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Reconstruct images from lensless coding cameras

lensless 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

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# lensless
Reconstruct images from lensless coding cameras

## 2D deconvolution using proximal gradient descent
The script `realtime_deconv.m` takes care of loading in measured data, preparing file names etc., then calls `diffuser_2d_deconv_v2.m` to do the deconvolution. `diffuser_2d_deconv_v2` relies on the code in `antipa/proxMin` to function, so you'll need that. At the top of `realtime_deconv` are:
1. `input_folder`  where you'll need to specify the path to the image to process 
2. `camera_type` string containing camera type. Currently needs to be either `'pco'` or `'flea3'`. This must be set because each camera's raw data contains a different bias, which, if not properly subtracted, leads to poor reconstruction.
3. `process_color` String specifying which color channel to use. For `flea3`, mono is the only option, but for `pco`, choices are:
  * `'mono'`: average RGB data in PSF and raw measurement
  * `'red'`: use only red channel (after demosaicing)
  * `'green'`: green only
  * `'blue'`: blue
  * `'all'`: solve all 3 colors independently, then create RGB output

At the top of `diffuser_2d_deconv_v2`, you can specify `ds` to be an integer. The images will all be downsampled (using a 'box' antialias filter) by this amount.

You can also set the regularizer parameters in the code, but this needs to be made more user friendly before it is recommended.

lensless

lensless 최신버젼 다운로드

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lensless

lensless 최신버젼 다운로드

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