Ms. Soo Mee Wong Profile

Soo Mee Wong

at Univ of Malaya

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Publications (2)

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SPIE Journal Paper | 8 July 2020

Regularization-based modulation transfer function compensation for optical satellite image restoration using joint statistical model in curvelet domain

Soo Mee Wong, Simying Ong, Chee Seng Chan

JARS, Vol. 14, Issue 03, 036506, (July 2020) https://doi.org/10.1117/12.10.1117/1.JRS.14.036506

KEYWORDS: Image restoration, Modulation transfer functions, Image quality, Image processing, Earth observing sensors, Satellites, Satellite imaging, Visualization, Signal to noise ratio

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The heavy-tailed properties of modulation transfer function (MTF) typically introduce noise and an aliasing effect in the MTF compensation (MTFC) effort to improve the spatial quality of optical satellite images. These degradative effects compromise the image’s signal-to-noise ratio (SNR). Consequently, users must evaluate the relative importance of image sharpness versus SNR for their applications to decide whether MTFC processing is appropriate. We propose a high-fidelity MTFC method that executes an optimal trade-off between noise regularization and detail preservation in the image restoration process to address this problem. To this end, we exploit the merit of image prior characteristics in both local smoothness and nonlocal self-similarity properties of an image in a hybrid domain (viz., space spatial and frequency) to design effective regularization terms that reflect these image properties. Furthermore, we establish a simple joint statistical model in the curvelet domain to combine these two properties. To make this regularization-based MTFC method tractable and robust, we employ the multiobjective bilevel optimization approach to solve the severely ill-posed inverse problem of MTFC. We conduct extensive experiments to evaluate the proposed regularization-based MTFC method using synthetically blurred images simulated from the level 2A product of IKONOS and real satellite images with unknown blur. Quantitative measurements of image quality reveal that the proposed method produces competitive restoration results with minimum computational complexity and exhibits a good convergence property. These experimental results show that the proposed method can find a compromise between regularizing noise and preserving image fidelity.

SPIE Journal Paper | 24 January 2019

On-orbit spatial characterization based on satellite image structure

Soo Mee Wong, Simying Ong, Chee Seng Chan

JARS, Vol. 13, Issue 01, 016510, (January 2019) https://doi.org/10.1117/12.10.1117/1.JRS.13.016510

KEYWORDS: Modulation transfer functions, Point spread functions, Earth observing sensors, Satellites, Satellite imaging, Image processing, Imaging systems, Image segmentation, Error analysis, Remote sensing

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One practical way to characterize the spatial performance of an in-flight satellite optical imaging system is to determine the modulation transfer function (MTF) from its remotely sensed images on the ground. Currently, there are many MTF measurement techniques designed to provide accurate measurements for high-resolution imaging systems. However, these existing techniques usually rely on the presence and manual identification of a well-separated characteristics target. We introduce an insight to effectively estimate a degradation function based on the MTF by analyzing the nonlocal self-similarity characteristics, namely the structural component, in the observed image. To this end, this paper presents several strategies to realize the aim of this work. First, we propose a segmentation method to select the ideal candidates for MTF estimation. Second, we develop an adaptive structure selection method that removes detrimental structures and selects useful information for point spread function (PSF) estimation. Finally, we put forward a robust estimation method by introducing a spatial prior that is able to simultaneously suppress noises while preserving the sparsity and continuity of the PSF kernels. For the experiments, datasets used in the analyses are synthetically blurred images simulated from level 2A product of IKONOS and real remotely sensed images from level 0 product of RazakSAT. The experimental results demonstrate that the proposed method is practical and effective, with <2.3 % of relative error at the Nyquist frequency as compared to the well-established edge method. This indicates that the proposed method can warrant a reliable result for on-orbit spatial characterization.

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