initial commit, implemented (#29)

aydin/gui/tabs/qt/denoise.py

@@ -20,7 +20,7 @@
 class DenoiseTab(QWidget):
     """
     Now it is time to denoise the previously selected and cropped images.
-
+    <br><br>
     Aydin comes with a growing variety of self-supervised, auto-tuned, and unsupervised denoising algorithms,
     each with their own strengths and weaknesses in terms of speed, denoising performance, artifacts, and propensity
     to hallucinate unsubstantiated details. We recommend you check our

aydin/gui/tabs/qt/dimensions.py

@@ -14,14 +14,14 @@
 class DimensionsTab(QWidget):
     """
     Interpreting image dimensions
-
+    <br><br>
     Images can have many dimensions: 2D, 3D, 4D, 3D+t... Some dimensions are 'spatio-temporal' and
     the signal is expected to have a degree of continuity and correlation across these dimensions.
     Other dimensions are 'batch' dimensions, they just state that we have multiple images of the same
-    kind and shape. <b>Read more...<b>
+    kind and shape. <b>Read more...</b>
 
     <moreless>
-
+    <br><br>
     Finally, some dimensions are 'channel' dimensions and carry vectorial information
     for each voxel of the image. In this tab, you can help aydin better denoise your images by telling it
     how to interpret the dimensions of your images. The choices made here will impact denoising speed

aydin/gui/tabs/qt/summary.py

@@ -15,7 +15,7 @@ class SummaryTab(QWidget):
     a number of self-supervised, auto-tuned, and unsupervised image denoising algorithms.
     Aydin handles from the get-go n-dimensional array-structured images with an arbitrary number
     of batch dimensions, channel dimensions, and typically up to 4 spatio-temporal dimensions.
-
+    <br><br>
     You can drag and drop an image anywhere on the window to start or click `Add File(s)`
     button on top left part of the window. You can also load any of the example images.
     Once one or several image files have been loaded, you can adjust your desired settings
@@ -24,7 +24,7 @@ class SummaryTab(QWidget):
     before and after denoising.
 
     <moreless>
-
+    <br><br>
     To learn how to use Aydin Studio -- this user-friendly interface, check our
     <a href='https://royerlab.github.io/aydin/tutorials/tutorials_home.html'>tutorials</a>.
     To see examples of how to tune Aydin for a particular image, check our

aydin/gui/tabs/qt/training_cropping.py

@@ -11,7 +11,7 @@ class TrainingCroppingTab(BaseCroppingTab):
 
     Use the sliders to select a region of the image
     to define the cropping region for training or auto-tuning. Aydin automatically
-    suggests a crop based on the image content. <b>Read more...<b>
+    suggests a crop based on the image content. <b>Read more...</b>
 
     <moreless>