Adaptive Measurement Network for CS Image Reconstruction
Conventional compressive sensing (CS) reconstruction is very slow for its characteristic of solving an optimization problem. Convolu- tional neural network can realize fast processing while achieving compa- rable results. While CS image recovery with high quality not only de- pends on good reconstruction algorithms, but also good measurements. In this paper, we propose an adaptive measurement network in which measurement is obtained by learning. The new network consists of a fully-connected layer and ReconNet. The fully-connected layer which has low-dimension output acts as measurement. We train the fully-connected layer and ReconNet simultaneously and obtain adaptive measurement. Because the adaptive measurement fits dataset better, in contrast with random Gaussian measurement matrix, under the same measuremen- t rate, it can extract the information of scene more efficiently and get better reconstruction results. Experiments show that the new network outperforms the original one.
Code (1)
Tasks
Compressive SensingImage ReconstructionSimilar Papers 제목 키워드 기반
Conformalized Rate-Adaptive Sensing
Many high-resolution imaging systems face the same fundamental question: when have enough measurements been collected to reconstruct an image accurately? We develop Conformalized Rate-Adaptive Sensing (CoRAS), a method t…
Image ReconstructionOnline Adaptive Image Reconstruction (OnAIR) Using Dictionary Models
Sparsity and low-rank models have been popular for reconstructing images and videos from limited or corrupted measurements. Dictionary or transform learning methods are useful in applications such as denoising, inpaintin…
DenoisingImage ReconstructionVideo ReconstructionEnhanced total variation minimization for stable image reconstruction
The total variation (TV) regularization has phenomenally boosted various variational models for image processing tasks. We propose to combine the backward diffusion process in the earlier literature of image enhancement …
Image EnhancementImage ReconstructionRate-Adaptive Neural Networks for Spatial Multiplexers
In resource-constrained environments, one can employ spatial multiplexing cameras to acquire a small number of measurements of a scene, and perform effective reconstruction or high-level inference using purely data-drive…
Object TrackingvalidReconstruction-free Cascaded Adaptive Compressive Sensing
Scene-aware Adaptive Compressive Sensing (ACS) has constituted a persistent pursuit holding substantial promise for the enhancement of Compressive Sensing (CS) performance. Cascaded ACS furnishes a proficient multi-s…
Compressive SensingImage Reconstruction