Single Satellite Image Sharpening With Any-Angle 2-D MTF Estimation

Abstract

Sharpening a single satellite image remains challenging due to low computational efficiency, complexity of multiparameters, unphysical modeling, and the potential for radiometric consistency loss. To address these issues, this article introduces a modulation transfer function (MTF)-based sharpening method that is fast, has a single tunable parameter, and effectively suppresses noise and over-enhancement. This article 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 2-D MTF estimation method provides precise and stable sharpening results. This article 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. In addition, the successful application of the one-time estimated 2-D 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 .