Abstract
Remotely sensed images are widely used in many imaging applications. Images captured under adverse atmospheric conditions lead to degraded images that are contrast deficient and noisy. This study is intended to address these defects of remotely sensed data efficiently. A perceptually inspired variational model is designed based upon the Bayesian framework, powered by the retinex theory. The atmospheric noise or the shot noise (precisely following a Poisson distribution) and contrast inhomogeneity are addressed in this article. The model thus designed is tested and verified both visually and quantitatively using various test data under different statistical measures. The comparative study reveals the efficiency of the model.
Highlights
M ONITORING of natural phenomena, mapping of land cover, meteorological studies, military surveillance, etc. actively use remote sensing images to give real-time updates
A further improvement can be noticed in the work [8], where the authors propose a histogram compacting transform along with a linear stretch to enhance the contrast of remote-sensed data
The authors of this work assume that the reflectance belongs to a space of bounded variation (BV) in which the total variation (TV) are bounded
Summary
M ONITORING of natural phenomena, mapping of land cover, meteorological studies, military surveillance, etc. actively use remote sensing images to give real-time updates. This issue is addressed to a considerable extent in brightness-preserving dynamic HE [3] and histogram modification framework [4] Another spatial domain model has been proposed for contrast enhancement. This model uses an adaptive gamma correction with weighting distribution (AGCWD) [5] They tend to neglect the local image features while considering the global aspect of the data. A method using discrete wavelet transform (DWT) and adaptive intensity transformation is introduced for remote-sensed image enhancement in [9]. A further improvement can be noticed in the work [8], where the authors propose a histogram compacting transform along with a linear stretch to enhance the contrast of remote-sensed data. The variational framework being an effective strategy to solve the inverse problems and analyze them from a theoretical perspective, we resolve to explore a variational retinex framework based on the perceptual model for enhancing and restoring images
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