Restoring and Processing Images by Applying Geometric Transformations with Respect to A Reference Image
Keywords:
Algorithms, Blurred images, Digital, Imaging systems, Remote sensing, RestorationAbstract
Imaging systems are widely used in applications such as commercial photography, microscopy, aerial imaging, astronomy, and space exploration. However, the acquired images or videos often suffer from blur caused by lens imperfections, transmission media, image processing algorithms, or motion of the camera or subject. Quantifying and mitigating this blur is a critical challenge. Image processing and restoration techniques aim to enhance image or video quality using various approaches, primarily through the manipulation of pixel intensities. An image can be mathematically modeled as a two-dimensional function f(x,y)f(x, y)f(x,y), where xxx and yyy represent spatial coordinates. In this study, geometric transformations are applied for image restoration and processing. The methodology involves reading the image into the algorithm, defining pixel coordinates, applying a geometric transformation with a rotation angle of 31 degrees, performing inlier–outlier matching, and generating the restored image. Additionally, performance is evaluated using mean squared error (MSE) and peak signal-to-noise ratio (PSNR). The entire investigation is implemented using a MATLAB .m script.
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