Minor edits

ShrayanRoy · Mar 6, 2024 · c7d6431 · c7d6431
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diff --git a/presentation/finalpresentation.Rmd b/presentation/finalpresentation.Rmd
@@ -233,8 +233,6 @@ $$k(x,y) = \frac{C_{h,r}}{2\pi h^2} e^{-\frac{x^2 + y^2}{2h^2}} \times \text{I}_
 
 * The prior family used is motivated from the observation that the distribution of image gradients have a **sharp peak near zero** and and relatively **heavier tails** than the Gaussian distribution and Laplace distribution.
 
---
-
 ```{r ,warning=FALSE,echo=FALSE,out.width='75%',out.height='30%',fig.align='center',echo=FALSE,fig.cap="Figure: Eight sharp images and their density plot of horizontal gradients"}
 
 knitr::include_graphics("pimg/prior1.png")
@@ -337,8 +335,6 @@ $$\hat{\theta} = \underset{\boldsymbol{\theta}}{\text{argmax}} \ \log L(\boldsym
 
 * Simulated experiments using disc kernel are conducted for this purpose.
 
---
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 * We consider a sequence of values for $r \in [1,4]$ with $\Delta{r} = 0.05$, and for $\sigma \in [0.01,0.4]$ with $\Delta{\sigma} = 0.01$, with $\eta = 0.001$ constant.
 
 --
@@ -375,8 +371,6 @@ knitr::include_graphics("pimg/exp2.png")
 
 * Poor estimation of blur kernel can lead to artifacts.
 
---
-
 ```{r ,warning=FALSE,echo=FALSE,out.width='65%',fig.align='center',echo=FALSE,fig.cap="Figure: Effect of poor estimation of radius r in disc kernel (Using Richardson Lucy Algorithm)"}
 
 knitr::include_graphics("pimg/deconv_prob.png")

diff --git a/presentation/finalpresentation.html b/presentation/finalpresentation.html
@@ -243,8 +243,6 @@
 
 * The prior family used is motivated from the observation that the distribution of image gradients have a **sharp peak near zero** and and relatively **heavier tails** than the Gaussian distribution and Laplace distribution.
 
---
-
 &lt;div class="figure" style="text-align: center"&gt;
 &lt;img src="pimg/prior1.png" alt="Figure: Eight sharp images and their density plot of horizontal gradients" width="75%" height="30%" /&gt;
 &lt;p class="caption"&gt;Figure: Eight sharp images and their density plot of horizontal gradients&lt;/p&gt;
@@ -347,8 +345,6 @@
 
 * Simulated experiments using disc kernel are conducted for this purpose.
 
---
-
 * We consider a sequence of values for `\(r \in [1,4]\)` with `\(\Delta{r} = 0.05\)`, and for `\(\sigma \in [0.01,0.4]\)` with `\(\Delta{\sigma} = 0.01\)`, with `\(\eta = 0.001\)` constant.
 
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@@ -385,8 +381,6 @@
 
 * Poor estimation of blur kernel can lead to artifacts.
 
---
-
 &lt;div class="figure" style="text-align: center"&gt;
 &lt;img src="pimg/deconv_prob.png" alt="Figure: Effect of poor estimation of radius r in disc kernel (Using Richardson Lucy Algorithm)" width="65%" /&gt;
 &lt;p class="caption"&gt;Figure: Effect of poor estimation of radius r in disc kernel (Using Richardson Lucy Algorithm)&lt;/p&gt;