Jointly Adversarial Network to Wavelength Compensation and Dehazing of Underwater Images

Abstract

Severe color casts, low contrast and blurriness of underwater images caused by light absorption and scattering result in a difficult task for exploring underwater environments. Different from most of previous underwater image enhancement methods that compute light attenuation along object-camera path through hazy image formation model, we propose a novel jointly wavelength compensation and dehazing network (JWCDN) that takes into account the wavelength attenuation along surfaceobject path and the scattering along object-camera path simultaneously. By embedding a simplified underwater formation model into generative adversarial network, we can jointly estimates the transmission map, wavelength attenuation and background light via different network modules, and uses the simplified underwater image formation model to recover degraded underwater images. Especially, a multi-scale densely connected encoderdecoder network is proposed to leverage features from multiple layers for estimating the transmission map. To further improve the recovered image, we use an edge preserving network module to enhance the detail of the recovered image. Moreover, to train the proposed network, we propose a novel underwater image synthesis method that generates underwater images with inherent optical properties of different water types. The synthesis method can simulate the color, contrast and blurriness appearance of real-world underwater environments simultaneously. Extensive experiments on synthetic and real-world underwater images demonstrate that the proposed method yields comparable or better results on both subjective and objective assessments, compared with several state-of-the-art methods.

Underwater Image Enhancement Using Dual Adversarial Network

“…By comparing the 1st, 2nd and 3rd rows of Fig. 4, it can be clearly observed that the synthesized images based on [25] and [26] cannot simulate the characteristics of real underwater images well, especially in color casts. In our opinion, this is mainly caused by incorrect ambient light settings.…”

“…Underwater image synthesis is usually solved from two different perspectives: based on Generative Adversarial Networks (GAN) [23], [24] and physical models [25], [26]. Li et al [23] propose a deep model, named WaterGAN, to generate underwater-like images from in-air images and depth maps in an unsupervised manner.…”

“…Li et al [25] establish an underwater synthetic image database via a simplified physical model, including ten subsets for different water types. Ding et al [26] modify the work of [25] by introducing the color shift and object blurriness effect, producing relatively good synthetic data. Nevertheless, the homogeneous ambient light is usually randomly generated in the process of synthesis, which makes a large difference between the synthetic and real underwater images.…”

“…Li et al [25] synthesize the first underwater-like dataset based on Eq.1. Ding et al [26] introduce the color component to improve the work of [25]. J c (x) is redefined as the scene irradiance at point x, which is closer to the characteristic of light propagation in the water.…”

“…Ding et al [26] establish a new underwater-like dataset based on Eq.3 by multiple parameters including absorption coefficient a c (λ), scattering coefficient b c (λ), object-camera distance d(x), surface-object distance D(x) and ambient light B ∞ c . They deem that low visibility and color casts are key degradation parameters for image quality and ignore the effect of ambient light.…”