Abstract
Due to improper camera settings and atmospheric turbulence, captured scene images are usually degraded by blurring. Image restoration is the process of extracting a blurred image's original image. Image restoration is an articulate technique that facilitates the retrieval of original images in a one-step manner. First blind deconvolution is among the most significant restoration methods in which the blurring operator is not identified. Blind system deconvolution is a very complicated process that restores objects without knowing what they were like before they started to deteriorate PSF. The PSF is an impulse response of a point source. This paper introduces a novel approach of blind deconvolution for blurred color images. Here, first perform the degradation process, then use the Wiener filter to reduce the noise from the input blurred image using the degradation function. Then the restoration process is performed using blind deconvolution, involving the estimation of PSF kernels through a thresholding mechanism. Following this, deblurring can be carried out for different sizes of PSF images. The deblurred images are restored with some ringing effects. In conclusion, the Canny edge detector is utilized to reduce noise and achieve the optimal restored image. The cost of MSE and PSNR was calculated for each method and compared in the results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Khan A, Ali I, Waleed M (2020) An online platform based on google collaboration for quick estimation of real blur in blind deblurring of a single image. In: Electronics, computers, and AI 2020: 12th international conference
Shajkofci A, Liebling L (2020) Blind deconvolution and depth estimation using spatially variable CNN-based point spread function estimation in optical microscopy. IEEE Trans Image Process 29:5849–5862
Ning M, Zhang S. Wang S (2018) Image restoration of motion blurred stars using mems gyroscope aid and blur kernel correction. 18(8):2663
Tabib RA, Mudenagudi U, Patil U, Dhanakshirur RR (2019) Learning-based PSF estimation for motion deblurring: evidence-based feature selection and collaborative representation. In: International conference on computer vision by the IEEE
Khongkraphan K, Phonon A, Yusoh M (2018) Using the gradient descent method to estimate motion blur parameters. In: 22nd International conference on computer science and engineering. Tao X et al (2018) Deep Image deblurring using a scalable recurrent network. In: 2018 IEEE Conference on pattern recognition and computer vision
Xiang S, Peng S, Yin W, Liu RW (2018) L0 regularized priors based on hybrid gradient sparsity for robust single image blind deblurring. In: IEEE conference proceedings on acoustics, speech signal processing, Seoul, Korea, April 2018, pp 1349–1353
Pfister H, Yang MH, Sun D, Pan J (2018) Image blurring using a dark channel before. IEEE Trans Pattern Recogn Mach Learn 40:2316–2329
Ren W, Cao X, Guo Y, Yan Y, Wang R (2017) Prior to deblurring images using extreme channels. In: The IEEE conference on computer vision and pattern recognition, Honolulu, July 2017, pp 6976–6984
Yang M, Su Z, Pan J, Hu Z (2014) Text image deblurring using L0 regularised gradients and intensities. In: The IEEE Conference on pattern recognition and computer vision proceedings, Columbus, Sept 2014, pp 2902–2909
Lee S, Wang J, Cho H (2012) Deblurring of text images utilizing text-specific characteristics. J Eur Comput Vis Conf 525–538
Tang X, Sun J, He K (2011) Using the dark channel to remove haze from a single image. J Pattern Recogn Mach Learn 33:2342–2354
Durand F, Levin A, Freeman W, Weiss Y (2009) Understanding and evaluating blind deconvolution techniques. In: IEEE Conference on computer vision and pattern recognition, Miami, Florida, pp 1965–1972, Mar 2009
Jia J, Shan Q, Agarwala A (2008) High-quality motion blurs removal with only one image. ACM Trans Graph 27:1–11
Hertzmann A, Fergus R, Freeman WT, Roweis ST, Singh B (2006) Removing camera shake from a single shot. ACM’s Trans Graph 25:788–795
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Narasimharao, J., Deepthi, P., Aditya, B., Reddy, C.R.S., Reddy, A.R., Joshi, G. (2024). Advanced Techniques for Color Image Blind Deconvolution to Restore Blurred Images. In: Devi, B.R., Kumar, K., Raju, M., Raju, K.S., Sellathurai, M. (eds) Proceedings of Fifth International Conference on Computer and Communication Technologies. IC3T 2023. Lecture Notes in Networks and Systems, vol 897. Springer, Singapore. https://doi.org/10.1007/978-981-99-9704-6_35
Download citation
DOI: https://doi.org/10.1007/978-981-99-9704-6_35
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-9703-9
Online ISBN: 978-981-99-9704-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)