1,747 research outputs found
Natural and Realistic Single Image Super-Resolution with Explicit Natural Manifold Discrimination
Recently, many convolutional neural networks for single image
super-resolution (SISR) have been proposed, which focus on reconstructing the
high-resolution images in terms of objective distortion measures. However, the
networks trained with objective loss functions generally fail to reconstruct
the realistic fine textures and details that are essential for better
perceptual quality. Recovering the realistic details remains a challenging
problem, and only a few works have been proposed which aim at increasing the
perceptual quality by generating enhanced textures. However, the generated fake
details often make undesirable artifacts and the overall image looks somewhat
unnatural. Therefore, in this paper, we present a new approach to
reconstructing realistic super-resolved images with high perceptual quality,
while maintaining the naturalness of the result. In particular, we focus on the
domain prior properties of SISR problem. Specifically, we define the
naturalness prior in the low-level domain and constrain the output image in the
natural manifold, which eventually generates more natural and realistic images.
Our results show better naturalness compared to the recent super-resolution
algorithms including perception-oriented ones.Comment: Presented in CVPR 201
Lossless Compression of Medical Image Sequences Using a Resolution Independent Predictor and Block Adaptive Encoding
The proposed block-based lossless coding technique presented in this paper targets at compression of volumetric medical images of 8-bit and 16-bit depth. The novelty of the proposed technique lies in its ability of threshold selection for prediction and optimal block size for encoding. A resolution independent gradient edge detector is used along with the block adaptive arithmetic encoding algorithm with extensive experimental tests to find a universal threshold value and optimal block size independent of image resolution and modality. Performance of the proposed technique is demonstrated and compared with benchmark lossless compression algorithms. BPP values obtained from the proposed algorithm show that it is capable of effective reduction of inter-pixel and coding redundancy. In terms of coding efficiency, the proposed technique for volumetric medical images outperforms CALIC and JPEG-LS by 0.70 % and 4.62 %, respectively
Reconstruction from Spatio-Spectrally Coded Multispectral Light Fields
In dieser Arbeit werden spektral kodierte multispektrale Lichtfelder untersucht, wie sie von einer Lichtfeldkamera mit einem spektral kodierten Mikrolinsenarray aufgenommen werden. Für die Rekonstruktion der kodierten Lichtfelder werden zwei Methoden entwickelt, eine basierend auf den Prinzipien des Compressed Sensing sowie eine Deep Learning Methode. Anhand neuartiger synthetischer und realer Datensätze werden die vorgeschlagenen Rekonstruktionsansätze im Detail evaluiert
Reconstruction from Spatio-Spectrally Coded Multispectral Light Fields
In this work, spatio-spectrally coded multispectral light fields, as taken by a light field camera with a spectrally coded microlens array, are investigated. For the reconstruction of the coded light fields, two methods, one based on the principles of compressed sensing and one deep learning approach, are developed. Using novel synthetic as well as a real-world datasets, the proposed reconstruction approaches are evaluated in detail
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