Year: 2026
Type: article
Abstract: Cloud cover can substantially restrict the information available in optical satellite images, affecting applications including agricultural monitoring, disaster assessment, and urban planning.Although deeplearning and multi-temporal techniques have improved cloud mitigation, they commonly depend on considerable computational resources, training data, or repeated observations.This study presents a classical image-processing framework for identifying cloud-covered regions and restoring the affected image areas.The proposed method uses CLAHE (Contrast Limited Adaptive Histogram Equalization)-based contrast enhancement, HSV (Hue, Saturation, Value)-based masking, connected component analysis, and a two-step inpainting algorithm.The proposed method is evaluated on the RICE dataset using mean squared error, root mean square error, peak signal-to-noise ratio, structural similarity index measure, accuracy, and area under the receiver operating characteristic curve.The resulting framework provided effective cloud detection while retaining a relatively lightweight computational requirement.Although the reconstruction quality remains lower than that of recent deep-learning approaches, the method provides an efficient baseline for resourceconstrained environments where computational efficiency is prioritized.
Institution: MIT World Peace University
Language: English
Topic: Image Enhancement Techniques
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid:
OpenAlex ID: W7204947203