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ΣΔ ADCs show promising perspectives in the field of compressive sensing (CS). A major limitation related to the first-order ΣΔ is its high oversampling ratio (OSR) that is required for a specific bit resolution. An extension to the high-order incremental ΣΔ for the implementation of a dedicated image sensor is presented. By extending the concept of the ΣΔ CS ADC, optimal working points can be reached...
A new approach to perform the acquisition and the reconstruction of spatially super-resolved hyperspectral images is presented. The proposed hyperspectral sensing strategy is based on acquiring several low-resolved grayscale images following a specific acquisition scheme which takes profit from different spectral dependent blurring kernels. The proposed model describes how output grayscale pixels...
Recently developed Compressive Sensing image sensor architectures tend to provide compact on-chip implementations to perform alternative acquisitions. On the other hand, the time of reconstruction generally limits possible applications taking advantage of those specific sensing schemes. This work proposes an entire Compressive Sensing system composed of an encoder (a dedicated imager top-level architecture)...
Some smart imagers embed image processing operations or Compressive Sensing at the focal plane level. This process introduces artifacts due to technology dispersion and unpredictable behaviors. This article presents a generic algorithm structure well suited for compensating block artifacts by appropriate post processing operations. The proposed restoration method is composed by a three steps loop:...
We propose a novel approach to reconstruct High Dynamic Range images from few compressive measurements. The reconstruction algorithm directly merges the information of multi-capture bayerized images. It simultaneously performs demosaicing and naive predefined tone-mapping. Two different color spaces are taken into account at the reconstruction stage to add multiple constraints on the signal. The proposed...
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