The system will be used for observation or image capture in a variety of applications in industrial, military, consumer and medical settings
Mandi: Indian Institute of Technology Mandi researchers are using mathematical modelling to improve the quality of images captured by various imaging systems. Imaging systems are used for observation or image capture in a variety of applications in industrial, military, consumer and medical settings. They typically comprise of a camera, imaging lens and possibly a source of light. Active Imaging is the reflection of light collected from an object when a light source is used to illuminate it.
The research team led by Dr. Rajendra Kumar Ray used mathematical methods to eliminate speckles. They have used a grey level indicator-based nonlinear telegraph diffusion model which views the image as an elastic sheet, which when compressed, removes the speckles.
The results of their work have been published in the Society for Industrial and Applied Mathematics (SIAM) Journal of Imaging Sciences.
The proposed technique uses the benefit of the combined effects of diffusion equation as well as the wave equation. The wave nature of the system preserves the high oscillatory and texture patterns in an image. One of the main advantages of this partial differential equation (PDE)-based approach is that it has a strong theoretical base, which is not always certain for a non-PDE based approach.
“Operating our camera under flash mode is a simple example of active imaging” explains Dr. Ray, adding that active imaging is extensively used in many high-tech applications ranging from biomedical imaging to satellite-based surveillance.
The quality of images generated using some active imaging techniques are degraded by the presence of ‘speckles’. Speckles render the images grainy and result in loss of details. They can be caused by the imaging equipment itself or by the external environment associated with image capture, transfer or storage.
“Speckles are common in ultrasound, laser and synthetic aperture radar (SAR) images”, says Dr. Ray; these types of imaging systems are extensively used in critical applications like military surveillance and medical diagnosis. The poor image quality due to speckles, not only makes analysis unreliable or even dangerous but also renders automated image processing tasks difficult. Complex despeckling operations are therefore necessary to extract the actual image signal from the speckle noise.
“The proposed approach enjoys the benefits of both telegraph-diffusion equation and the grey level of the image, which is not only robust to remove noise from images but also preserves image structural details”, says Dr. Ray on the superiority of their method over existing despeckling approaches in its genre.
The researchers applied their model on test images and a few real-time SAR images, into which defects were deliberately introduced. They then compared the despeckling efficiency of their model with other existing models and showed that their model was superior to others.
“Our next aim is to apply this model into biomedical and SAR image processing”, says Dr. Ray, about the future of this development.
The removal of speckles is, as can be expected, critical in medical imaging applications, as well as in defence applications for surveillance operations. Apart from these high-tech applications, despeckling will also find use in imaging activities including cinematography and photography.
