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A new localized and computationally efficient approach is presented for shift/space-variant image restoration. Unlike conventional approaches, it models shift-variant blurring in a completely local form based on the recently proposed Rao transform (RT). RT facilitates almost exact inversion of the blurring process locally and permits very fine-grain(More)
A new passive ranging technique named Robust Depth-from-Defocus (RDFD) is presented for autofocusing in digital cameras. It is adapted to work in the presence of image shift and scale change caused by camera/hand/object motion. RDFD is similar to spatial-domain Depth-from-Defocus (DFD) techniques in terms of computational efficiency, but it does not require(More)
A new approach is presented for 3D shape recovery of local planar surface patches from two shift/space-variant defocused images. It is based on a recently proposed technique for inverting the shift-variant blurring in a camera system. It is completely localized, applicable to general point spread functions, and facilitates fine-grain parallel implementation(More)
Depth From Defocus (DFD) is a depth recovery method that needs only two defocused images recorded with different camera settings. In practice, this technique is found to have good accuracy for cameras operating in Normal Mode. In this paper, we present new algorithms to extend the DFD method to cameras working in Macro Mode used for very close objects in a(More)
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