Image dehazing based on microscanning approach

Sergei Voronin; Artyom Makovetskii; Vitaly Kober; Aleksei Voronin; Tatyana Makovetskaya

doi:10.1117/12.2568946

21 August 2020 Image dehazing based on microscanning approach

Sergei Voronin, Artyom Makovetskii, Vitaly Kober, Aleksei Voronin, Tatyana Makovetskaya

Proceedings Volume 11510, Applications of Digital Image Processing XLIII; 115102X (2020) https://doi.org/10.1117/12.2568946
Event: SPIE Optical Engineering + Applications, 2020, Online Only

Abstract

Over the past two decades, methods have been proposed for deaerating images, and most of them use a method of improving or restoring images. An image without haze should have a higher contrast than the original hazed image. It is possible remove haze by increasing the local contrast of the restored image. Some haze removal approaches estimate a hazed image from the observed hazed scene by solving an objective function whose parameters are adapted to the local statistics of the hazed image inside a moving window. Common image dehazing techniques use only one observed image for processing. Various variants of local adaptive algorithms for single image dehazing are known. A dehazing method based on spatially displaced sensors is also described. In this presentation, we propose a new dehazing algorithm that uses several scene images. Using a set of observed images, the dehazing of the image is carried out by solving a system of equations, which is derived from the optimization of the objective function. These images are made in such a way that they are spatially offset relative to each other and made in different time. Computer simulation results of are presented to illustrate the performance of the proposed algorithm for the restoration of hazed images.

Citation Download Citation

Sergei Voronin, Artyom Makovetskii, Vitaly Kober, Aleksei Voronin, and Tatyana Makovetskaya "Image dehazing based on microscanning approach", Proc. SPIE 11510, Applications of Digital Image Processing XLIII, 115102X (21 August 2020); https://doi.org/10.1117/12.2568946

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