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+<a name="image.paramtrans"></a>
+## Parameterized transformations ##
+This section includes functions for performing transformations on 
+images requiring parameter Tensors like a warp `field` or a convolution
+`kernel`.
+
+<a name="image.warp"></a>
+### [res] image.warp([dst,]src,field,[mode,offset,clamp_mode,pad_val]) ###
+Warps image `src` (of size`KxHxW`) 
+according to flow field `field`. The latter has size `2xHxW` where the 
+first dimension is for the `(y,x)` flow field. String `mode` can 
+take on values [lanczos](https://en.wikipedia.org/wiki/Lanczos_resampling), 
+[bicubic](https://en.wikipedia.org/wiki/Bicubic_interpolation),
+[bilinear](https://en.wikipedia.org/wiki/Bilinear_interpolation) (the default), 
+or *simple*. When `offset` is true (the default), `(x,y)` is added to the flow field.
+The `clamp_mode` variable specifies how to handle the interpolation of samples off the input image.
+Permitted values are strings *clamp* (the default) or *pad*.
+When `clamp_mode` equals `pad`, the user can specify the padding value with `pad_val` (default = 0). Note: setting this value when `clamp_mode` equals `clamp` will result in an error.
+If `dst` is specified, it is used to store the result of the warp.
+Otherwise, returns a new `res` Tensor.
+
+<a name="image.convolve"></a>
+### [res] image.convolve([dst,] src, kernel, [mode]) ###
+Convolves Tensor `kernel` over image `src`. Valid string values for argument 
+`mode` are :
+ * *full* : the `src` image is effectively zero-padded such that the `res` of the convolution has the same size as `src`;
+ * *valid* (the default) : the `res` image will have `math.ceil(kernel/2)` less columns and rows on each side;
+ * *same* : performs a *full* convolution, but crops out the portion fitting the output size of *valid*;
+Note that this function internally uses 
+[torch.conv2](https://github.com/torch/torch7/blob/master/doc/maths.md#torch.conv.dok).
+If `dst` is provided, it is used to store the output image. 
+Otherwise, returns a new `res` Tensor.
+
+<a name="image.lcn"></a>
+### [res] image.lcn(src, [kernel]) ###
+Local contrast normalization (LCN) on a given `src` image using kernel `kernel`.
+If `kernel` is not given, then a default `9x9` Gaussian is used 
+(see [image.gaussian](tensorconstruct.md#image.gaussian)).
+
+To prevent border effects, the image is first global contrast normalized
+(GCN) by substracting the global mean and dividing by the global 
+standard deviation.
+
+Then the image is locally contrast normalized using the following equation:
+```lua
+res = (src - lm(src)) / sqrt( lm(src) - lm(src*src) )
+```
+where `lm(x)` is the local mean of each pixel in the image (i.e. 
+`image.convolve(x,kernel)`) and  `sqrt(x)` is the element-wise 
+square root of `x`. In other words, LCN performs 
+local substractive and divisive normalization. 
+
+Note that this implementation is different than the LCN Layer defined on page 3 of 
+[What is the Best Multi-Stage Architecture for Object Recognition?](http://yann.lecun.com/exdb/publis/pdf/jarrett-iccv-09.pdf).
+
+<a name="image.erode"></a>
+### [res] image.erode(src, [kernel, pad]) ###
+Performs a [morphological erosion](https://en.wikipedia.org/wiki/Erosion_(morphology)) 
+on binary (zeros and ones) image `src` using odd 
+dimensioned morphological binary kernel `kernel`. 
+The default is a kernel consisting of ones of size `3x3`. Number 
+`pad` is the value to assume outside the image boundary when performing 
+the convolution. The default is 1.
+
+<a name="image.dilate"></a>
+### [res] image.dilate(src, [kernel, pad]) ###
+Performs a [morphological dilation](https://en.wikipedia.org/wiki/Dilation_(morphology)) 
+on binary (zeros and ones) image `src` using odd 
+dimensioned morphological binary kernel `kernel`. 
+The default is a kernel consisting of ones of size `3x3`. Number 
+`pad` is the value to assume outside the image boundary when performing 
+the convolution. The default is 0.
+