Fully convolutional measurement network for compressive sensing image reconstruction

Du, Jiang; Xie, Xuemei; Wang, Chenye; Shi, Guangming; Xu, Xun; Wang, Yuxiang

doi:10.1016/j.neucom.2018.04.084

Cited by 51 publications

(49 citation statements)

References 28 publications

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“…This is because the mapping from the scene image to the measurements is fully-connected, leading to an extremely large-scale parameter nightmare. Inspired by fully convolutional measurement network (FCMN) [36], we employ a fully convolutional architecture to measure and recover the scene images in the proposed framework, which can get rid of the disaster of the exploding number of parameters. Moreover, the fully convolutional architecture can preserve the correspondence among pixels (instead of reshaping into column vector).…”

Section: Full Image Compressive Sensing Networkmentioning

confidence: 99%

“…Recently, deep neural networks (DNNs) has been applied to CS tasks [ [36]. These DNN-based methods methods can be divided into two categories depending on whether measurement and reconstruction process are trained jointly.…”

Section: Introductionmentioning

confidence: 99%

“…The second category jointly trains the measurement part and the recovery part, such as Deepcodec [26], Adaptive [37], and FCMN [36]. These methods totally overcome the problem that the measurement part is independent from the recovery part.…”

Section: Introductionmentioning

confidence: 99%

“…In [37], a fully-connected layer as the measurement matrix along with a super-resolution network as the recovery part is trained. FCMN [36] firstly uses a fully convolutional network where the measurement part is implemented with an overlapped convolution operation. All these methods recover the scene image on pixel level.…”

Section: Introductionmentioning

confidence: 99%

“…Adp-Rec [37] jointly train the coder-decoder and brings significant improvement on reconstruction quality. Fully convolutional measurement network (FCMN) [36] firstly measures and recovers full images. However, all the above methods focus on pixel level, and ignore the high-level structure information.…”

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confidence: 99%

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Perceptual Compressive Sensing

Xie

Wang

et al. 2018

Pattern Recognition and Computer Vision

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Compressive sensing (CS) works to acquire measurements at sub-Nyquist rate and recover the scene images. Existing CS methods always recover the scene images in pixel level. This causes the smoothness of recovered images and lack of structure information, especially at a low measurement rate. To overcome this drawback, in this paper, we propose perceptual CS to obtain high-level structured recovery. Our task no longer focuses on pixel level. Instead, we work to make a better visual effect. In detail, we employ perceptual loss, defined on feature level, to enhance the structure information of the recovered images. Experiments show that our method achieves better visual results with stronger structure information than existing CS methods at the same measurement rate. 2 J. Du et al.the CS measurements can be recovered real-time. [18] [25] learns the recovery network by the training data. Adp-Rec [37] jointly train the coder-decoder and brings significant improvement on reconstruction quality. Fully convolutional measurement network (FCMN) [36] firstly measures and recovers full images. However, all the above methods focus on pixel level, and ignore the high-level structure information. This makes the reconstructed results look smooth and have unsatisfactory visual effect. To overcome the drawback, we consider to add high-level perceptual information to CS. So the question is, how to add high-level perceptual information on the low-level CS task.Recently, perceptual loss [17] has been used in many reconstruction tasks, such as style transfer [17] [5]. They are a combination of low-level detailed information and high level semantic information. Perceptual loss is a widely used way to achieve these goals. It is because perceptual loss is defined in feature space, which can convert the ability of catching high-level structure information to recovery network. Thus, the recovered images will contain rich structure information. Inspired by the above applications, we propose perceptual CS, which focuses more on sensing and recovering structure information. Here perceptual loss is employed on CS framework. We use FCMN [36] as base network to measure and recover scene images, and adopt perceptual loss to train it. We surprisingly find that this framework is capable of capturing and recovering the structure information, especially at extremely low measurement rate, where the measurements can merely contain very limited amount of information.The contribution of this paper is that, we propose perceptual CS, which can measure and recover the structure information of scene images. It should be pointed out that, only one deconvolutional layer and one Res-block are used in our proposed framework. This is just an illustration. One can employ a deeper network architecture if necessary.Moreover, perceptual CS indicates an universal architecture. One can change the loss network using pre-trained or dynamic feature extractors for more specific tasks. In this paper, we use VGG [32] as an example. Our code will be available on github 1 fo...

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Section: Full Image Compressive Sensing Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%