Photo by TheRegisti on Unsplash

Low Quality Image Detection—Part 1

How to perform low quality image detection (for instance, blur detection, glare detection or noise detection) using machine learning and deep learning.

Low-quality image detection is an interesting machine learning problem because it addresses real-world challenges across diverse applications (for instance, blurry image detection in surveillance systems or automatic quality check while taking photos with a smartphone). The quality of images can significantly impact the outcomes of various downstream tasks, making the development of effective detection algorithms crucial.

In this tutorial we try to build a machine learning model able to detect whether a photo has any quality issue.

An example of good quality photo. Photo by Clay Banks on Unsplash.

Image quality issues may include: blurriness, presence of bands, noises, over exposure, glare, darkness, etc.

Examples of low quality images (generated with the algorithms that will be presented in this tutorial). Original (good quality) photo by Clay Banks on Unsplash.

Whenever we are trying to perform a blur detection, a glare detection or a noise…