Convolutional Deblurring for Natural Imaging
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In this paper, we propose a novel design of image deblurring in the form of one-shot convolution filtering that can directly convolve with naturally blurred images for restoration. The problem of optical blurring is a common disadvantage to many imaging applications that suffer from optical imperfections. Despite numerous deconvolution methods that blindly estimate blurring in either inclusive or exclusive forms, they are practically challenging due to high computational cost and limited image quality reconstruction. Both conditions of high accuracy and high speed are prerequisite for high-throughput imaging platforms in digital archiving. It becomes equally important as reconstruction accuracy that how quickly the implemented algorithms are capable of recovering the latent images? In such platforms, deblurring is required after image acquisition to feed into the communication pipeline to be either stored, previewed, or processed for high-level interpretation. Therefore, on-the-fly correction of such images are highly preferred to avoid possible time delays, mitigate computational expenses, and increase the image perception quality. We bridge this gap by synthesizing a deconvolution kernel as a linear combination of Finite Impulse Response (FIR) even derivative filters that can be directly convolved with input blurry images to boost the frequency falloff of the Point Spread Function (PSF) associated with the optical blur. We employ a Gaussian lowpass filter to decouple the image denoising problem for image edge deblurring. Furthermore, we propose a blind approach to estimate the PSF statistics for two Gaussian and Laplacian models that are common in many imaging pipelines. Thorough experiments are designed to test and validate the efficiency of the proposed method using 2054 naturally blurred images across six imaging applications and seven state-of-the-art deconvolution methods.
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