20,318 research outputs found
Geometry-Aware Neighborhood Search for Learning Local Models for Image Reconstruction
Local learning of sparse image models has proven to be very effective to
solve inverse problems in many computer vision applications. To learn such
models, the data samples are often clustered using the K-means algorithm with
the Euclidean distance as a dissimilarity metric. However, the Euclidean
distance may not always be a good dissimilarity measure for comparing data
samples lying on a manifold. In this paper, we propose two algorithms for
determining a local subset of training samples from which a good local model
can be computed for reconstructing a given input test sample, where we take
into account the underlying geometry of the data. The first algorithm, called
Adaptive Geometry-driven Nearest Neighbor search (AGNN), is an adaptive scheme
which can be seen as an out-of-sample extension of the replicator graph
clustering method for local model learning. The second method, called
Geometry-driven Overlapping Clusters (GOC), is a less complex nonadaptive
alternative for training subset selection. The proposed AGNN and GOC methods
are evaluated in image super-resolution, deblurring and denoising applications
and shown to outperform spectral clustering, soft clustering, and geodesic
distance based subset selection in most settings.Comment: 15 pages, 10 figures and 5 table
Blind Quality Assessment for Image Superresolution Using Deep Two-Stream Convolutional Networks
Numerous image superresolution (SR) algorithms have been proposed for
reconstructing high-resolution (HR) images from input images with lower spatial
resolutions. However, effectively evaluating the perceptual quality of SR
images remains a challenging research problem. In this paper, we propose a
no-reference/blind deep neural network-based SR image quality assessor
(DeepSRQ). To learn more discriminative feature representations of various
distorted SR images, the proposed DeepSRQ is a two-stream convolutional network
including two subcomponents for distorted structure and texture SR images.
Different from traditional image distortions, the artifacts of SR images cause
both image structure and texture quality degradation. Therefore, we choose the
two-stream scheme that captures different properties of SR inputs instead of
directly learning features from one image stream. Considering the human visual
system (HVS) characteristics, the structure stream focuses on extracting
features in structural degradations, while the texture stream focuses on the
change in textural distributions. In addition, to augment the training data and
ensure the category balance, we propose a stride-based adaptive cropping
approach for further improvement. Experimental results on three publicly
available SR image quality databases demonstrate the effectiveness and
generalization ability of our proposed DeepSRQ method compared with
state-of-the-art image quality assessment algorithms
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