This paper presents an effective approach to recover a high resolution image from a single low resolution input image. In this technique lifting wavelet transform and stationary wavelet transform is used to increase the spatial resolution .The wavelet domain filters support to model the regularity of natural images while the edge details of image get sharper while up sampling. An iterative back projection method is used to reconstruct the high resolution image in an efficient iterative manner. Experimental results demonstrate that the proposed approach is very effective in increasing resolution compared to state-of-art super-resolution algorithms.

Traffic monitoring by aerial images is an important issue. Here, a novel method for vehicle detection from high resolution aerial images and videos is put forward. The system exploits the background of the aerial view for better performance. It works by identification of structural element followed by morphological operations. The morphological operations include opening and top-hat transformation for light background components and closing followed by bot-hat transformation for dark background components. Then big objects are sieved using an area threshold which is larger than a typical vehicle followed by morphological opening transformation to remove targets whose width is narrower than the diameter of structural element utilized in the morphological operations. Finally the results are overlaid in order to amalgamate those vehicles detected by both cases. The experimental result of this method on highway aerial image of 0.15×0.15 m spatial resolution and a video footage shows that the method is robust and efficient. In addition to this a method for classification of vehicles is also proposed based on morphological operations and component parameters of the images which classify the detected vehicles into big and small vehicles. Direction of vehicles is also found.