Enhance Low Visibility Image Using Haze-Removal Framework

Abstract

We proposed a novel image enhancement framework to raise the visibility of the image's content. Our primary concern is eliminating haze-like effects and simultaneously increasing images' brightness. Dehazing and luminance enhancement algorithms are considered standard techniques to overcome these issues. However, natural environments usually involve several unfavorable conditions simultaneously, such as insufficient illumination, blur caused by the haze, and color cast resulting from the sun or scattering; this makes dehazing algorithms challenging to overcome environmental issues. Besides, dehazing algorithms sometimes result in artifacts. The proposed framework solves these issues simultaneously by implementing a double-side-enhancement in contrast and brightness based on a new dehazing algorithm. We compare the new dehazing algorithm with others using full-reference benchmarks to ensure performance stability. Afterward, to show the advantage of using the new dehazing algorithm, we evaluate the compatibility between the proposed framework and all dehazing algorithms using non-reference benchmarks. At last, we pair dehazing and luminance enhancement algorithms and compare the combinations with the proposed framework. Eventually, experimental results prove that the new dehazing algorithm outperforms others and is better compatible with the proposed framework. Meanwhile, the proposed framework is superior in contrast and brightness enhancements and outperforms whether single dehazing algorithm or the combinations.

Enhance Dehazed Images Rapidly Without Losing Restoration Accuracy

“…Research in [41] offered an additional enhancement after eliminating the scattered light of underwater images. Recently, more and more research has treated dehazing tasks as enhancements [42]- [44].…”