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Super-resolution interpolation is a popular technique, which is used to increase the image's resolution beyond its original size. However, there are several interpolation techniques available for super-resolution, and determining which technique to use for a given image can be challenging. The aim of the project is to perform a comparative study of different interpolation techniques for super-resolution and identify the best technique for different images. It starts by collecting a dataset of images with different characteristics such as noise, blur, and contrast and then preprocess the images and apply different interpolation techniques such as nearest-neighbor, bilinear, bicubic, Lanczos and Spline etc. The super-resolved images are evaluated and compared using different metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Mean Opinion Score (MOS). Based on the results of comparative study, conclusions about the strengths and weaknesses of each method are drawn. And the most appropriate interpolation technique for specific application is identified.
Jahnavi et al. (2024) studied this question.