Update: 2019/11/18

Author: sunyufei

Chapter 2/2.5.ipynb

@@ -18,23 +18,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### The toolbox supports a number of predefined 2_D linear spatial filters, obtained by using function `fspecial`, which generates a filter mask, `w`, using the syntax"
+    "The toolbox supports a number of predefined 2_D linear spatial filters, obtained by using function `fspecial`, which generates a filter mask, `w`, using the syntax"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "w = fspecial('type', parameters)"
+    "**w = fspecial('type', parameters)**"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### where `'type'` specifies the filter type, and `parameters` further define the specified filter. The spatial filters that `fspecial` can generate are summarized in Table 2.5, including applicable parameters for each filter."
+    "where `'type'` specifies the filter type, and `parameters` further define the specified filter. The spatial filters that `fspecial` can generate are summarized in Table 2.5, including applicable parameters for each filter."
    ]
   },
   {
@@ -48,7 +46,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### First, we generate and display the Laplacian filter:"
+    "First, we generate and display the Laplacian filter:"
    ]
   },
   {
@@ -79,7 +77,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Next we apply `w` to the input image, `f`, which is of class `uint8`:"
+    "Next we apply `w` to the input image, `f`, which is of class `uint8`:"
    ]
   },
   {

Chapter 4/4.02.ipynb

@@ -18,30 +18,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### The Image Processing Toolbox uses function `imnoise` to corrupt an image with noise. This function has the basic syntax"
+    "The Image Processing Toolbox uses function `imnoise` to corrupt an image with noise. This function has the basic syntax"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "g = imnoise(f, type, parameters)"
+    "**g = imnoise(f, type, parameters)**"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### where $f$ is the input image, and `type` and `parameters` are as explained below. Function `imnoise` converts the input image to class `double` in the range $[0,1]$ before adding noise to it. The syntax forms for this function are:"
+    "where $f$ is the input image, and `type` and `parameters` are as explained below. Function `imnoise` converts the input image to class `double` in the range $[0,1]$ before adding noise to it. The syntax forms for this function are:"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
     "g = imnoise(f, 'gaussian', m, var)"
    ]
@@ -50,7 +46,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### adds Gaussian noise of mean $m$ and variance $var$ to image $f$. The default is zero mean noise with 0.01 variance."
+    "adds Gaussian noise of mean $m$ and variance $var$ to image $f$. The default is zero mean noise with 0.01 variance."
    ]
   },
   {