Sensors Free Full Text Event Guided Image Super Resolution The event camera efficiently detects scene radiance changes and produces an asynchronous event stream with low latency, high dynamic range (hdr), high temporal resolution, and low power consumption. however, the large output data caused by the asynchronous imaging mechanism makes the increase in spatial resolution of the event camera limited. in this paper, we propose a novel event camera. Feature papers represent the most advanced research with significant potential for high impact in the field. a feature paper should be a substantial original article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.
Sensors Free Full Text Event Guided Image Super Resolution The event camera efficiently detects scene radiance changes and produces an asynchronous. event stream with low latency, high dynamic range (hdr), high temporal resolution, and low power. Modern depth sensors are often characterized by low spatial resolution, which hinders their use in real world applications. however, the depth map in many scenarios is accompanied by a corresponding high resolution color image. in light of this, learning based methods have been extensively used for guided super resolution of depth maps. a guided super resolution scheme uses a corresponding. Event cameras detect the intensity changes and produce asynchronous events with high dynamic range and no motion blur. recently, several attempts have been made to super resolve the intensity images guided by events. however, these methods directly fuse the event and image features without distinguishing the modality difference and achieve image super resolution (sr) in multiple steps, leading. In this paper, we propose a novel event camera super resolution (sr) network (efsr net) based on a deep learning approach to address the problems of low spatial resolution and poor visualization of event cameras. the network model is capable of reconstructing high resolution (hr) intensity images using event streams and active sensor pixel (aps.
Sensors Free Full Text Event Guided Image Super Resolution Event cameras detect the intensity changes and produce asynchronous events with high dynamic range and no motion blur. recently, several attempts have been made to super resolve the intensity images guided by events. however, these methods directly fuse the event and image features without distinguishing the modality difference and achieve image super resolution (sr) in multiple steps, leading. In this paper, we propose a novel event camera super resolution (sr) network (efsr net) based on a deep learning approach to address the problems of low spatial resolution and poor visualization of event cameras. the network model is capable of reconstructing high resolution (hr) intensity images using event streams and active sensor pixel (aps. The proposed efsr net can improve the peak signal to noise ratio (psnr) metric by 1–2 db compared with state of the art methods and is able to reconstruct high resolution intensity images with more details and less blurring in synthetic and real datasets, respectively. the event camera efficiently detects scene radiance changes and produces an asynchronous event stream with low latency, high. Kuk jin yoon kaist, south korea. [email protected]. figure 1. reconstructing high definition photo realistic intensity images from pure events in end to end learning. our events to super resolved intensity image reconstruction recovers more details with less artifacts in comparison to recent methods of eg [24] and ev [17].
Sensors Free Full Text Event Guided Image Super Resolution The proposed efsr net can improve the peak signal to noise ratio (psnr) metric by 1–2 db compared with state of the art methods and is able to reconstruct high resolution intensity images with more details and less blurring in synthetic and real datasets, respectively. the event camera efficiently detects scene radiance changes and produces an asynchronous event stream with low latency, high. Kuk jin yoon kaist, south korea. [email protected]. figure 1. reconstructing high definition photo realistic intensity images from pure events in end to end learning. our events to super resolved intensity image reconstruction recovers more details with less artifacts in comparison to recent methods of eg [24] and ev [17].
Sensors Free Full Text Event Guided Image Super Resolution
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