Singe Image Dehazing With Unsharp Masking and Color Gamut Expansion

Abstract

Image dehazing is a fundamental problem in computer vision and has hitherto engendered prodigious amounts of studies. Recently, with the well-recognized success of deep learning techniques, this field has been dominated by deep dehazing models. However, deep learning is not always a panacea, especially for the practicalities of image dehazing, because high computational complexity, expensive maintenance costs, and high carbon emission are three noticeable problems. Computational efficiency is, therefore, a decisive factor in real-world circumstances. To cope with this growing demand, we propose a linear time algorithm tailored to three primitive parts: unsharp masking (pre-processing), dehazing, and color gamut expansion (post-processing). The first enhances the sharpness according to the local variance of image intensities. The second removes haze based on the improved color attenuation prior, and the third addresses a residual effect of color gamut reduction. Extensive experimental results demonstrated that the proposed method performed comparatively with popular benchmarks, notably deep dehazing models. With such a comparative performance, the proposed method is still fast and efficient, favoring real-world computer vision systems.INDEX TERMS Image dehazing, unsharp masking, color gamut expansion, linear time complexity

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

“…In [36], we demonstrated that the base algorithm achieves performance comparable to data-driven methods, such as those proposed by Cai et al [6] and Ren et al [22], while exhibiting significantly lower computational costs. However, real-time processing requirements through software implementation remain challenging.…”

“…As data-driven methods are not yet ready for widespread deployment, this paper presents an alternative for real-time high-quality single-image dehazing: a symmetric MPSoC-based solution that balances the trade-off between dehazing performance and computational complexity. Building upon our previous work of linear-time single-image dehazing [36], the proposed algorithm incorporates the following features (as illustrated in Figure 1):…”

“…In [36], we presented an O(N) dehazing method, where N denotes the number of image pixels. This method enhances the visibility of hazy images through several steps.…”

“…To address this, a postprocessing step, namely color gamut expansion, is employed to ensure an artifact-free output. Interested readers are referred to [36] for a more comprehensive description.…”

“…With its efficacy confirmed, the next section will introduce a corresponding hardware accelerator to enhance its practical usability. Previous studies [32,36] have reported that software implementations of dehazing algorithms are unable to achieve a processing speed of at least 25 fps, failing to meet real-time processing requirements. This observation underscores the critical need for hardware implementation.…”