High-resolution image reconstruction from multiple differently exposed images

Super-resolution reconstruction is the process of reconstructing a high-resolution image from multiple low-resolution images. Most super-resolution reconstruction methods assume that exposure time is fixed for all observations, which is not necessarily true. In reality, cameras have limited dynamic range and nonlinear response to the quantity of light received, and exposure time might be adjusted automatically or manually to capture the desired portion of the scene's dynamic range. In this letter, we propose a Bayesian super-resolution algorithm based on an imaging model that includes camera response function, exposure time, sensor noise, and quantization error in addition to spatial blurring and sampling.