Restoration of out-of-focus images based on circle of confusion estimate

Abstract

In this paper a new method for a fast out-of-focus blur estimation and restoration is proposed. It is suitable for CFA (Color Filter Array) images acquired by typical CCD/CMOS sensor. The method is based on the analysis of a single image and consists of two steps: 1) out-of-focus blur estimation via Bayer pattern analysis; 2) image restoration. Blur estimation is based on a block-wise edge detection technique. This edge detection is carried out on the green pixels of the CFA sensor image also called Bayer pattern. Once the blur level has been estimated the image is restored through the application of a new inverse filtering technique. This algorithm gives sharp images reducing ringing and crisping artifact, involving wider region of frequency. Experimental results show the effectiveness of the method, both in subjective and numerical way, by comparison with other techniques found in literature..H\ZRUGV Bayer pattern, circle of confusion, blur, blind restoration, inverse filtering, out-of-focus ,1752'8&7,21In the consumer electronics world the tendency is to provide an increasing number of functionality in a single unit. As a result, a mobile phone can acquire pictures and send them as e-mail messages through the cellular network. The images are acquired by CCD/CMOS sensor and processed by typical processing technique (Image Generation Pipeline) as described in [1]. However, due to limited dimension and quality of the camera inside the phone, images are often degraded. An enhancement of the acquired images is necessary in order to make them pleasant to the human viewer [2][3]. Our work is aimed to restore slightly out-of-focus images taken through low quality camera and lens. In this paper a new method for simultaneous out-of-focus blur estimation and restoration based on a single image is proposed. The method consists of two steps: 1) out-of-focus blur estimation; 2) image restoration by a new inverse filtering technique. Out-of-focus blur estimation and image restoration are two different image-processing problems. Blur estimation is used both for depth perception from two differently focused images of the same scene [4] and to estimate the out-of-focus blur of an image [5] [6]. The image restoration problem has been faced in several ways. NAS-RIF [7], for example, involves minimizing a cost function while NLIVQ (nonlinear interpolative vector quantization) [8] and ARMA methods [9] [10], are borrowed from data compression field. Others well known methods are blind deconvolution [11] and zero sheets algorithms [12]. A few of these methods require the knowledge of the Point Spread Function (PSF), others use statistical information. Others recent approaches like in [15], gives good results, but are not suitable for real time application. Our work starts from consideration found in [13], [14], which propose complete blind restoration systems. In [13], analyzed images are subdivided in sub-blocks and an edge-detection algorithm, based on DCT coefficients, is applied. PSF is computed based on the average 1-D step res...

Out of focus blur estimation using genetic algorithm

“…It seems the presented method in [11] works in noise free environments only. In [14] 11 dB improvement in SNR support is reported but there is no numerical result for method precision in its simulation results.…”

A robust noise independent method to estimate out of focus blur

“…This method works mainly on low and median frequency therefore sharp details are not improved in restoration process. There are some other methods that work in frequency domain; edge information is employed to find out of focus blur in [4]; however, extended DCT approach were used for edge detection of Bayer pattern in this paper. Using ML method [8] and wavelet transform [5] is also presented in literature to estimate out of focus blur.…”

Parameter Estimation for Defocus Blurred Image Based on Polar Transformation

“…Cannon has identified the point spread function (PSF) parameters in the power spectrum of the image by inspecting the negative peak (Cannon, 1976). Vivirito et al used bayer patterns analysis to extract edge detection details and used this information to find defocus blur (Vivirito,Battiato, Curti, et al,2002). Moghadam presented an iterative algorithm using optical transfer function estimate blur parameter (Moghaddam, 2007;Moghaddam, 2008).…”