Dr. Arpan Pal Profile

Dr. Arpan Pal

at Tata Consultancy Services Ltd

SPIE Involvement:

Author

Publications (1)

Proceedings Article | 19 October 2023 Poster + Paper

Image restoration techniques for space-based lightweight optically sparse aperture Earth-observation telescopes in the longwave infrared domain

Avyarthana Ghosh, Pavan Reddy, Achanna Anil Kumar, Balamuralidhar P., Arpan Pal

Proceedings Volume 12733, 1273316 (2023) https://doi.org/10.1117/12.2687127

KEYWORDS: Image restoration, Imaging systems, Image filtering, Segmented mirrors, Convolutional neural networks, Point spread functions, Remote sensing, Lightweight mirrors

Read Abstract +

Any high spatial resolution space-borne electro-optical sensing system operating in long wavelengths, like Earth-observation facilities operating in the longwave infrared are subjected to an inherent design and implementation challenge of deploying large monolithic primary aperture mirrors, to achieve a ground resolution distance of a few tens of cm. To outflank this issue, many present-date missions design and commission lightweight segmented mirrors, mostly with equal sized sub-apertures. One step ahead, these sub-apertures could be of particular non-uniform size distributions (One-by-Three, Taylor-ln and Taylor-invtan), thereby ensuring a smaller and even lighter primary and with marginal compromise in imaging quality due to significant sidelobe suppression. This is also confirmed by the fact that these particular non-uniform sized mirrors have very less loss of spatial frequencies with respect to that of equal-sized segmented mirrors. Therefore, under lossless conditions, there is hardly any degradation in imaging performance of these two configurations. However, in the presence of gaussian, impulse and shot noise, the situation worsens because of the compromised collecting area as well as noise contribution. A simple deconvolution technique for image restoration in presence of noise is no longer possible because of the lack of convergence. This calls upon for the use of iterative reconstruction algorithms with denoisers like Total Variation (TV), Block Matching and 3D Filtering (BM3D) or Convolutional Neural Networks (CNN) in the post-processing step to ensure better output images with high Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) along with good edge and texture preservation of the features. A comparison of these three kinds of denoisers, TV, BM3D and DnCNN implemented as a part of the Alternating Directions Method of Multipliers (ADMM) reconstruction technique is presented in this work. It is seen that in presence of some shot noise, random gaussian noise with σ= 0.03 and some impulse noise, the best performance is achieved for ADMM-BM3D technique with comparable performance from the ADMM-DnCNN method (except for Taylor-ln design). On the contrary, denoising with TV can perform well only in presence of shot noise. Additionally, this technique is nearly rejected for use in case of the Taylor-invtan model because of extremely low SSIM when all three noise types are incorporated.

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years