This paper presents a novel and fully automatic technique to estimate depth information from a single input image. The proposed method is based on a new image classification technique able to classify digital images (also in Bayer pattern format) as indoor, outdoor with geometric elements or outdoor without geometric elements. Using the information collected in the classification step a suitable depth map is estimated. The proposed technique is fully unsupervised and is able to generate depth map from a single view of the scene, requiring low computational resources.

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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...

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