Image Processing: Super-resolution Image Reconstruction
In our mobile development projects, our experts frequently handle video and image processing tasks. To achieve superior and reliable results, Abto Software specialists conduct detailed research into various image restoration methods used in digital image processing applications.
One of the key techniques we explore is Super-resolution (SR), which enhances the resolution of images. Our approach is based on the low-resolution image formation model developed by S. Baker and T. Kanade.
For a detailed example of this technique, refer to our other project focused on Super-resolution for Identification Purposes.
The first step in our approach involves registering low-resolution images with sub-pixel precision in the frequency domain. In the next step, we address the super-resolution challenge by solving a least-squares optimization problem using a conjugate gradients algorithm.
We simulate these proposed algorithms in MATLAB and compare the results with standard magnification methods such as nearest-neighbor, bilinear, and bi-cubic interpolations. This comparison helps us evaluate the effectiveness of our image processing algorithms.
Additionally, we conduct research on image deconvolution, which is also integral to our other projects. Image deconvolution and other techniques play a crucial role in improving image quality, removing noise, and enhancing the final image.
Apart from image super-resolution, we are also conducting the research on Image Deconvolution method, which is used in our other project.
To get to know more about different techniques that we use in projects that delve into the 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.
