Iterative Refinement of Uniformly Focused Image Set for Accurate Depth from Focus

Iterative feature refinement for accurate undersampled MR image reconstruction.

Accelerating MR scan is of great significance for clinical, research and advanced applications, and one main effort to achieve this is the utilization of compressed sensing (CS) theory. Nevertheless, the existing CSMRI approaches still have limitations such as fine structure loss or high computational complexity. This paper proposes a novel iterative feature refinement (IFR) module for accurate...

Accurate Recovery of Three-Dimensional Shape from Image Focus

Image interpolation using shearlet based iterative refinement

This paper proposes an image interpolation algorithm exploiting sparse representation for natural images. It involves three main steps: (a) obtaining an initial estimate of the high resolution image using linear methods like FIR filtering, (b) promoting sparsity in a selected dictionary through iterative thresholding, and (c) extracting high frequency information from the approximation to refin...

Iterative Refinement of Transformation Parameters for Image Registration

Image registration is an important process in high-level image interpretation systems developed for civilian and defense applications. Registration is carried out in two phases : namely feature extraction and feature correspondence. The basic building block of feature based image registration scheme involves matching feature points that are extracted from a sensed image to their counter parts i...

Deep Depth From Focus

Depth from Focus (DFF) is one of the classical ill-posed inverse problems in computer vision. Most approaches recover the depth at each pixel based on the focal setting which exhibits maximal sharpness. Yet, it is not obvious how to reliably estimate the sharpness level, particularly in low-textured areas. In this paper, we propose ‘Deep Depth From Focus (DDFF)’ as the first end-to-end learning...