Enhanced Single Image Dehazing Technique based on HSV Color Space

Abstract

The clarity of images degrades significantly due to the impact of weather conditions such as fog and haze. Persistent particles scatter light, attenuating reflected light from the scene, and the dispersed atmospheric light will mix with the light received by the camera affecting image contrast in both outdoor and indoor images. Conventionally, the atmospheric scattering model (ATSM) is a model often used to recover hazy images. In ATSM, two unknown factors/parameters must be estimated: Airlight and scene transmission. The accuracy of these estimations has a significant influence on the dehazed image quality. This paper focuses on the first parameter. It introduces a new technique for estimating the airlight based on the HSV color space. The HSV color space is utilized to identify the haziest opaque area in the image. Consequently, the amount of airlight in the selected area is calculated. To assess the effectiveness of the suggested approach, the well-known dataset, RESIDE SOTS, has been used that contains two parts; namely, SOTS-indoor and SOTS-outdoor. Each of dataset includes 500 images. Experimental findings show that the suggested approach outperforms the existing techniques in terms of peak signal-to-noise-ratio and structural similarity index`.

Research on the teaching innovation path of art design based on big data technology

“…1(a). In the field of design, the artist takes the traditional standard colors as a benchmark and adds white to dilute its concentration, i.e., changes the saturation of the color [15]. Addition of black to reduce the intensity of its reflection, that is, to change the brightness of the color so as to modulate the desired color.…”