Exploring the Effect of Sparse Recovery on the Quality of Image Superresolution

Dictionary learning can be used for image superresolution by learning a pair of coupled dictionaries of image patches from high-resolution and low-resolution image pairs such that the corresponding pairs share the same sparse vector when represented by the coupled dictionaries. These dictionaries then can be used to to reconstruct the corresponding high-resolution patches from low-resolution input images based on sparse recovery. The idea is to recover the shared sparse vector using the low-resolution dictionary and then multiply it by the high-resolution dictionary to recover the corresponding high-resolution image patch. In this work, we study the effect of the sparse recovery algorithm that we use on the quality of the reconstructed images. We offer empirical experiments to search for the best sparse recovery algorithm that can be used for this purpose. Image super-resolution is an important problem in computer vision due to its numerous practical applications and significant impact on various domains. Super-resolution techniques can significantly enhance the visual quality of images by increasing their resolution without improving the imaging device. This is particularly valuable in applications where high-quality images are crucial, such as medical imaging, satellite imagery, and surveillance.