Abstract: A method includes obtaining, using at least one processing device of an electronic device, multiple sets of training image frames, where each set of training image frames has an associated ground truth image. The method also includes applying, using the at least one processing device, motion blur and warping to the multiple sets of training image frames in order to generate additional sets of training image frames. In addition, the method includes training, using the at least one processing device, a machine learning model to align image frames and remove motion blur from the image frames based on at least the additional sets of training image frames and the ground truth images.

Abstract: To employ low resolution, noisy tone mapping operations for high resolution images, at least one raw image frame is converted to a first image at a higher resolution and a second image at a lower resolution. Tone mapping is applied to the second image to derive a third image at the lower resolution. Histogram matching and regularization are performed to determine a lookup table approximating histogram matching of the second image to the third image. A global gain map is derived based on luma for the first image and the lookup table. A local gain map is derived by up-sampling and denoising residual differences between the third image and the lookup table applied to the second image. Based on the global gain map and the local gain map, a total gain map is determined for tone mapping the first image to produce a fourth image at the first resolution.