Images captured in front of a glass obstruction often suffer from degradation due to the presence of reflections. The intensity of these reflections are mostly influenced by the prevailing lighting conditions in the desirable background and the undesirable reflection scenes. The reflections can be classified as either high transmitted or low transmitted depending upon whether the captured image is dominated by either the background or the reflections respectively. Current approaches either aim to handle only high transmitted reflections or propose to train a unified neural network for addressing both kinds of reflections. However, using a single network to address different types of reflections is not very effective and moreover these methods are also computationally expensive and impractical to deploy on resource limited devices such as smartphones. To address these challenges, we present a multi-stage pipeline for single image reflection removal within a scale space framework to address low and high transmitted reflections separately. Specifically, we treat the removal of low transmitted reflections that typically obscure the desired background as an inpainting challenge, while we handle high transmitted reflections using conventional techniques. The proposed method is light weight and is capable of removing reflections from very high resolution images frequently encountered in real world. Our method shows superior performance both qualitatively and quantitatively compared to state of the art methods and our smartphone implementation takes about 2 seconds to process a high resolution 12MP image.