Python DICOM Enhancement & Fixes

I have a Python code-base that already reads DICOM studies, but several routines throw errors or return images that look off. The immediate mission is to process those DICOMs for visual quality—think contrast, noise reduction, and overall clarity—so every slice is presentation-ready.

What I need from you: troubleshoot the existing scripts (built around pydicom, NumPy, and OpenCV) and remove the bugs that break the current pipeline, then add the missing pieces that perform proper image enhancement. New helper functions may be required for smoother file I/O, metadata preservation, and batch handling; I’ll point out the exact spots once you dive in.

Deliverables
• Clean, well-commented Python files that load DICOM series safely, run the enhancement steps, and save results without corrupting tags
• A short README or doc-string guide so I can reproduce the processing on a fresh machine
• Quick unit or notebook tests showing at least one before/after example per enhancement step

Everything should run on standard Python 3.x with the usual imaging stack (pydicom, OpenCV or scikit-image, NumPy). Drop-in compatibility is key—I want to pull the repo, install requirements, and hit run.