# CompressiveSpectralImaging
### *class* deepinv.physics.CompressiveSpectralImaging(img_size, mask=None, mode='ss', shear_dir='h', device='cpu', rng=None, \*\*kwargs)
Bases: [`LinearPhysics`](https://deepinv.org/api/stubs/deepinv.physics.LinearPhysics.html.md#deepinv.physics.LinearPhysics)
Compressive Hyperspectral Imaging operator.
Coded-aperture snapshot spectral imaging (CASSI) operator, which is a popular
approach for hyperspectral imaging.
The CASSI operator performs a combination of masking (“coded aperture”), shearing,
and flattening in the channel dim.
We provide two specific popular CASSI models: single-disperser (i.e. only spatial encoding)
and spatial-spectral encoding:
$$
y =
\begin{cases}
\Sigma_{c=1}^{C} S^{-1} MSx & \text{if mode='spatial-spectral'} \\
\Sigma_{c=1}^{C} SMx & \text{if mode='single-disperser'}
\end{cases}
$$
where $M$ is a binary mask (the “coded aperture”), $S$ is a pixel shear in the 2D
channel-height of channel-width plane and $C$ is number of channels.
Note that the output size of the single-disperser mode has the `H` or `W` dim extended by `C-1` pixels.
For more details see e.g. the paper Choi *et al.*[1](#footcite-choi2017high).
The implementation is a type of linear physics as it is not completely decomposable due to edge effects and different scaling.
* **Examples:**
```pycon
>>> from deepinv.physics import CompressiveSpectralImaging
>>> physics = CompressiveSpectralImaging(img_size=(7, 32, 32))
>>> x = torch.rand(1, 7, 32, 32) # 7-band image
>>> y = physics(x)
>>> y.shape
torch.Size([1, 1, 32, 32])
```
* **Parameters:**
* **img_size** ([*tuple*](https://docs.python.org/3.12/library/stdtypes.html#tuple)) – image size, must be of form (C,H,W) where C is number of bands.
* **mask** ([*torch.Tensor*](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor) *,* [*float*](https://docs.python.org/3.12/library/functions.html#float)) – coded-aperture mask. If `None`, generate random mask using
[`deepinv.physics.generator.BernoulliSplittingMaskGenerator`](https://deepinv.org/api/stubs/deepinv.physics.generator.BernoulliSplittingMaskGenerator.html.md#deepinv.physics.generator.BernoulliSplittingMaskGenerator) with masking ratio
of 0.5, if mask is `float`, sets mask ratio to this. If `Tensor`, set mask to this,
must be of shape `(B,C,H,W)`.
* **mode** ([*str*](https://docs.python.org/3.12/library/stdtypes.html#str)) – ‘sd’ for SD-CASSI i.e. single disperser (only spatial encoding) or
‘ss’ for SS-CASSI i.e. spatial-spectral encoding. Defaults to ‘ss’. See above for details.
* **shear_dir** ([*str*](https://docs.python.org/3.12/library/stdtypes.html#str)) – “h” for shear in H-C plane or “w” for shear in W-C plane where C is channel dim, defaults to “h”
* **device** ([*torch.device*](https://docs.pytorch.org/docs/stable/tensor_attributes.html#torch.device)) – torch device, only used if `mask` is `None` or `float`
* **rng** ([*torch.Generator*](https://docs.pytorch.org/docs/stable/generated/torch.Generator.html#torch.Generator)) – torch random generator, only used if `mask` is `None` or `float`
* **References:**
* **[1]** Inchang Choi, Daniel S. Jeon, Giljoo Nam, Diego Gutierrez, and Min H. Kim. High-quality hyperspectral reconstruction using a spectral prior. *ACM Trans. Graph.*, 2017.
#### A(x, mask=None, \*\*kwargs)
Applies the CASSI forward operator.
If a mask is provided, it updates the class attribute `mask` on the fly.
* **Parameters:**
* **x** ([*torch.Tensor*](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor)) – input image
* **mask** ([*torch.Tensor*](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor)) – CASSI mask
* **Returns:**
([`torch.Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor)) output measurements
* **Return type:**
[torch.Tensor](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor)
#### A_adjoint(y, mask=None, \*\*kwargs)
Applies the CASSI adjoint operator.
If a mask is provided, it updates the class attribute `mask` on the fly.
* **Parameters:**
* **x** ([*torch.Tensor*](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor)) – input measurements
* **mask** ([*torch.Tensor*](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor)) – CASSI mask
* **Returns:**
([`torch.Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor)) output image
* **Return type:**
[torch.Tensor](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor)
#### crop(x)
Crop image on bottom or on right.
* **Parameters:**
**x** ([*torch.Tensor*](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor)) – input padded image
#### flatten(x)
Average over channel dimension
* **Parameters:**
**x** ([*torch.Tensor*](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor)) – input image of shape B,C,H,W
#### pad(x)
Pad image on bottom or on right.
* **Parameters:**
**x** ([*torch.Tensor*](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor)) – input image
#### shear(x, un=False)
Efficient pixel shear in channel-spatial plane
* **Parameters:**
* **x** ([*torch.Tensor*](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor)) – input image of shape (B,C,H,W)
* **un** ([*bool*](https://docs.python.org/3.12/library/functions.html#bool)) – if `True`, unshear in opposite direction.
#### unflatten(y)
Repeat over channel dimension
* **Parameters:**
**y** ([*torch.Tensor*](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor)) – input image of shape B,C,H,W
## Examples using `CompressiveSpectralImaging`:
[Remote sensing with satellite images](https://deepinv.org/auto_examples/physics/demo_remote_sensing.html.md)