Convolutional Deblurring for Natural Imaging

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 low image reconstruction quality. Both conditions of high accuracy and high speed are prerequisites for high-throughput imaging platforms in digital archiving. In such platforms, deblurring is required after image acquisition before being stored, previewed, or processed for high-level interpretation. Therefore, on-the-fly correction of such images is important to avoid possible time delays, mitigate computational expenses, and increase 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 blurry input images to boost the frequency fall-off of the Point Spread Function (PSF) associated with the optical blur. We employ a Gaussian low-pass 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.Comment: 15 pages, for publication in IEEE Transaction Image Processin

Програмні засоби для деблюрінга зображень за допомогою генеративних змагальницьких систем

До бакалаврської дипломної роботи Зарічкового Олександра Анатолійовича на тему: «Програмні засоби для деблюрінга зображень за допомогою генеративних змагальницьких систем». Дипломна робота присвячена розробці алгоритму для деблюрінга зображень за допомогою генеративних змагальницьких систем та впровадженню його в використання через веб-застосунок. У роботі проведено порівняльний аналіз існуючих підходів для деблюрінга зображень на основі алгоритмів машинного навчання. Розроблені рекомендації щодо функціонального застосування розробленого програмного продукту. Загальний обсяг роботи: 62 сторінки, 17 рисунків, 19 таблиць, 45 джерел.Abstract to the bachelor thesis work of Zarichkovyi Oleksandr: "Software for image deblurring using generative adversarial systems." This thesis is devoted to the development of an algorithm for the deblurring of images using generative competing systems and implemented as the web application. In this work was done a comparative analysis of existing approaches for the images deblurring that based on machine learning algorithms. Developed recommendations for the functional usage of the developed application. Work includes 62 pages, 17 figures, 19 tables, 45 citations

Training Very Deep CNNs for General Non-Blind Deconvolution

© 1992-2012 IEEE. Non-blind image deconvolution is an ill-posed problem. The presence of noise and band-limited blur kernels makes the solution of this problem non-unique. Existing deconvolution techniques produce a residual between the sharp image and the estimation that is highly correlated with the sharp image, the kernel, and the noise. In most cases, different restoration models must be constructed for different blur kernels and different levels of noise, resulting in low computational efficiency or highly redundant model parameters. Here we aim to develop a single model that handles different types of kernels and different levels of noise: general non-blind deconvolution. Specifically, we propose a very deep convolutional neural network that predicts the residual between a pre-deconvolved image and the sharp image rather than the sharp image. The residual learning strategy makes it easier to train a single model for different kernels and different levels of noise, encouraging high effectiveness and efficiency. Quantitative evaluations demonstrate the practical applicability of the proposed model for different blur kernels. The model also shows the state-of-the-art performance on synthesized blurry images