in this paper, a modified fuzzy Sobel method for edge detection and enhancement is proposed. This method is a modification for Fuzzy Sobel method proposed in [I]. The proposed method overcomes the drawbacks of the conventional gradient methods for edge detection such as Prewitt and Sobel methods. it automatically obtains four threshold values, and apply fuzzy reasoning for edge enhancement. The edges extracted by this method are very clear and provides better representation for image edges and object contours.

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Several quality evaluation studies have been performed for videoplus-depth coding systems. In these studies, however, the distortions in the synthesized views have been quantified in experimental setups where both the texture and depth videos are compressed. Nevertheless, there are several factors that affect the quality of the synthesized view. Incorporating more than one source of distortion in the study could be misleading; one source of distortion could mask (or be masked by) the effect of other sources of distortion. In this paper, we conduct a quality evaluation study that aims to assess the distortions introduced by the view synthesis procedure and depth map compression in multiview-video-plus-depth coding systems. We report important findings that many of the existing studies have overlooked, yet are essential to the reliability of quality evaluation. In particular, we show that the view synthesis reference software yields high distortions that mask those due to depth map compression, when the distortion is measured by average luma peak signalto-noise ratio. In addition, we show what quality metric to use in order to reliably quantify the effect of depth map compression on view synthesis quality. Experimental results that support these findings are provided for both synthetic and real multiview-video-plusdepth sequences.Index Terms -Depth map compression, multi-view video coding, video plus depth, view synthesis

In multiplexed computational imaging schemes, high-resolution images are reconstructed by fusing the information in multiple low-resolution images detected by a two-dimensional array of low-resolution image sensors. The reconstruction procedure assumes a mathematical model for the imaging process that could have generated the low-resolution observations from an unknown high-resolution image. In practical settings, the parameters of the mathematical imaging model are known only approximately and are typically estimated before the reconstruction procedure takes place. Violations to the assumed model, such as inaccurate knowledge of the field of view of the imagers, erroneous estimation of the model parameters, and/or accidental scene or environmental changes can be detrimental to the reconstruction quality, even if they are small in number. We present an adaptive algorithm for robust reconstruction of high-resolution images in multiplexed computational imaging architectures. Using robust M-estimators and incorporating a similarity measure, the proposed scheme adopts an adaptive estimation strategy that effectively deals with violations to the assumed imaging model. Comparisons with nonadaptive reconstruction techniques demonstrate the superior performance of the proposed algorithm in terms of reconstruction quality and robustness.

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