A Generalized Enhancement Framework for Hazy Images With Complex Illumination

Images captured under low-light conditions are generally characterized by poor illumination, low contrast, and nonignorable large amount of noise. In order to improve the visibility in weak illumination scenes, multiple artificial light sources are used, which leads to severe uneven illumination of the scene. The main challenges of dehazing images with complex illumination are to suppress the boosting of unsightly noise when enhancing contrast and avoid overenhancement in bright glow regions. To circumvent problems above, this letter proposes a generalized enhancement framework, which works well not only in uniform light conditions but also in strongly nonuniform illumination low-light scenes. To achieve this, we first decompose the input hazy image into a structure layer containing low-frequency illumination variance and a texture layer containing large amount of high-frequency details. Sequentially, benefit from two derived masks that are intrinsically similar to weight maps, the proposed framework can perform regional adaptive brightness adjustment on the structure layer according to the distribution of light in the input image. Meanwhile, regions of effective details in the texture layer are assigned higher weights, while regions that belong to noise are suppressed. Finally, adding the enhanced texture layer back to the brightened structure layer, visually appealing results are generated. Experimental results on various scenarios demonstrate the superiority of the proposed framework over state-of-the-art methods in terms of both qualitative and quantitative.

I. Introduction

As common weather phenomenon, haze and fog have brought serious negative influence to many vision-based techniques, such as tracking, video surveillance, and remote sensing missions [1]–[3], as shown in Fig. 1(a). When it comes to nighttime or deep sea, the problem becomes worse. Hazy images captured under low-light conditions are often characterized by poor illumination, low contrast, and obvious noise. Meanwhile, multiple artificial light sources cause a strong nonuniform illumination of the scene. These combined challenges make state-of-the-art dehazing techniques suffering from important limitations, say relatively bright regions and large amount of noise will be obviously overenhanced and wash out corresponding details. Fig. 1.

Application of the proposed framework in dehazing remote sensing images with complex illumination. (a) Input remote sensing images. (b) Results generated by the proposed method.