Image Processing: Super-resolution Image Reconstruction

Image Processing: Super-resolution Image Reconstruction

Within the frames of our mobile development projects, our experts often deal with video and image processing. To obtain better and more trustworthy results Abto Software specialists conduct specific internal research of various image restoration methods used in digital image processing applications.

One of the techniques for imaging system resolution enhancement that we explore is Super-resolution (SR). Our approach is based on the model of the low-resolution image formation given by S. Baker and T. Kanade. You can also read about its usage in one of our other projects – Super-resolution for Identification Purposes.

The first stage of our approach is the registration of low-resolution images with sub-pixel precision in the frequency domain. To find a super-resolution image in the second stage we solve the least-squares optimization problem by using a conjugated gradients algorithm.

The proposed algorithms are simulated in MATLAB and the results are compared with such standard magnification methods as nearest-neighbor, bilinear, and bi-cubic interpolations.

Super-resolution Image Reconstruction
1. Not reconstructed image
Image Reconstruction
2. Reconstructed 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.

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