A Fast Semi-inverse Approach to Detect and Remove the Haze from a Single Image

Abstract

Abstract. In this paper we introduce a novel approach to restore a single image degraded by atmospheric phenomena such as fog or haze. The presented algorithm allows for fast identification of hazy regions of an image, without making use of expensive optimization and refinement procedures. By applying a single per pixel operation on the original image, we produce a 'semi-inverse' of the image. Based on the hue disparity between the original image and its semi-inverse, we are then able to identify hazy regions on a per pixel basis. This enables for a simple estimation of the airlight constant and the transmission map. Our approach is based on an extensive study on a large data set of images, and validated based on a metric that measures the contrast but also the structural changes. The algorithm is straightforward and performs faster than existing strategies while yielding comparative and even better results. We also provide a comparative evaluation against other recent single image dehazing methods, demonstrating the efficiency and utility of our approach.

Image Haze Removal Using Dark Channel Prior and Inverse Image

“…In [15], Ancuti et al applied semi-inverse image to image haze removal. The semi-inverse image can be obtained by replacing the RGB values of each pixel on a per channel basis by the maximum of the initial channel value and its inverse.…”

“…Besides Equation (6) and Equation (7), he also added the information loss cost. In [16], Tang et al proposed similar constraints to [15]. …”

“…The three works [6], [15]- [16] told us that inverse image is very useful for haze removal. But, only it can not deal with all cases.…”

Image Enhancement Focusing on Hazy and Non-uniform Illumination Images

“…He et al [4] proposed a method based on dark channel prior and the airlight map is estimated using dark channel prior and refined by soft matting. Ancuti et al [6] significantly reduced the complexity of He's algorithm by modifying the block-based approach to a layerbased one. In addition, He et al's algorithm has been adopted and improved by many researchers [7,8,9,10].…”

Visibility in underwater robotics: Benchmarking and single image dehazing

“…Similarly, [Ancuti et al, 2011] relies on the same assumption as the DCP but does a different processing. A semiinverse transformation is used to distinguish haze free regions from sky or haze areas in a per pixel basis that makes it suitable for parallelization.…”