Washington, July 9 (ANI): Coimbatore-based experts have turned to neural networks to help photographers clean up blur's noise and distortion in images.
S. Uma of the Coimbatore Institute of Technology and S. Annadurai of the Government College of Technology say that their approach can significantly reduce information loss while reversing blurring caused by lens aberrations and faults and reducing noise that distorts the appearance of an image.
They suggest that distortions in an image due to atmospheric disturbances between camera and distant subjects could be unravelled and a photo taken on a hot, hazy day made acceptable.
The researchers point out that earlier attempts at this kind of inverse filtering of an image rely on the image having a high signal-to-noise (SNR) ratio.
According to them, other approaches require huge amounts of computing power and are generally untenable.
They say that this is especially true in the fledgling field of artificial vision, whether robotic or prosthetic.
However, they add, some success with neural networks has been achieved.
Uma and Annadurai have developed a modified recurrent Hopfield neural network that builds and extends the work of others to allow them to quickly process an image, and reduce distortion, noise and blurring.
When they tested their approach on square grayscale images just 256 pixels across, they were able to reverse severe blurring and noise deliberately added to the original photographic sample to much more acceptable levels in a short time using limited computing resources than was possible with previous neural network approaches or any other inverse filtering techniques.
An analysis of the before and after quality shows that quality is improved by between 39 and 67 per cent using the team's approach, and results take half the time of other methods that produce lesser improvements.
The success bodes well for image processing, in various fields including vision research, art, homeland security, and science.
A research article describing the new approach has been published in the journal International Journal of Signal and Imaging Systems Engineering. (ANI)