restoration like medical imaging, Richardson algorithm was the best algorithm for restoration of degraded images in all parameters with PSNR-26.31, SSIM -0.79, .…”

Section: Introductionmentioning

confidence: 99%

Image Restoration Using Lucy Richardson Algorithm for Deblurring Images with Improved PSNR, SSIM, NC in Comparison with Wiener Filter

Reddy¹,

Nanmaran²,

Paramasivam³

2021

alinteri

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Aim: Image is the most powerful tool to analyze the information. Sometimes the captured image gets affected with blur and noise in the environment, which degrades the quality of the image. Image restoration is a technique in image processing where the degraded image can be restored or recovered to its nearest original image. Materials and Methods: In this research Lucy-Richardson algorithm is used for restoring blurred and noisy images using MATLAB software. And the proposed work is compared with Wiener filter, and the sample size for each group is 30. Results: The performance was compared based on three parameters, Power Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Normalized Correlation (NC). High values of PSNR, SSIM and NC indicate the better performance of restoration algorithms. Lucy-Richardson provides a mean PSNR of 10.4086db, mean SSIM of 0.4173%, and NC of 0.7433% and Wiener filter provides a mean PSNR of 6.3979db, SSIM of 0.3016%, NC of 0.3276%. Conclusion: Based on the experimental results and statistical analysis using independent sample T test, image restoration using Lucy-Richardson algorithm significantly performs better than Wiener filter on restoring the degraded image with PSNR (P<0.001) and SSIM (P<0.001).

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A Survey on Different Image Deblurring Techniques

Cited by 20 publications

References 7 publications

Deep Learning-Based Thermal Image Reconstruction and Object Detection

Deep Learning-Based Thermal Image Reconstruction and Object Detection

A Novel Motion Blur Resistant vSLAM Framework for Micro/Nano-UAVs

Image Restoration Using Lucy Richardson Algorithm for Deblurring Images with Improved PSNR, SSIM, NC in Comparison with Wiener Filter

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