It is challenging to render novel views from a single image input due to inherent ambiguities in the geometry and texture information of the desired scene. As a consequence, existing methods often encounter various types of distortions in synthesized views. To this end, we propose a distortion-resilient Depth-Image-Based Rendering (DIBR) method for synthesizing novel views given a single image input. The proposed method is qualitatively and quantitatively evaluated on the Real-Estate 10K dataset, showing superior results compared to baselines.