3 research outputs found
Exposure Interpolation by Combining Model-driven and Data-driven Methods
Deep learning based methods have penetrated many image processing problems
and become dominant solutions to these problems. A natural question raised here
is "Is there any space for conventional methods on these problems?" In this
paper, exposure interpolation is taken as an example to answer this question
and the answer is "Yes". A framework on fusing conventional and deep learning
method is introduced to generate an medium exposure image for two
large-exposureratio images. Experimental results indicate that the quality of
the medium exposure image is increased significantly through using the deep
learning method to refine the interpolated image via the conventional method.
The conventional method can be adopted to improve the convergence speed of the
deep learning method and to reduce the number of samples which is required by
the deep learning method.Comment: 10 page
Deep Convolutional Sparse Coding Networks for Image Fusion
Image fusion is a significant problem in many fields including digital
photography, computational imaging and remote sensing, to name but a few.
Recently, deep learning has emerged as an important tool for image fusion. This
paper presents three deep convolutional sparse coding (CSC) networks for three
kinds of image fusion tasks (i.e., infrared and visible image fusion,
multi-exposure image fusion, and multi-modal image fusion). The CSC model and
the iterative shrinkage and thresholding algorithm are generalized into
dictionary convolution units. As a result, all hyper-parameters are learned
from data. Our extensive experiments and comprehensive comparisons reveal the
superiority of the proposed networks with regard to quantitative evaluation and
visual inspection
Single Image Brightening via Multi-Scale Exposure Fusion with Hybrid Learning
A small ISO and a small exposure time are usually used to capture an image in
the back or low light conditions which results in an image with negligible
motion blur and small noise but look dark. In this paper, a single image
brightening algorithm is introduced to brighten such an image. The proposed
algorithm includes a unique hybrid learning framework to generate two virtual
images with large exposure times. The virtual images are first generated via
intensity mapping functions (IMFs) which are computed using camera response
functions (CRFs) and this is a model-driven approach. Both the virtual images
are then enhanced by using a data-driven approach, i.e. a residual
convolutional neural network to approach the ground truth images. The
model-driven approach and the data-driven one compensate each other in the
proposed hybrid learning framework. The final brightened image is obtained by
fusing the original image and two virtual images via a multi-scale exposure
fusion algorithm with properly defined weights. Experimental results show that
the proposed brightening algorithm outperforms existing algorithms in terms of
the MEF-SSIM metric.Comment: 11 page