Abstract
An Intensified Charge-Coupled Device (ICCD) image is captured by the ICCD image sensor in extremely low-light conditions. Its noise has two distinctive characteristics. (a) Different from the independent identically distributed (i.i.d.) noise in natural image, the noise in the ICCD sensing image is spatially clustered, which induces unexpected structure information; (b) The pattern of the clustered noise is formed randomly. In this paper, we propose a denoising scheme to remove the randomly clustered noise in the ICCD sensing image. First, we decompose the image into non-overlapped patches and classify them into flat patches and structure patches according to if real structure information is included. Then, two denoising algorithms are designed for them, respectively. For each flat patch, we simulate multiple similar patches for it in pseudo-time domain and remove its noise by averaging all the simulated patches, considering that the structure information induced by the noise varies randomly over time. For each structure patch, we design a structure-preserved sparse coding algorithm to reconstruct the real structure information. It reconstructs each patch by describing it as a weighted summation of its neighboring patches and incorporating the weights into the sparse representation of the current patch. Based on all the reconstructed patches, we generate a reconstructed image. After that, we repeat the whole process by changing relevant parameters, considering that blocking artifacts exist in a single reconstructed image. Finally, we obtain the reconstructed image by merging all the generated images into one. Experiments are conducted on an ICCD sensing image dataset, which verifies its subjective performance in removing the randomly clustered noise and preserving the real structure information in the ICCD sensing image.
Highlights
An Intensified Charge-Coupled Device (ICCD) image sensor is used to capture the scene at extremely low-light-level conditions [1,2,3]
The denoising method based on the K-singular value decomposition (SVD) [40] could partly remove the clustered noise when the relevant parameter was properly chosen
Figure 6. the Thedataset subjective performance the(b) proposed sparse with simulated clusteredof noise; the results structure-preserved of proposed method; (c) the resultscoding of the method: (a) the dataset with simulated clustered noise; the results proposed results of the method based on DCT; (d) the result of the(b) method based onofK-SVD; (e) themethod; result of (c) the the method basedon on DCT; Beta Process (BP). (d) the result of the method based on K-clustering with singular value decomposition (K-SVD); (e) the result of the method based method based on BP
Summary
An Intensified Charge-Coupled Device (ICCD) image sensor is used to capture the scene at extremely low-light-level conditions [1,2,3]. Some electrons are obtained and injected into many voltages applied microchannel tubes after photovoltaic conversion process. Each electron crashes into the wall of a tube to generate more electrons. All the electrons will be ejected from the tubes and shot onto the fluorescent screen. The image is captured by the CCD image sensor [4,5,6]. In this way, the ICCD image sensor could capture the scene in a low-light environment.
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