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Joint Image De-noising and Enhancement for Satellite-Based SAR

Shahrokh Hamidi

TL;DR

This work targets the dual SAR image issues of speckle noise and low contrast by proposing a unified pipeline that combines histogram-based median filtering with CLAHE-based contrast enhancement within the omega-k SAR reconstruction framework. The approach leverages a fast histogram-based median filter and blockwise CLAHE to achieve denoising and contrast improvement in a single pass, reducing computational load relative to separate steps. Validation on ERS-2 strip-map data from the ASF demonstrates improved detail and contrast, with Doppler centroid estimates around $f_{dc} \approx -172.5$ Hz and a small squint angle (~$-0.038^\circ$), and a PSNR peak near 35.6 dB for $p=q=16$. The results support the method’s potential for efficient, high-quality satellite SAR imaging under all-weather conditions.

Abstract

The reconstructed images from the Synthetic Aperture Radar (SAR) data suffer from multiplicative noise as well as low contrast level. These two factors impact the quality of the SAR images significantly and prevent any attempt to extract valuable information from the processed data. The necessity for mitigating these effects in the field of SAR imaging is of high importance. Therefore, in this paper, we address the aforementioned issues and propose a technique to handle these shortcomings simultaneously. In fact, we combine the de-noising and contrast enhancement processes into a unified algorithm. The image enhancement is performed based on the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique. The verification of the proposed algorithm is performed by experimental results based on the data that has been collected from the European Space Agency's ERS-2 satellite which operates in strip-map mode.

Joint Image De-noising and Enhancement for Satellite-Based SAR

TL;DR

This work targets the dual SAR image issues of speckle noise and low contrast by proposing a unified pipeline that combines histogram-based median filtering with CLAHE-based contrast enhancement within the omega-k SAR reconstruction framework. The approach leverages a fast histogram-based median filter and blockwise CLAHE to achieve denoising and contrast improvement in a single pass, reducing computational load relative to separate steps. Validation on ERS-2 strip-map data from the ASF demonstrates improved detail and contrast, with Doppler centroid estimates around Hz and a small squint angle (~), and a PSNR peak near 35.6 dB for . The results support the method’s potential for efficient, high-quality satellite SAR imaging under all-weather conditions.

Abstract

The reconstructed images from the Synthetic Aperture Radar (SAR) data suffer from multiplicative noise as well as low contrast level. These two factors impact the quality of the SAR images significantly and prevent any attempt to extract valuable information from the processed data. The necessity for mitigating these effects in the field of SAR imaging is of high importance. Therefore, in this paper, we address the aforementioned issues and propose a technique to handle these shortcomings simultaneously. In fact, we combine the de-noising and contrast enhancement processes into a unified algorithm. The image enhancement is performed based on the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique. The verification of the proposed algorithm is performed by experimental results based on the data that has been collected from the European Space Agency's ERS-2 satellite which operates in strip-map mode.
Paper Structure (6 sections, 7 equations, 11 figures, 1 table)

This paper contains 6 sections, 7 equations, 11 figures, 1 table.

Table of Contents

  1. Introduction
  2. Model Geometry
  3. $\omega-k$ Algorithm
  4. Image De-nosing and Enhancement
  5. Experimental Results
  6. conclusion

Figures (11)

  • Figure 1: The geometry of the model which shows a satellite-based SAR system in strip-map mode with non-zero squint angle.
  • Figure 2: The image presents the absolute value of the raw data which has been collected from the satellite. The data is in In-phase and Quadrature format.
  • Figure 3: The result for the amplitude-based Doppler centroid frequency estimation. The estimated value is $\rm f_{dc} = -169 \; Hz$. The estimation is based on averaging over all 4912 range cells.
  • Figure 4: The result for the phase-based Doppler centroid frequency estimation using ACCC method. The estimated value is $\rm f_{dc} = -176 \; Hz$. The estimation is based on averaging over all 4912 range cells.
  • Figure 5: The reconstructed image from the raw data, presented in Fig. \ref{['fig:img_raw']}, based on the $\omega-k$ algorithm.
  • ...and 6 more figures