11,139 research outputs found
Modulating Image Restoration with Continual Levels via Adaptive Feature Modification Layers
In image restoration tasks, like denoising and super resolution, continual
modulation of restoration levels is of great importance for real-world
applications, but has failed most of existing deep learning based image
restoration methods. Learning from discrete and fixed restoration levels, deep
models cannot be easily generalized to data of continuous and unseen levels.
This topic is rarely touched in literature, due to the difficulty of modulating
well-trained models with certain hyper-parameters. We make a step forward by
proposing a unified CNN framework that consists of few additional parameters
than a single-level model yet could handle arbitrary restoration levels between
a start and an end level. The additional module, namely AdaFM layer, performs
channel-wise feature modification, and can adapt a model to another restoration
level with high accuracy. By simply tweaking an interpolation coefficient, the
intermediate model - AdaFM-Net could generate smooth and continuous restoration
effects without artifacts. Extensive experiments on three image restoration
tasks demonstrate the effectiveness of both model training and modulation
testing. Besides, we carefully investigate the properties of AdaFM layers,
providing a detailed guidance on the usage of the proposed method.Comment: Accepted by CVPR 2019 (oral); code is available:
https://github.com/hejingwenhejingwen/AdaF
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
Learning a Dilated Residual Network for SAR Image Despeckling
In this paper, to break the limit of the traditional linear models for
synthetic aperture radar (SAR) image despeckling, we propose a novel deep
learning approach by learning a non-linear end-to-end mapping between the noisy
and clean SAR images with a dilated residual network (SAR-DRN). SAR-DRN is
based on dilated convolutions, which can both enlarge the receptive field and
maintain the filter size and layer depth with a lightweight structure. In
addition, skip connections and residual learning strategy are added to the
despeckling model to maintain the image details and reduce the vanishing
gradient problem. Compared with the traditional despeckling methods, the
proposed method shows superior performance over the state-of-the-art methods on
both quantitative and visual assessments, especially for strong speckle noise.Comment: 18 pages, 13 figures, 7 table
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