Kernel Aware Burst

Kernel-aware burst processing focuses on leveraging the information contained within multiple, slightly varied images (a "burst") captured in rapid succession, primarily to improve image quality and overcome limitations of single-image processing. Current research emphasizes developing algorithms and neural network architectures, such as transformers and those incorporating deformable alignment and burst affinity fusion, to effectively align and fuse information from burst images, addressing challenges like motion blur, noise, and occlusion. This work has significant implications for various applications, including image super-resolution, denoising, and object recognition, as well as improving the efficiency and accuracy of spiking neural networks and enhancing predictions in high-performance computing environments.