A New Study of Blind Deconvolution with Implicit Incorporation of Nonnegativity Constraints

<table class="figure-group"><tr class="fig-image" id="a"><td><object data="https://static.hindawi.com/articles/ijcm/volume-2015/860263/figures/860263.fig.001a.svgz" name="860263.fig.001a" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(a) </b>True data</td></tr><tr class="fig-image" id="b"><td><object data="https://static.hindawi.com/articles/ijcm/volume-2015/860263/figures/860263.fig.001b.svgz" name="860263.fig.001b" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(b) </b>Blurred image</td></tr><tr class="fig-image" id="c"><td><object data="https://static.hindawi.com/articles/ijcm/volume-2015/860263/figures/860263.fig.001c.svgz" name="860263.fig.001c" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(c) </b>Algorithm <a href="../alg1/">1</a> result</td></tr></table>

<div>Good restoration results for Example 1 (a) from a corrupted image (b) using Algorithm <a href="../alg1/">1</a>. This model is able to improve the edges of the restored image (c), though the restoration is not excellent.</div>

International Journal of Computational Mathematics

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Figure 1

Figure 1: A New Study of Blind Deconvolution with Implicit Incorporation of Nonnegativity Constraints 