We present a novel approach to HDR (high-dynamic-range) image fusion that copes with image blur degradation often present in long-exposed images. The proposed approach can deal with both camera and object motion blur in a computationally efficient manner suitable for implementation on a mobile device. The main idea is to exploit the differences between the image degradations that affect images captured for HDR fusion. Short-exposed images are mainly affected by sensor noise and less affected by motion blur, whereas longer exposed images are less noisy but potentially blurry due to motion during their exposure. Our approach consists of two steps. First we calculate an HDR representation of the scene by applying a typical HDR fusion approach. This representation could be blurry if some of the input images with long exposure time are blurry. We then fuse the HDR result with a photometrically modified version of the image with the shortest exposure time, which allows us to retain the sharpness of the short-exposed image and the noise-free characteristics of the HDR-fused image. The method does not assume an invariant blur PSF over any of the input images, and hence it can solve both for local and global blur, due to object or camera motion, respectively. We demonstrate the algorithm through a series of experiments and comparisons on natural images.