Multiplexing systems performance enhancements with all-optical signal processing

It is believed that an all-optical signal processing based on advanced integrated devices (passive, active or both) would play an important role in future multiplexing systems. In this paper we discuss approaches and techniques we have developed and demonstrated for improving the scalability and performance of Optical Code Division Multiplexing (OCDM)

Towards integrated devices for ultra-fast all-optical signal processing in optical networks

Over the last decades, the internet traffic has been rapidly growing resulting in Terabits per second aggregate data throughputs. We have also been witnessing that the electronic signal processing the way it is known today is approaching its limitations due to a grooving manifestation of the electronic bottleneck. It is believed that the ultra-fast signal processing based on all-optical integrated devices (passive, active or both) could play an important role in communication systems of the future. In this paper we discuss integrated devices, approaches and techniques we have developed and demonstrated towards achieving these ambitious goals and leading also to improve the scalability of optical networks such as Optical Code Division Multiple Access (OCDMA), namely: all-optical ultrafast demultiplexers, self-clocked all-optical time gates, all-optical thresholders, filters, AWGs, tunable optical delay lines, and devices for dispersion management

SAM-Deblur: Let Segment Anything Boost Image Deblurring

Image deblurring is a critical task in the field of image restoration, aiming to eliminate blurring artifacts. However, the challenge of addressing non-uniform blurring leads to an ill-posed problem, which limits the generalization performance of existing deblurring models. To solve the problem, we propose a framework SAM-Deblur, integrating prior knowledge from the Segment Anything Model (SAM) into the deblurring task for the first time. In particular, SAM-Deblur is divided into three stages. First, We preprocess the blurred images, obtain image masks via SAM, and propose a mask dropout method for training to enhance model robustness. Then, to fully leverage the structural priors generated by SAM, we propose a Mask Average Pooling (MAP) unit specifically designed to average SAM-generated segmented areas, serving as a plug-and-play component which can be seamlessly integrated into existing deblurring networks. Finally, we feed the fused features generated by the MAP Unit into the deblurring model to obtain a sharp image. Experimental results on the RealBlurJ, ReloBlur, and REDS datasets reveal that incorporating our methods improves NAFNet's PSNR by 0.05, 0.96, and 7.03, respectively. Code will be available at \href{https://github.com/HPLQAQ/SAM-Deblur}{SAM-Deblur}.Comment: Under revie