Indian Institute of Technology Madras (IIT-M) on Thursday said its researchers have developed enhanced image processing techniques to mitigate the impact of haze on images captured by surveillance cameras.
This can prove crucial in helping law enforcement agencies solve crimes and help the common public as many communities are now installing CCTVs to safeguard themselves.
This research can also be applied to self-driving and autonomous vehicles as they also require high-quality images taken by cameras for efficient and safe navigation, especially in urban areas, the Institute said.
To improve surveillance camera images, the research team proposed a new approach, published in the peer-reviewed journal of IEEE Transactions on Image Processing, to increase the visibility of images degraded by haze.
“Reducing the effect of haze is a very challenging problem, more so when only a single observation of the scene is available,” Professor A.N. Rajagopalan, Institute Chair Professor, Department of Electrical Engineering, IIT Madras, said in a statement.
“After this successful research, we are looking at addressing heavy haze and other adverse weather conditions,” Rajagopalan added.
According to the Institute, the research team devised a post-processing technique to mitigate the effect of haziness. They modeled portions of haze in the image from the standpoint of the image formation process itself.
They based their research on the premise that if an image is considered as comprising of several local patches, then the similarity with the neighboring patches must relate to the similarity in the depth of the image as well.
In case there are abrupt changes in patch similarity then it will typically imply changes in-depth information too, the Institute said.
The scientists stated that as the degradation of image quality due to atmospheric conditions increases with depth, the patch similarity within a local neighborhood can be used as a measure to estimate the parameters of a model that captures the effect of a decrease in intensity of the image.
The parameter corresponding to the haze component is computed by using the non-local mean of patches which show abrupt changes.
Using a similar approach, the research team was also able to handle haziness in images taken in night-time or even underwater.