Topic
Channel (digital image)
About: Channel (digital image) is a research topic. Over the lifetime, 7211 publications have been published within this topic receiving 69974 citations.
Papers published on a yearly basis
Papers
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TL;DR: Zhang et al. as mentioned in this paper proposed a channel enhancement feature pyramid network (CE-FPN) with three simple yet effective modules to alleviate the loss of semantical information due to channel reduction.
Abstract: Feature pyramid network (FPN) has been an effective framework to extract multi-scale features in object detection. However, current FPN-based methods mostly suffer from the intrinsic flaw of channel reduction, which brings about the loss of semantical information. And the miscellaneous fused feature maps may cause serious aliasing effects. In this paper, we present a novel channel enhancement feature pyramid network (CE-FPN) with three simple yet effective modules to alleviate these problems. Specifically, inspired by sub-pixel convolution, we propose a sub-pixel skip fusion method to perform both channel enhancement and upsampling. Instead of the original 1x1 convolution and linear upsampling, it mitigates the information loss due to channel reduction. Then we propose a sub-pixel context enhancement module for extracting more feature representations, which is superior to other context methods due to the utilization of rich channel information by sub-pixel convolution. Furthermore, a channel attention guided module is introduced to optimize the final integrated features on each level, which alleviates the aliasing effect only with a few computational burdens. Our experiments show that CE-FPN achieves competitive performance compared to state-of-the-art FPN-based detectors on MS COCO benchmark.
35 citations
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TL;DR: An effective scalable mobile image retrieval approach by exploiting the advantage of mobile end that people usually take multiple photos of an object in different viewpoints and focuses, and makes full use of the multiphotos taken at mobile end to extract saliency.
35 citations
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TL;DR: Experimental results show that the proposed scheme can significantly improve image fusion performance, performs very well in fusion and outperforms conventional methods such as traditional discrete wavelet transform, dual tree complex wavelet and PCNN in terms of objective criteria and visual appearance.
Abstract: Image fusion combines information from multiple images of the same scene to obtain a composite image which is more suitable for further image processing tasks. This study presented an image fusion scheme based on the modified dual pulse coupled neural network (PCNN) in non-subsampled contourlet transform (NSCT) domain. NSCT can overcome the lack of shift invariance in contourlet transform. Original images were decomposed to obtain the coefficients of low-frequency subbands and high-frequency subbands. In this fusion scheme, a new sum-modified Laplacian of the low-frequency subband image, which represents the edge-feature of the low-frequency subband image in NSCT domain, is presented and input to motivate modified dual PCNN. For fusion of high-frequency subband coefficients, spatial frequency will be used as the gradient features of images to motivate dual channel PCNN and to overcome Gibbs phenomena. Experimental results show that the proposed scheme can significantly improve image fusion performance, performs very well in fusion and outperforms conventional methods such as traditional discrete wavelet transform, dual tree complex wavelet and PCNN in terms of objective criteria and visual appearance.
35 citations
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TL;DR: This paper investigates the extension of the concept of ordering and order statistics filtering to multichannel and color images and provides the statistical analysis of marginal order filtering for color images.
Abstract: Color images are two-dimensional, three-channel stochastic signals. Their statistical characterization is quite different from that of black
and white images (single-channel, two-dimensional signals). This paper investigates the extension of the concept of ordering and order statistics
filtering to multichannel and color images. Marginal order statistics is the most straightforward extension to multichannel filtering. Marginal median
filtering is the same as median filtering each channel independently. However, its statistical analysis is not a simple extension of the analysis of the single-channel case. The statistical analysis of marginal order filtering for color images is provided.
35 citations
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TL;DR: A novel automated framework for analyzing and tracking sewer inspection close-circuit television (CCTV) videos and enables efficient revaluation of CCTV videos to extract sewer condition data is proposed.
35 citations