Exploring the Effect of Sparse Recovery on the Quality of Image Superresolution
This work addresses the problem of improving image superresolution quality for applications like medical imaging or photography, but it is incremental as it focuses on optimizing an existing method rather than introducing a new one.
The paper investigates how different sparse recovery algorithms affect the quality of image superresolution using coupled dictionary learning, finding that the choice of algorithm significantly impacts reconstruction performance.
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.