Computational photography relies on specialized image processing techniques to combine multiple images captured by a camera in order to generate a desired image of the scene. We consider the High Dynamic Range (HDR) imaging problem. We can change either the exposure time or the aperture while capturing multiple images of the scene to generate an HDR image. This paper addresses the HDR imaging problem for static and dynamic scenes when we do not have any knowledge of the camera settings. We have proposed a novel framework based on sparse representation which enables us to process images while getting rid of artifacts due to moving objects and defocus blur. We show that the proposed approach is able to produce significantly good results through dynamic object rejection and deblurring capabilities. We also show that the same framework can be used to fuse flash/no-flash image pairs to produce a better detailed image. We compare the results with other competitive approaches and discuss the relative advantages of the proposed approach.