joss format

paper.md

@@ -42,20 +42,18 @@ interest are transient or variable, cosmic ray rejection with multiple
 exposures can be sub-optimal or infeasible. These cases would require specialized
 algorithms to detect cosmic rays in single images.
 
-``deepCR`` is a Python package for single frame cosmic ray rejection which is
-based on deep learning and written with the Pytorch framework [@pytorch].
-The figure below shows the neural network architecture of ``deepCR``. Feature 
+![Neural network architecture of ``deepCR``. Feature 
 maps are represented by gray boxes while the number of channels and example 
 feature map dimensions are indicated on the top of and to the left of each 
 feature map, respectively. Different computational operations are marked in 
 the legend to the lower left. Unfilled boxes to the right of blue arrows represent 
 feature maps directly copied from the left, which are to be concatenated with the 
 adjacent feature map. To apply the inpainting model, the predicted mask (dotted box 
-at left) is concatenated with the original image as the input.
+at left) is concatenated with the original image as the 
+input.](https://raw.githubusercontent.com/profjsb/deepCR/master/imgs/network.png)
 
-![Figure 2](https://raw.githubusercontent.com/profjsb/deepCR/master/imgs/network.png)
-
-
+``deepCR`` is a Python package for single frame cosmic ray rejection which is
+based on deep learning and written with the Pytorch framework [@pytorch].
 Since ``deepCR`` is based on deep learning, different models trained on
 data taken with different instrument configurations are required, when applied to different
 data. The current version of ``deepCR`` is prepackaged with model for Hubble 
@@ -72,12 +70,11 @@ training models. To apply an available model, ``deepCR`` takes in an input image
  On GPU, training a new model takes as short as 20 minutes, 
  while applying ``deepCR`` on a 10 Mpix image requires less than 0.2 second, 
  orders of magnitude faster than current state of the art ``LACosmic`` [@lacosmic].
-The figure below shows examples of cosmic ray contaminated image cutouts (first row),
- deepCR cosmic ray mask predictions (middle row), and original image with artifact
-pixels replaced with deepCR predictions 
-(last row).
 
-![Figure 2](https://raw.githubusercontent.com/profjsb/deepCR/master/imgs/postage-sm.jpg)
+![Examples of cosmic ray contaminated image cutouts (first row),
+ deepCR cosmic ray mask predictions (middle row), and images with artifact
+pixels replaced with deepCR predictions 
+(last row).](https://raw.githubusercontent.com/profjsb/deepCR/master/imgs/postage-sm.jpg)
 
 In the paper accompanying ``deepCR`` [@deepcr], the authors showed that 
 on Hubble Space Telescope (HST) ACS/WFC data,