Single image super-resolution using self-optimizing mask via fractional-order gradient interpolation and reconstruction
This addresses the challenge of producing clear, artifact-free high-resolution images from limited low-resolution data for applications in image processing, but it appears incremental as it builds on existing interpolation and reconstruction techniques.
The paper tackled the problem of recovering detailed information from low-resolution images to reconstruct high-resolution ones, and the result showed that the proposed method performs better than current single image super-resolution techniques.
Image super-resolution using self-optimizing mask via fractional-order gradient interpolation and reconstruction aims to recover detailed information from low-resolution images and reconstruct them into high-resolution images. Due to the limited amount of data and information retrieved from low-resolution images, it is difficult to restore clear, artifact-free images, while still preserving enough structure of the image such as the texture. This paper presents a new single image super-resolution method which is based on adaptive fractional-order gradient interpolation and reconstruction. The interpolated image gradient via optimal fractional-order gradient is first constructed according to the image similarity and afterwards the minimum energy function is employed to reconstruct the final high-resolution image. Fractional-order gradient based interpolation methods provide an additional degree of freedom which helps optimize the implementation quality due to the fact that an extra free parameter $α$-order is being used. The proposed method is able to produce a rich texture detail while still being able to maintain structural similarity even under large zoom conditions. Experimental results show that the proposed method performs better than current single image super-resolution techniques.