image zooming is one of the important issues of image processing that maintains the quality and structure of image. Zooming an image necessitates placing the extra pixels in the image data. Moreover, adding the data to the image must be consistent with the texture in the image in order to prevent artificial blocks. In this study, the required pixels are estimated using barycentric rational interpolation. The proposed method is a non-linear one which can preserve the edges and reduces the blur and block artifacts on the zoomed image. Numerical results are presented using PSNR and SSIM fidelity measures and they are compared to some other methods. The average PSNR of the original image and image zooming was 33. 08 which can prove that image zooming is very similar to the original image. The experimental results reveal that the proposed method has a better performance compared to other methods and can provide good image quality.

Pixel transitions are critical in image processing, largely depending on interpolation methods to ensure smoothness and clarity. This work focuses on two widely used image interpolation techniques: nearest neighbor interpolation and bilinear interpolation, both implemented using integrated software code. Our methodology enables each interpolation technique to be applied independently, allowing for a direct comparison of their performance. To achieve a thorough evaluation of each interpolation method, we utilize a set of essential quality assessment metrics: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Grayscale Analysis, and Mean Squared Error (MSE). These metrics were selected to provide a balanced assessment of image sharpness, structural accuracy, and overall visual quality. The results of this study offer a detailed analysis of the strengths and limitations of each interpolation technique. These findings are intended to assist researchers and practitioners in selecting the most suitable interpolation method for their specific requirements in the image processing domain. By providing a comparative framework, this work contributes to the field by enhancing methods for assessing and optimizing image quality in digital imaging applications.

image inpainting is the process of filling in damaged or missing regions in an image by using information from known regions or known pixels of the image. One of the most important techniques for inpainting is convolution-based methods, in which a kernel is convolved with the damaged image iteratively. Convolution based algorithms are very quick, but they don’t have good results in structures and textural regions and result in blurring. The kernel size in the convolution-based algorithm is a critical parameter. The large size results in edge blurring, and if the kernel size is small, the information may not be sufficient for reconstruction. In this paper, a novel convolution-based algorithm is proposed that uses known gradient of the pixels to construct a convolution mask. In this algorithm, the kernel size is controlled by the gradient of the image in the known regions. The algorithm computes the weighted sum of the known pixels in a neighborhood around a damaged pixel and replaces the value in the place of that damaged pixel. The proposed algorithm is fast and results in good edges and smooth regions reconstruction. It is an iterative algorithm and its implementation is very simple. Experimental results show the effectiveness of our algorithm.