Image dehazing method based on dark channel prior and interval interpolation wavelet transform

Abstract: Nowadays, smart agriculture has become a research hotspot in the field of agriculture technology. Meanwhile, the image is one of the important data sources for smart agriculture and related technology. Image processing technology has been widely used in modern agricultural research. In the application of outdoor agriculture, environmental conditions are important factors that degrade the quality of the obtained image. In particular, the haze is a very common factor that decreases image quality seriously. Images acquired in bad weather, such as haze, are seriously degraded by the scatting of the atmosphere particles, which reduces the contrast, color saturation and hue shift and makes the object features difficult to identify. In order to remove the negative effect of the haze in degrading image quality, this study proposes a new image dehazing algorithm that combines the dark channel prior model with the interval interpolating wavelet transform. The dark channel prior is based on the statistics of the haze-free outdoor images. Specifically, it is based on a key observation, i.e. most local patches in haze-free outdoor images contain some pixels which have very low intensities in at least one color channel. Using this prior with the haze imaging model, we can directly estimate the thickness of the haze and recover a high quality haze-free image. The wavelet transform is used to carry out multi-scale refinement through the operation of telescopic translation, which can highlight the characteristics of the details of the image. Interval interpolation wavelet may reduce the error caused by the approximation of the wavelet. Firstly, we estimate the transmission and the atmospheric light value by using the dark channel prior theory, and restore the image. Secondly, the obtained image is decomposed by interval interpolation wavelet transform, and then reconstructed by processing high frequency sub-band wavelet coefficients. An experiment is carried out using this method. The results show that after the image processing by using this method, the whole image looks like comparatively bright, and the image contrast and clarity are improved. Finally, it works to filter out the negative effect caused by the haze. The processed image fits the human observation feeling well. It has good visual effect, obvious layering and rich texture detail. For color images, color saturation can be well kept, and the distortion is correspondingly low. Hence, the processed color images are close to the real objects with true color. Moreover, after the image processing, the contour contrast of the scene is obvious and is not blurred. It also makes distant scenery in the image very clear. We compare our haze removal results with that by the dark channel prior algorithm. On average, the standard deviation values of our algorithm in the R, G, and B channels are respectively improved by 25.44%, 27.90% and 26.24%. In sum, this study presents a new method that combines the dark channel prior model with the interval interpolating wavelet transform, and the image can be well dehazed and achieve good restoration in image visibility using this method, and thereby lays the foundation for acquiring accurate image information. Moreover, it is also useful for the application in modern precision agriculture.