Adaptive Multi-Scale Entropy Fusion De-Hazing Based on Fractional Order

Uche Nnolim

doi:10.3390/jimaging4090108

Abstract

This paper describes a proposed fractional filter-based multi-scale underwater and hazy image enhancement algorithm. The proposed system combines a modified global contrast operator with fractional order-based multi-scale filters used to generate several images, which are fused based on entropy and standard deviation. The multi-scale-global enhancement technique enables fully adaptive and controlled color correction and contrast enhancement without over exposure of highlights when processing hazy and underwater images. This in addition to the illumination/reflectance estimation coupled with global and local contrast enhancement. The proposed algorithm is also compared with the most recent available state-of-the-art multi-scale fusion de-hazing algorithm. Experimental comparisons indicate that the proposed approach yields a better edge and contrast enhancement results without a halo effect, without color degradation, and is faster and more adaptive than all other algorithms from the literature.

Highlights

Hazy and underwater images share similar characteristics in terms of reduced visibility and low contrast due to the nature of image formation [1,2]
We present the development of the multi-scale algorithm for local contrast enhancement, which replaces the contrast limited adaptive histogram equalization (CLAHE) used in previous work by drastically reducing complexity and run-time
We investigated the use of CLAHE to improve local contrast, which results in drastic improvements

Summary

Introduction

Hazy and underwater images share similar characteristics in terms of reduced visibility and low contrast due to the nature of image formation [1,2]. Several single image-based enhancement and restoration models and algorithms have been proposed to solve this problem [1,2]. They work with varying degrees of success at the cost of increased structural and computational complexity. Color correction combined with these highly complex de-hazing algorithms have been used to restore underwater images. There are relatively few digital hardware realizations and reduced real-time prospects for such schemes due to a high computational cost. We propose a fractional order-based algorithm for the enhancement of hazy and underwater images. The algorithm performs color correction and multiscale spatial filter-based localized enhancement. We compare results with other algorithms from the literature and show that the proposed system is effective with the fastest execution time

Methods

Results

Discussion

Conclusion