4 accepted papers
Recent research has shown that fine-tuning diffusion models (DMs) with arbitrary rewards, including non-differentiable ones, is feasible with reinforcement learning (RL) techniques, enabling flexible model alignment. However, applying existing RL methods to step-distilled DMs is challenging for ultr…
When taking a picture, any camera shake during the shutter time can result in a blurred image. Recovering a sharp image from the one blurred by camera shake is a challenging yet important problem. Most existing deep learning methods use supervised learning to train a deep neural network (DNN) on a d…
Most existing non-blind image deconvolution methods assume that the given blurring kernel is error-free. In practice, blurring kernel often is estimated via some blind deblurring algorithm which is not exactly the truth. Also, the convolution model is only an approximation to practical blurring effe…
Non-blind deblurring is an important problem encountered in many image restoration tasks. The focus of non-blind deblurring is on how to suppress noise magnification during deblurring. In practice, it often happens that the noise level of input image is unknown and varies among different images. Thi…