In this paper, we propose a spatially-varying deblurring method to remove the out-of-focus blur. Our proposed method mainly contains three parts: blur map generation, image deblurring, and scale selection. First, we derive a blur map using local contrast prior and the guided filter. Second, we propose our image deblurring method with L1-2 optimization to obtain a better image quality. Finally, we adopt the scale selection to ensure our output free from ringing artifacts. The experimental results demonstrate our proposed method is promising.Index Terms-out-of-focus blur, spatially-varying deblurring, L1-2 optimization, guided blur map.

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We propose a method to detect saliency from a single image using feature extraction and superpixel belief propagation. We observe that the previous works are hard to deal with the intrinsic material discontinuity and non-homogeneous color distribution within an object or a region. Motivated by this observation, we bring the belief propagation into the saliency detection. First, we separate the image into middlelevel superpixels and also extract the low-level feature within each superpixel. Then, we build up a Markov-Random-Field (MRF) on the middle-level super-pixels and adopt propagation technique to optimize the superpixel saliency. Afterward, we refine this middle-level solution to per-pixel saliency map. Experimental results demonstrate that our proposed method is promising as compared to the state-of-the-art methods in both MSRA-1000 and SED datasets.

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