Date

2021-9-6

Author

Arslan, Ahmet

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Motion blur is a common problem that produces blurry images with inevitable information loss and unpleasant visual quality. Relative motion between scene and image recording device during exposure time causes the motion blur. This artifact degrades the human perceptual quality and also challenges subsequent computer vision applications. Most of the studies in the literature assume uniform blur present in the image and focuses on removing motion blur with non-blind deconvolution algorithms because of their low computational cost. Low-light conditions and rotation around the optical axis violate this uniform motion blur assumption and decrease the deblurring performances. In this thesis, we developed a novel adaptive mesh-grid based motion deblurring algorithm to handle non-uniform motion blur and decrease the high computation cost of existing methods. We use an inertial measurement unit (IMU) for obtaining the motion during the exposure period and generate a point spread function (PSF). The mesh-grid implementation is simply dividing an image into sub-block and performing blur kernel estimation and deblurring separately for each block. In addition, a variance parameter is proposed for representing the level of blur non-uniformity. To increase PSF estimation accuracy, mesh-size changed according to this variance parameter. Using this approach, two adaptive mesh-size algorithms are developed for the best quality and optimal quality with low computation time. It is observed that the proposed adaptive mesh-size algorithm increases the performance and quality of the existing methods.

Subject Keywords

Motion Blur, Motion Deblurring, PSF Estimation, Mesh-Grid

URI

https://hdl.handle.net/11511/93023

Collections

Graduate School of Natural and Applied Sciences, Thesis