Abstract
Haze reduces the contrast of an image and causes the loss in colors, which has a negative effect on the subsequent object detection; therefore, single image dehazing is a challenging visual task. In addition, defects exist in previous existing dehazing approaches: Pixel-based dehazing approaches are likely to result in insufficient information to estimate the transmission, whereas patch-based ones are prone to generate shadows. They both also tend to induce color deviations. Therefore, this study proposes a novel method based on multi-scale wavelet and non-local dehazing. A hazy image is first decomposed into a low-frequency and three high-frequency sub-images by wavelet transform. Non-local dehazing and wavelet denoising are then employed on the low-frequency and high-frequency sub-images to remove the haze and noise, respectively. Finally, a haze-free image is obtained from the reconstruction of sub-images. The proposed method focuses on the dehazing and denoising on the low-frequency and high-frequency images respectively, through which the details on the image can be well preserved. Experimental results indicate that the proposed method is superior to the state-of-the-art approaches in both quantitative and qualitative evaluation on the synthetic and real-world image datasets.
Highlights
As a challenging and improperly posed problem, dehazing has attracted wide attention in the field of image processing in recent years
This study proposes a novel method based on multi-scale wavelet and non-local dehazing
The contributions of this study are summarized: This study proposes a novel method that combines multi-resolution wavelet transform with a non-local prior image dehazing method to effectively achieve haze removal of images
Summary
As a challenging and improperly posed problem, dehazing has attracted wide attention in the field of image processing in recent years. Limited by poor weather and lighting conditions, such as fog, smog and other human factors, the visibility of images is noticeably reduced and the image quality from cameras is greatly decreased, which severely hindered the execution and application of computer vision programs. In recent years, image dehazing techniques have developed rapidly, which can greatly eliminate poor quality images and effectively help to restore hazy images. Air is filled with suspended particles while being affected by scattered light in the environment, which in turn reduces the quality of the image taken. The combination coefficient of this model is transmittance which is affected by the distance between the object and camera. The longer distance the light travels, the greater the light is affected by scattered medium. The transmittance becomes smaller under the circumstance of a smaller effect on light from scattered medium
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