Work only on affected columns in the sinogram

httomolibgpu/prep/stripe.py

@@ -254,38 +254,45 @@ def _rs_large(sinogram, snr, size, drop_ratio=0.1, norm=True):
     """
     Remove large stripes.
     """
-    drop_ratio = max(min(drop_ratio, 0.8), 0)  # = cp.clip(drop_ratio, 0.0, 0.8)
+    drop_ratio = max(min(drop_ratio, 0.8), 0)
     (nrow, ncol) = sinogram.shape
     ndrop = int(0.5 * drop_ratio * nrow)
     sinosort = cp.sort(sinogram, axis=0)
     sinosmooth = median_filter(sinosort, (1, size))
+
     list1 = cp.mean(sinosort[ndrop : nrow - ndrop], axis=0)
     list2 = cp.mean(sinosmooth[ndrop : nrow - ndrop], axis=0)
     listfact = list1 / list2
-    # Locate stripes
+
     listmask = _detect_stripe(listfact, snr)
     listmask = binary_dilation(listmask, iterations=1).astype(listmask.dtype)
 
-    # Normalize
+    # Normalize only affected columns
     if norm:
-        sinogram /= cp.tile(listfact, (nrow, 1))
+        sinogram[:, listmask > 0] /= listfact[None, listmask > 0]
 
+    # Identify affected columns
+    listxmiss = cp.where(listmask > 0.0)[0]
+    if listxmiss.size == 0:
+        return sinogram  # No stripes, return early for efficiency
+
+    # Process only affected columns
     sino_transposed = sinogram.T
-    ids_sort = cp.argsort(sino_transposed, axis=1)
+    sino_subset = sino_transposed[listxmiss]  # Extract affected columns
 
-    # Apply sorting without explicit matindex
-    sino_sorted = cp.take_along_axis(sino_transposed, ids_sort, axis=1)
+    # Sort only the required subset
+    ids_sort = cp.argsort(sino_subset, axis=1)
+    sino_sorted = cp.take_along_axis(sino_subset, ids_sort, axis=1)
 
-    # Smoothen sorted sinogram
-    sino_sorted[:, :] = cp.transpose(sinosmooth)
+    # Apply smoothing
+    sino_sorted[:, :] = cp.transpose(sinosmooth[:, listxmiss])
 
     # Restore original order
     ids_restore = cp.argsort(ids_sort, axis=1)
-    sino_corrected = cp.take_along_axis(sino_sorted, ids_restore, axis=1).T
+    sino_corrected = cp.take_along_axis(sino_sorted, ids_restore, axis=1)
 
-    # Apply corrections only to affected columns
-    listxmiss = cp.where(listmask > 0.0)[0]
-    sinogram[:, listxmiss] = sino_corrected[:, listxmiss]
+    # Place back corrected data
+    sinogram[:, listxmiss] = sino_corrected.T
 
     return sinogram