2017/06/02 by Guangtao Nie, Ying Fu, Nie, Guangtao +6
Computer Science · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image and Signal Denoising Methods #Photoacoustic and Ultrasonic Imaging #Sparse and Compressive Sensing Techniques #cs.CV
paper · pdf · doi:10.48550/arxiv.1706.00597
arxiv created 2017/06/02 · openalex publication_date 2017/06/02 · arxiv updated 2017/06/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A series of methods have been proposed to reconstruct an image from compressively sensed random measurement, but most of them have high time complexity and are inappropriate for patch-based compressed sensing capture, because of their serious blocky artifacts in the restoration results. In this paper, we present a non-iterative image reconstruction method from patch-based compressively sensed random measurement. Our method features two cascaded networks based on residual convolution neural network to learn the end-to-end full image restoration, which is capable of reconstructing image patches and removing the blocky effect with low time cost. Experimental results on synthetic and real data show that our method outperforms state-of-the-art compressive sensing (CS) reconstruction methods with patch-based CS measurement. To demonstrate the effectiveness of our method in more general setting, we apply the de-block process in our method to JPEG compression artifacts removal and achieve outstanding performance as well.