Reducing Noise in Digital Images

NVidia has announced impressive progress in using AI to remove noise from “grainy” images without access to a clean version of the image to learn from.

https://news.developer.nvidia.com/ai-can-now-fix-your-grainy-photos-by-only-looking-at-grainy-photos/

By noise – they tend to refer to the grainy result of a low light digital photo, a side benefit being that they can also easily remove textual noise.  Currently, the result is “softer” than the original clean image, but I’m curious whether it will end up causing issues with watermarking or other copy protection schemes.  At what point will “good enough” be sufficient for a derivative use when we deal in low resolution imagery on the web all the time?

Many of us in collections rely on the use of watermarks to make openly sharing our collections more palatable to our donors.  Already, we have to warn them that there is no low barrier way to really prevent unattributed image reuse… This is simply going to make that conversation even more difficult.

 

Detecting Book Spines in an Image

I have images of stacks of books – can I detect the spines? Can I get the call numbers?

Combining Image and Text Features: A Hybrid Approach to Mobile Book Spine Recognition
(combine text recognition with comparing to known images of book spines)

Automatic Book Spine Extraction and Recognition for Library Inventory Management

Discussion on the OPENCV forum

Matching book-spine images for library shelf-reading process automation

Smart Library: Identifying Books on Library Shelves Using Supervised Deep Learning for Scene Text Reading

Mobile augmented reality for books on a shelf

Viewpoint-independent book spine segmentation

Identifying books in library using line segment detector and contour clustering

A review of Augmented Reality and its application in context aware library system