Single image dehazing using improved dark channel prior

Abstract

Huge quantities of suspended particles in our atmosphere, cause scenes to appear hazy or foggy, this reduces visibility of objects and their contrast, and makes detection of objects within the scene more difficult. Most existing algorithms are based on a strong, statistically based prior, the dark channel prior. We introduce an improved Dark Channel Prior method for dehazing image. The transmission map is refined by opening (eroding and dilating) it, thus reducing the halo and block effect.Our method recovers the true radiance of distant objects and the problem of bluishness is reduced.On the basis of color information has been made adaptive.The results show that our method is effective in comparison with other methods.

An Improved Defogging Algorithm Based on Dark Color Theory Combined with Self-Adaptive Threshold Mechanism

“…Currently, most of image defogging algorithms are based on image restoration, the core idea of which is as follows: firstly, an imaging mode should be established; secondly, the degraded part of the imaging model is compensated and the interferential part of it is filtered; thirdly, the clear image is restored [5][6][7][8][9][10][11][12][13][14][15][16][17]. Theoretically, the defogging effect of these algorithms can be ideal; however, most of the existing imaging models are dependent on the image depth information; unfortunately, they cannot be accurately calculated using a single image.…”

“…The defogging algorithm based on DCP is simple and effective; however, the inaccurate estimates of transmission would lead to distortion of the image, such as halo artifacts and overly enhanced restoration in light color areas, and also the optimization algorithm of transmission has high spatial complexity. Therefore, some improved methods have been proposed [9][10][11][12][13][14][15][16][17] based on DCP. Li et al [11] proposed an edge-preserving decomposition method to estimate transmission map.…”