Deep Plug-and-Play Nighttime Non-Blind Deblurring With Saturated Pixel Handling Schemes

Due to the setting of shutter speeds, over-exposed blurry images can often be seen in nighttime photography. Although image deblurring is a classic problem in image restoration, state-of-the-art methods often fail in nighttime cases with saturated pixels. The primary reason is that those pixels are out of the sensor range and thus violate the assumption of the linear blur model. To address this issue, we propose a new nighttime non-blind deblurring algorithm with saturated pixel handling schemes, including a pixel stretching mask, an image segment mask, and a saturation awareness mechanism (SAM). Our algorithm achieves superior results by strategically adjusting mask configurations, making our method robust to various saturation levels. We formulate our task into two new optimization problems and introduce a unified framework based on the plug-and-play alternating direction method of multipliers (PnP-ADMM). We also evaluate our approach qualitatively and quantitatively to demonstrate its effectiveness. The results show that the proposed algorithm recovers sharp latent images with finer details and fewer artifacts than other state-of-the-art deblurring methods.