Abstract
In order to remove the strong noise with complex shapes and high density in nuclear radiation scenes, a lightweight network composed of a Noise Learning Unit (NLU) and Texture Learning Unit (TLU) was designed. The NLU is bilinearly composed of a Multi-scale Kernel Module (MKM) and a Residual Module (RM), which learn non-local information and high-level features, respectively. Both the MKM and RM have receptive field blocks and attention blocks to enlarge receptive fields and enhance features. The TLU is at the bottom of the NLU and learns textures through an independent loss. The entire network adopts a Mish activation function and asymmetric convolutions to improve the overall performance. Compared with 12 denoising methods on our nuclear radiation dataset, the proposed method has the fewest model parameters, the highest quantitative metrics, and the best perceptual satisfaction, indicating its high denoising efficiency and rich texture retention.
Highlights
IntroductionIn an environment with intense ionizing radiation, energetic particles can damage the electronic and optical components of image sensors [1], causing the captured digital images to be highly degraded; the images are essential for the subsequent professional analysis or other advanced computer vision tasks, e.g., image classification [2], object detection [3], and semantic segmentation [4], etc
The traditional image prior-based methods are tested on a CPU (Intel Core i7-6700), and the Floating Point of Operations (FLOPs) [40] of Convolutional Neural Networks (CNN)-based methods are based on the same input tensor shape (1, 3, 480, 640)
We evaluate the trainability of our model compared with six latest results are marked in boldface
Summary
In an environment with intense ionizing radiation, energetic particles can damage the electronic and optical components of image sensors [1], causing the captured digital images to be highly degraded; the images are essential for the subsequent professional analysis or other advanced computer vision tasks, e.g., image classification [2], object detection [3], and semantic segmentation [4], etc Shielding measures such as covering sensors with lead boxes [5] can improve the radiation resistance level to a certain extent, these measures will increase volumes and workloads of the perception machines sharply or raise the costs of these alternative sensors.
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