COLOR BALANCED HISTOGRAM EQUALIZATION FOR IMAGE ENHANCEMENT

Recovering a clear image solely from a hazy input image is a challenging task. Existing enhancement techniques are empirically expected to be helpful in performing high-level computer vision tasks like object detection. However, this is not always accurate or practical. In this study, we propose a novel image dehazing method: Color Balancing and Histogram Equalization (CBHE). The method is designed with an aim to merge it with existing object detector models like Faster RCNN and improve the accuracy of object detection under poor visibility. In this method, color balancing and histogram equalization along with image processing techniques have been applied for dehazing. We used the dataset from the UG2+ challenge Track 2 competition called Realistic Single Image Dehazing(RESIDE) - STANDARD that comprised of a diverse set of both synthetic and real-world images. Experimental results on both indoor and outdoor test datasets demonstrate a large improvement in the object detection
performance compared to existing techniques when the dehazed image is merged with a pre-trained object detector.