Abstract: Convolutional neural networks have recently demonstrated high-quality reconstruction for single-image super-resolution. In this paper, we propose the Laplacian Pyramid Super-Resolution Network (LapSRN) to progressively reconstruct the sub-band residuals of high-resolution images. At each pyramid level, our model takes coarse-resolution feature maps as input, predicts the high-frequency residuals, and uses transposed convolutions for upsampling to the finer level. Our method does not require the bicubic interpolation as the pre-processing step and thus dramatically reduces the computational complexity. We train the proposed LapSRN with deep supervision using a robust Charbonnier loss function and achieve high-quality reconstruction. Furthermore, our network generates multi-scale predictions in one feed-forward pass through the progressive reconstruction, thereby facilitates resource-aware applications. Extensive quantitative and qualitative evaluations on benchmark datasets show that the proposed algorithm performs favorably against the state-of-the-art methods in terms of speed and accuracy.
|deep learning, computer vision|
Please login or register if you wish to leave a comment.
3,031 listings in 21 categories, with 1,076,696 clicks. Directory last updated Jan 22, 2018. Welcome Pankaj, the newest member.
best, worldclass, medical, hospitals, kidneystone, urineinfections, kidneyproblem, kidneystonesymptoms, gastrointestinal cancer, best gastro hospital in india, endoscopy demonstration by, dr pankaj vohra, gallblader, gallbladder video, the gallbladder, laparoscopic surgery laparoscopic surgeons laparos, cholecystectomy, nose reshaping surgery, cosmetic surgery, reconstructive surgery