Title: SenseNet: satellite image enhancement using optimised deep denoiser for cloud removal

Authors: Renuka Sandeep Gound; Sudeep D. Thepade

Addresses: PCET's Nutan Maharashtra Institute of Engineering and Technology, Pune, 410507, India ' PCET's Pimpri Chinchwad University, Pune, 412106, India

Abstract: The research focuses on devising a hybrid coyote fox optimisation (CFO) enabled deep denoiser model to eliminate the blurring edges of cloud-covered boundaries in the RS image. The need for reconstructing the high-quality satellite image is elaborated in this research article for which a proposed hybrid CFO deep denoiser is developed. The optimised learning of deep denoiser increases the reconstruction ability, which is the main focus of the research. The satellite images are pre-processed and exposed to the reconstruction in such a way that the proposed hybrid CFO deep denoiser reconstructs the high-quality satellite image without the influence of the cloud. The experimental results also demonstrate that the CFO-based deep denoiser exhibits higher performance in terms of PSNR, SSIM, and MSE when compared with the existing denoiser. The performance improvement of 2.165 dB, 1.436%, and 0.816% is obtained by the hybrid CFO-deep denoiser concerning existing denoisers in terms of PSNR by maintaining the K-fold at 5.

Keywords: image enhancement; satellite image; deep learning; coyote fox optimisation; CFO; cloud removal.

DOI: 10.1504/IJBIC.2026.151783

International Journal of Bio-Inspired Computation, 2026 Vol.27 No.1, pp.45 - 59

Received: 23 May 2024
Accepted: 30 Oct 2024
Published online: 19 Feb 2026 *

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