Automating a Dehazing System by Self-Calibrating on Haze Conditions

Abstract

Existing image dehazing algorithms typically rely on a two-stage procedure. The medium transmittance and lightness are estimated in the first stage, and the scene radiance is recovered in the second by applying the simplified Koschmieder model. However, this type of unconstrained dehazing is only applicable to hazy images, and leads to untoward artifacts in haze-free images. Moreover, no algorithm that can automatically detect the haze density and perform dehazing on an arbitrary image has been reported in the literature to date. Therefore, this paper presents an automated dehazing system capable of producing satisfactory results regardless of the presence of haze. In the proposed system, the input image simultaneously undergoes multiscale fusion-based dehazing and haze-density-estimating processes. A subsequent image blending step then judiciously combines the dehazed result with the original input based on the estimated haze density. Finally, tone remapping post-processes the blended result to satisfactorily restore the scene radiance quality. The self-calibration capability on haze conditions lies in using haze density estimate to jointly guide image blending and tone remapping processes. We performed extensive experiments to demonstrate the superiority of the proposed system over state-of-the-art benchmark methods.

Attention Optimized Deep Generative Adversarial Network for Removing Uneven Dense Haze

“…parameters, and scene depth can be correctly calculated, we can directly restore the hazefree image from a hazy input. Based on the atmospheric scattering model, a lot of early traditional dehazing algorithms [5][6][7][8][9][10][11][12][13][14][15] are proposed. Among them, dark channel prior (DCP) [5] is the most successful traditional dehazing algorithm.…”

“…Based on the atmospheric scattering model, a lot of early traditional dehazing algorithms [5][6][7][8][9][10][11][12][13][14][15] are proposed. Among them, dark channel prior (DCP) [5] is the most successful traditional dehazing algorithm.…”

A Symmetric Multiprocessor System-on-a-Chip-Based Solution for Real-Time Image Dehazing

“…It features a densely hazy image from the IVC dataset [39] and showcases two dehazing results obtained using the two weights, respectively. Parameters ρ 1 and ρ 2 are set to 0.8811 and 0.9344, as described in [40], and W is fixed at 1.2. Subjective evaluation shows that the dehazing result in Figure 2d is less favorable compared to the result with our proposed weight in Figure 2e.…”