EXPLICIT RINGING REMOVAL IN IMAGE DEBLURRING

Abstract

In this paper, we present a simple yet effective image deblurring method to produce ringing-free deblurred images. Our work is inspired by the observation that large-scale deblurring ringing artifacts are measurable through a multiresolution pyramid of low-pass filtering of the blurred-deblurred image pair. We propose to model such a quantification as a convex cost function and minimize it directly in the deblurring process in order to reduce ringing regardless of its cause. An efficient primal-dual algorithm is proposed as a solution to this optimization problem. Since the regularization is more biased toward ringing patterns, the details of the reconstructed image are prevented from over-smoothing. An inevitable source of ringing is sensor saturation which can be detected costlessly contrary to most other sources of ringing. However, dealing with the saturation effect in deblurring introduces a non-linear operator in optimization problem. In this paper, we also introduce a linear approximation as a solution to handling saturation in the proposed deblurring method. As a result of these steps, we significantly enhance the quality of the deblurred images. Experimental results and quantitative evaluations demonstrate that the proposed method performs favorably against state-ofthe- art image deblurring methods.

Existing System

Dark channel prior algorithm

Proposed System

In this project, we consider ringing removal as a sub-problem of deconvolution and present a deconvolution method that generates restored images with significantly reduced ringing and well-preserved details. We first develop a means of quantifying ringing in the deblurred image presented in a convex function. Then, we propose to minimize the amount of ringing using this ringing measurement along with the traditional TV in a deblurring model. The proposed model explicitly accounts for the ringing regardless of its causes. Hence, in the first step, we assume a linear imaging model and drop the non-linear function R (.) from the model. We propose an efficient primal-dual solution to the optimization problem that significantly minimizes the ringing while preventing details from being washed out. Sensor saturation mainly due to long exposures is very common in light-limited photography where motion blur often occurs. It is arguably the most noticeable source of ringing and yet complimentary to detect in an image. Hence, in the next step, we extend the deblurring method to non-linear imaging cases when sensor saturation occurs. We avoid complicated CRF estimation and consider only a basic non-differentiable clip function for R (.). Despite the non-differential behaviour of the clip function, we propose an explicit solution to the problem of deblurring images with saturated pixels which is more efficient than standard numerical solutions. For the sake of brevity, the proposed deblurring method is presented in the context of non-blind deblurring. However, it can be extended to a blind deblurring scheme with a little effort.

Architecture

BLOCK DIAGRAM