Handling exposure time in multi-frame image restoration

The process of reconstructing a high-resolution image from multiple low-resolution images is known as super-resolution reconstruction. One of the assumptions of current super-resolution reconstruction methods is that all images are captured with the same exposure time and aperture size. This is not necessarily true. In reality, cameras have limited dynamic range and a nonlinear response to the quantity of light received; also, camera settings might be adjusted automatically or manually to capture the desired portion of the scene´s dynamic range. We propose a super-resolution algorithm based on an imaging model that includes a camera response function, exposure time, sensor noise and quantization error in addition to spatial blurring and sampling. The algorithm is based on Bayesian estimation. Initial experiments demonstrate the effectiveness of the algorithm.