A 4-channelled hazy image input generation and deep learning-based single image dehazing

The images in foggy weather are often degraded which may affect the object detection process. Hence, in recent times, several schemes have been designed to eliminate the haze effect and enhance the performance of computer vision systems. Although these methods reduce the haze effect, they also produce over-saturation and over-degradation on most occasions. These problems occur due to inadequate utilization of the regional haze properties during the dehazing process. Therefore, a new dehazing model is proposed in this paper to address the issues faced by existing dehazing algorithms. The residual convolutional neural network (RCNN) with 14 layers is developed in the present work. In this model, features to indicate the extent of haze are extracted using the regional characteristics of a hazy image and are used as the fourth channel along with the three colour channels of a hazy image. The proposed RCNN is trained with 4-channel hazy images as input and their corresponding haze-free images as output. The results of our model are obtained with several images taken from three different datasets. Through the experimentation, quantitative and qualitative improvements are observed when compared to state-of-the-art dehazing methods. •The local-haze characteristics driven dehazing method is developed in this work.•The regional haze properties are extracted using pixel-wise local max-min difference and pixel intensities of the hazy image.•A 4-channel hazy image is developed by concatenating the regional haze properties map to the hazy image.•A 14-layered RCNN is developed with the 4-channel hazy image as input and the haze-free image as output.