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+<a name="image.simpletrans"></a>
+## Simple Transformations ##
+This section includes simple but very common image transformations 
+like cropping, translation, scaling and rotation. 
+
+<a name="image.crop"></a>
+### [res] image.crop([dst,] src, x1, y1, [x2, y2]) ###
+Crops image `src` at coordinate `(x1, y1)` up to coordinate 
+`(x2, y2)`. If `dst` is provided, it is used to store the output
+image. Otherwise, returns a new `res` Tensor.
+
+### [res] image.crop([dst,] src, format, width, height) ###
+Crops a `width x height` section of source image `src`. The argument
+`format` is a string specifying where to crop: it can be "c", "tl", "tr",
+"bl" or "br" for center, top left, top right, bottom left and bottom right,
+respectively.  If `dst` is provided, it is used to store the output
+image. Otherwise, returns a new `res` Tensor.
+
+<a name="image.translate"></a>
+### [res] image.translate([dst,] src, x, y) ###
+Translates image `src` by `x` pixels horizontally and `y` pixels 
+vertically. If `dst` is provided, it is used to store the output
+image. Otherwise, returns a new `res` Tensor.
+
+<a name="image.scale"></a>
+### [res] image.scale(src, width, height, [mode]) ###
+Rescale the height and width of image `src` to have 
+width `width` and height `height`.  Variable `mode` specifies 
+type of interpolation to be used. Valid values include 
+[bilinear](https://en.wikipedia.org/wiki/Bilinear_interpolation)
+(the default), [bicubic](https://en.wikipedia.org/wiki/Bicubic_interpolation),
+or *simple* interpolation. Returns a new `res` Tensor.
+
+### [res] image.scale(src, size, [mode]) ###
+Rescale the height and width of image `src`.  Variable `size` is a number
+or a string specifying the size of the result image. When `size` is a
+number, it specifies the maximum height or width of the output. When it is
+a string like `WxH` or `MAX` or `^MIN`, `*SC` or `*SCn/SCd` it specifies
+the `height x width`, maximum height or width of the output, minimum height
+or width of the output, scaling factor (number), or fractional scaling
+factor (int/int), respectively.
+
+### [res] image.scale(dst, src, [mode]) ###
+Rescale the height and width of image `src` to fit the dimensions of 
+Tensor `dst`. 
+
+<a name="image.rotate"></a>
+### [res] image.rotate([dst,], src, theta, [mode]) ###
+Rotates image `src` by `theta` radians. 
+If `dst` is specified it is used to store the results of the rotation.
+Variable `mode` specifies type of interpolation to be used. Valid values include 
+*simple* (the default) or *bilinear* interpolation.
+
+<a name="image.polar"></a>
+### [res] image.polar([dst,], src, [interpolation], [mode]) ###
+Converts image `src` to polar coordinates. In the polar image, angular information is in the vertical direction and radius information in the horizontal direction.
+If `dst` is specified it is used to store the polar image. If `dst` is not specified, its size is automatically determined. Variable `interpolation` specifies type of interpolation to be used. Valid values include *simple* (the default) or *bilinear* interpolation. Variable `mode` determines whether the *full* image is converted to the polar space (implying empty regions in the polar image), or whether only the *valid* central part of the polar transform is returned (the default).
+
+<a name="image.logpolar"></a>
+### [res] image.logpolar([dst,], src, [interpolation], [mode]) ###
+Converts image `src` to log-polar coordinates. In the log-polar image, angular information is in the vertical direction and log-radius information in the horizontal direction.
+If `dst` is specified it is used to store the polar image. If `dst` is not specified, its size is automatically determined. Variable `interpolation` specifies type of interpolation to be used. Valid values include *simple* (the default) or *bilinear* interpolation. Variable `mode` determines whether the *full* image is converted to the log-polar space (implying empty regions in the log-polar image), or whether only the *valid* central part of the log-polar transform is returned (the default). 
+
+<a name="image.hflip"></a>
+### [res] image.hflip([dst,] src) ###
+Flips image `src` horizontally (left<->right). If `dst` is provided, it is used to
+store the output image. Otherwise, returns a new `res` Tensor.
+
+<a name="image.vflip"></a>
+### [res] image.vflip([dst,], src) ###
+Flips image `src` vertically (upsize<->down). If `dst` is provided, it is used to
+store the output image. Otherwise, returns a new `res` Tensor.
+
+<a name="image.flip"></a>
+### [res] image.flip([dst,] src, flip_dim) ###
+Flips image `src` along the specified dimension. If `dst` is provided, it is used to
+store the output image. Otherwise, returns a new `res` Tensor.
+
+<a name="image.minmax"></a>
+### [res] image.minmax{tensor, [min, max, ...]} ###
+Compresses image `tensor` between `min` and `max`. 
+When omitted, `min` and `max` are infered from 
+`tensor:min()` and `tensor:max()`, respectively.
+The `tensor` is normalized using `min` and `max` by performing :
+```lua
+tensor:add(-min):div(max-min)
+```
+Other optional arguments (`...`) include `symm`, `inplace`, `saturate`, and `tensorOut`.
+When `symm=true` and `min` and `max` are both omitted, 
+`max = min*2` in the above equation. This results in a symmetric dynamic 
+range that is particularly useful for drawing filters. The default is `false`.
+When `inplace=true`, the result of the compression is stored in `tensor`. 
+The default is `false`.
+When `saturate=true`, the result of the compression is passed through
+a function that clips the values between 0 and 1 
+(i.e. anything below 0 is set to 0, anything above 1 is set to 1).
+When provided, Tensor `tensorOut` is used to store results. 
+Note that arguments should be provided as key-value pairs (in a table).
+
+<a name="image.gaussianpyramid"></a>
+### [res] image.gaussianpyramid([dst,] src, scales) ###
+Constructs a [Gaussian pyramid](https://en.wikipedia.org/wiki/Gaussian_pyramid)
+of scales `scales` from a 2D or 3D `src` image or size 
+`[nChannel x] width x height`. Each Tensor at index `i` 
+in the returned list of Tensors has size  `[nChannel x] width*scales[i] x height*scales[i]`.
+
+If list `dst` is provided, with or without Tensors, it is used to store the output images. 
+Otherwise, returns a new `res` list of Tensors.
+
+Internally, this function makes use of functions [image.gaussian](tensorconstruct.md#image.gaussian),
+[image.scale](#image.scale) and [image.convolve](paramtransform.md#image.convolve).