Super-Resolution for Identification Purposes
Our Research and Modeling experts explored how to create high-resolution (HR) images from a set of low-resolution (LR) images. They successfully developed an excellent program that enhances image quality, revealing even the smallest details that were previously invisible.
This program can be applied in various fields such as astronomy, microscopy, and healthcare. It’s also useful for identification, video surveillance systems, and any area where high-quality images are crucial.
Implementing the algorithm of high resolution images formation
Abto Software created a MATLAB prototype to implement object recognition. We created an algorithm based on the model of high-resolution images formation.
We successfully carried out a search for a certain high-resolution image. Our experts offered the solution of the optimization task by means of the conjugate gradients method.
Super resolution in detail – methods used in prototype and main results
Below you can see the example of an image in twelve low resolution frames of 128×128 pixels which we needed to resize and receive high resolution image. For that there can be used three methods:
1) “Nearest-neighbor interpolation”: interpolation by the nearest neighbor gives unsatisfactory results, as small details are still not seen;
2) Bicubic interpolation: the results are also poor as the received images are blurred;
3) “Super-resolution”: by means of super-resolution method most clear and distinct image is received where the smallest details which couldn’t be seen on the incoming images can be identified.
The image below obtaind after using “nearest-neighbor interpolation”:
The image below obtained after applying bicubic interpolation method:
The image below obtained after “super-resolution” method was used:
Wrapping Up
To get to know more about different techniques that we use in projects that delve into Digital Image Processing field, please view our articles “Introduction to Image Restoration Methods” posted in Abto Software Blog. There we describe general Image Restoration approaches, Convolution, Deconvolution, Inverse Filtering and Wiener filtering and some other topics regarding image restoration.