Image Defogging Framework Using Segmentation and the Dark Channel Prior

Abstract

Foggy images suffer from low contrast and poor visibility problem along with little color information of the scene. It is imperative to remove fog from images as a pre-processing step in computer vision. The Dark Channel Prior (DCP) technique is a very promising defogging technique due to excellent restoring results for images containing no homogeneous region. However, having a large homogeneous region such as sky region, the restored images suffer from color distortion and block effects. Thus, to overcome the limitation of DCP method, we introduce a framework which is based on sky and non-sky region segmentation and restoring sky and non-sky parts separately. Here, isolation of the sky and non-sky part is done by using a binary mask formulated by floodfill algorithm. The foggy sky part is restored by using Contrast Limited Adaptive Histogram Equalization (CLAHE) and non-sky part by modified DCP. The restored parts are blended together for the resultant image. The proposed method is evaluated using both synthetic and real world foggy images against state of the art techniques. The experimental result shows that our proposed method provides better entropy value than other stated techniques along with have better natural visual effects while consuming much lower processing time.

[Retracted] Research on Haze Image Enhancement based on Dark Channel Prior Algorithm in Machine Vision

“…The problem of transmission estimation is transformed into the estimation of minimum color channel difference between blurred image and nonblurred image, and a nonlinear model is proposed to estimate the bound function, so as to realize the accurate estimation of transmission. Anan et al [ 5 ] proposed a framework based on the segmentation of sky and nonsky regions to restore the sky and nonsky parts, respectively. The sky part is restored by contrast limited adaptive histogram equalization (CLAHE) method, and the nonsky part is restored by improved DCP method and fused to get the final image.…”

A Novel Power-Saving Reversing Camera System with Artificial Intelligence Object Detection

“…The reversing image obscured or polluted by weather conditions such as raindrops or haze will affect the accuracy of reversing driving. Therefore, many scholars are committed to image restoration processing technology to overcome the image degradation caused by rain [9][10][11][12][13][14][15] or fog [16][17][18][19][20][21][22][23][24] problem. In 2014, Shaodi You et al proposed an idea which is to use long-range trajectories to discover the motion and appearance features of raindrops locally along the trajectories to detect raindrops and to utilize patches indicated to remove adherent raindrops [9].…”

“…In 2020, Gao Tao et al proposed a novel defogging method that overcomes some limitations including imprecise estimation of atmospheric light, color distortion by both defining the more accurate atmospheric light by introducing the adaptive variable strategy and fusing dark channel and light channel to estimate more precise atmospheric light and transmittance [21]. In 2021, Sabiha Anan et al proposed a framework which is using a binary mask formulated to do segmentation of the sky and non-sky region by flood fill algorithm, and is using Contrast Limited Adaptive Histogram Equalization (CLAHE) and modified Dark Channel Prior (DCP) to restore the foggy sky and non-sky parts, respectively [22]. In 2021, Gabriele Graffieti and Davide Maltoni proposed a novel defogging technique which has a curriculum learning strategy and an enhanced CycleGAN model, named CurL-Defog, to reduce the number of produced artifacts, with the aim of minimizing the insertion of artifacts while maintaining good contrast restoration and visibility enhancement [23].…”

Region Adaptive Single Image Dehazing

“…In order to improve computational efficiency, other approaches such as bilateral filtering [39], median filtering [40,41], edgepreserving filtering [42], and guided filtering [43] are used to optimize the transmission instead of soft matting. To reduce the distortions in a large area of the sky or a bright white object where the dark channel prior is invalid, sky detection-based methods [44][45][46][47][48][49] and white object detection-based method are proposed [50]. Jackson et al [51] estimated the initial transmission map by modeling the scattering coefficients using Rayleigh scattering theory and dark channel prior.…”

“…Salazar-Colores et al [64] used local Shannon entropy to detect and segment a sky region in order to reduce artifacts and improve image recovery of the sky region. Image entropy was used as a qualitative metric to evaluate the quality of dehazed images in [49,65].…”