Single Satellite Image Sharpening with Any-angle 2D MTF Estimation

Sharpening a single satellite image remains challenging due to low computational efficiency, complexity of multi-parameters, unphysical modeling, and the potential for radiometric consistency loss. To address these issues, this paper introduces a MTF-based sharpening method that is fast, has a single tunable parameter, and effectively suppresses noise and over-enhancement. This paper also proposes an automatic method for extracting edge objects with any angle for MTF calculation, without relying on ideal edge objects. The improved slanted-edge method is more robust against noise by incorporating the logistic function and employing the Random Sample Consensus (RANSAC) algorithm to remove deflected edges. The new 2D MTF estimation method provides precise and stable sharpening results. This paper extends the proposed method to single image super-resolution (SISR) for satellite images. The proposed approach outperforms state-of-the-art SISR methods, including 11 deep learning-based methods, across three public datasets and raw images (water, city, and building) acquired from three satellites. The utmost correlation to the histogram of raw image proves the proposed method’s superiority in preserving radiometric information compared to other methods. Additionally, the successful application of the one-time estimated 2D MTF for raw satellite images over a year and its capability to improve edge sharpness uniformity across cameras within the sensor system further solidify the method’s universality and reliability. More comparison results and code are available at https://github.com/RSingKK/Any-angle-MTF.