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Comparative study of image denoising algorithms for power spectrum preservation in space mission planetary imagery

Authors: Snijders Trullàs, Andreu;

Comparative study of image denoising algorithms for power spectrum preservation in space mission planetary imagery

Abstract

This thesis presents a comparative study of image denoising algorithms with a focus on preserving the power spectral density (PSD) of planetary imagery, in the context of the Cassini-Huygens space mission. A detailed analysis of noise characteristics was carried out on real calibrated Cassini images to determine their statistical distribution. The results confirmed that the residual noise, after standard processing by NASA’s CISSCAL pipeline, follows a Gaussian distribution with stable variance. Based on this analysis, synthetic noise was generated and added to a realistic atmospheric simulation of Saturn. This allowed the creation of controlled clean/noisy image pairs, enabling quantitative benchmarking of five denoising algorithms: Wiener filtering, Starlet wavelet transform, Non-Local Means, BM3D, and Total Variation. Evaluation was based on classical full-reference metrics and novel PSD-based indicators.

Este trabajo presenta un estudio comparativo de algoritmos de eliminación de ruido con énfasis en la preservación de la densidad espectral de potencia (PSD) en imágenes planetarias, en el contexto de la misión espacial Cassini-Huygens. Se realizó un análisis estadístico detallado del ruido presente en imágenes reales calibradas de Cassini, concluyendo que el ruido residual, tras la calibración estándar CISSCAL de la NASA, sigue una distribución gaussiana con varianza estable. A partir de esta caracterización, se generó ruido sintético y se aplicó a una simulación atmosférica realista de Saturno, creando pares de imágenes limpia/ruidosa que permiten una evaluación cuantitativa precisa. Se compararon cinco algoritmos: filtrado de Wiener, transformada Starlet, Non-Local Means, BM3D y variación total, utilizando tanto métricas clásicas como indicadores espectrales basados en PSD.

Aquest treball presenta un estudi comparatiu d’algoritmes de reducció de soroll amb especial atenció a la preservació de la densitat espectral de potència (PSD) en imatges planetàries, dins el marc de la missió Cassini-Huygens. Primer es va fer una caracterització detallada del soroll present a les imatges reals calibrades de Cassini, confirmant que, un cop processades amb la pipeline oficial de la NASA (CISSCAL), el soroll residual segueix una distribució gaussiana amb variància estable. A partir d’aquestes mesures es va simular soroll sintètic, aplicant-lo sobre una simulació atmosfèrica realista de Saturn per generar parelles d’imatges netes i amb soroll. Aquest conjunt permet comparar quantitativament cinc algoritmes: Wiener, Starlet, Non-Local Means, BM3D i variació total, mitjançant mètriques clàssiques i indicadors espectrals basats en PSD.

Related Organizations
Keywords

Signal processing, Cassini mission, Computer algorithms, Gaussian noise, Denoising algorithms, Spectral preservation, Power spectral density, Signal processing--Digital techniques, Image denoising, Noise control, Planetary imagery, Algorismes computacionals, Tractament del senyal--Tècniques digitals, Soroll--Control, Scientific imaging, Simulation

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average