12,315 research outputs found
BLADE: Filter Learning for General Purpose Computational Photography
The Rapid and Accurate Image Super Resolution (RAISR) method of Romano,
Isidoro, and Milanfar is a computationally efficient image upscaling method
using a trained set of filters. We describe a generalization of RAISR, which we
name Best Linear Adaptive Enhancement (BLADE). This approach is a trainable
edge-adaptive filtering framework that is general, simple, computationally
efficient, and useful for a wide range of problems in computational
photography. We show applications to operations which may appear in a camera
pipeline including denoising, demosaicing, and stylization
Image enhancement using fuzzy intensity measure and adaptive clipping histogram equalization
Image enhancement aims at processing an input
image so that the visual content of the output image is more
pleasing or more useful for certain applications. Although
histogram equalization is widely used in image enhancement due
to its simplicity and effectiveness, it changes the mean brightness
of the enhanced image and introduces a high level of noise and
distortion. To address these problems, this paper proposes
image enhancement using fuzzy intensity measure and adaptive
clipping histogram equalization (FIMHE). FIMHE uses fuzzy
intensity measure to first segment the histogram of the original
image, and then clip the histogram adaptively in order to
prevent excessive image enhancement. Experiments on the
Berkeley database and CVF-UGR-Image database show that
FIMHE outperforms state-of-the-art histogram equalization
based methods
ART-EMAP: A Neural Network Architecture for Object Recognition by Evidence Accumulation
A new neural network architecture is introduced for the recognition of pattern classes after supervised and unsupervised learning. Applications include spatio-temporal image understanding and prediction and 3-D object recognition from a series of ambiguous 2-D views. The architecture, called ART-EMAP, achieves a synthesis of adaptive resonance theory (ART) and spatial and temporal evidence integration for dynamic predictive mapping (EMAP). ART-EMAP extends the capabilities of fuzzy ARTMAP in four incremental stages. Stage 1 introduces distributed pattern representation at a view category field. Stage 2 adds a decision criterion to the mapping between view and object categories, delaying identification of ambiguous objects when faced with a low confidence prediction. Stage 3 augments the system with a field where evidence accumulates in medium-term memory (MTM). Stage 4 adds an unsupervised learning process to fine-tune performance after the limited initial period of supervised network training. Each ART-EMAP stage is illustrated with a benchmark simulation example, using both noisy and noise-free data. A concluding set of simulations demonstrate ART-EMAP performance on a difficult 3-D object recognition problem.Advanced Research Projects Agency (ONR N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (90-0083
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