Blind Deblurring Based on Sigmoid Function

An improved non-blind image deblurring methods based on FoEs

Traditional non-blind image deblurring algorithms always use maximum a posterior(MAP). MAP estimates involving natural image priors can reduce the ripples effectively in contrast to maximum likelihood(ML). However, they have been found lacking in terms of restoration performance. Based on this issue, we utilize MAP with KL penalty to replace traditional MAP. We develop an image reconstruction a...

Scale Adaptive Blind Deblurring

The presence of noise and small scale structures usually leads to large kernel estimation errors in blind image deblurring empirically, if not a total failure. We present a scale space perspective on blind deblurring algorithms, and introduce a cascaded scale space formulation for blind deblurring. This new formulation suggests a natural approach robust to noise and small scale structures throu...

Iris Image Deblurring Based on Refinement of Point Spread Function

Blurred iris images are inevitable during iris image acquisition due to limited depth of field and movement of subjects. The blurred iris images lose detailed texture information for accurate identity verification, so this paper proposes a novel iris image deblurring method to enhance the quality of blurred iris images. Our method makes full use of the prior information of iris images. Firstly,...

Geoid Determination Based on Log Sigmoid Function of Artificial Neural Networks: (A case Study: Iran)

A Back Propagation Artificial Neural Network (BPANN) is a well-known learning algorithmpredicated on a gradient descent method that minimizes the square error involving the networkoutput and the goal of output values. In this study, 261 GPS/Leveling and 8869 gravity intensityvalues of Iran were selected, then the geoid with three methods “ellipsoidal stokes integral”,“BPANN”, and “collocation” ...

Blind image deblurring using elastic-net based rank prior