Local Scale Control for Edge Detection and Blur Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Multiresolution Segmentation for Images with Low Depth of Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Geometric Approach to Shape from Defocus
IEEE Transactions on Pattern Analysis and Machine Intelligence
Removing camera shake from a single photograph
ACM SIGGRAPH 2006 Papers
Focus Area Extraction by Blind Deconvolution for Defining Regions of Interest
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image and depth from a conventional camera with a coded aperture
ACM SIGGRAPH 2007 papers
Segmenting a low-depth-of-field image using morphological filters and region merging
IEEE Transactions on Image Processing
Reconstructing arbitrarily focused images from two differently focused images using linear filters
IEEE Transactions on Image Processing
Defocus map estimation from a single image
Pattern Recognition
Directional high-pass filter for blurry image analysis
Image Communication
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Image defocus estimation is useful for several applications including deblurring, blur magnification, measuring image quality, and depth of field segmentation. In this paper, we present a simple yet effective approach for estimating a defocus blur map based on the relationship of the contrast to the image gradient in a local image region. We call this relationship the local contrast prior. The advantage of our approach is that it does not require filter banks or frequency decomposition of the input image; instead we only need to compare local gradient profiles with the local contrast. We discuss the idea behind the local contrast prior and demonstrate its effectiveness on a variety of experiments.