Every day many images are taken by digital cameras, and people
are demanding visually accurate and pleasing result. Noise and
blur degrade images captured by modern cameras, and high-level
vision tasks (such as segmentation, recognition, and tracking)
require high-quality images. Therefore, image restoration
specifically, image
deblurring and image denoising is a critical preprocessing step.
A fundamental problem in image deblurring is to recover reliably
distinct spatial frequencies that have been suppressed by the
blur kernel. Existing image deblurring techniques often rely on
generic image priors that only help recover part of the frequency
spectrum, such as the frequencies near the high-end. To this end,
we pose the following specific questions: (i) Does class-specific
information offer an advantage over existing generic priors for
image quality restoration? (ii) If a class-specific prior exists,
how should it be encoded into a deblurring framework to recover
attenuated image frequencies? Throughout this work, we devise a
class-specific prior based on the band-pass filter responses and
incorporate it into a deblurring strategy. Specifically, we show
that the subspace of band-pass filtered images and their
intensity distributions serve as useful priors for recovering
image frequencies.
Next, we present a novel image denoising algorithm that uses
external, category specific image database. In contrast to
existing noisy image restoration algorithms, our method selects
clean image “support patches” similar to the noisy patch from
an external database. We employ a content adaptive distribution
model for each patch where we derive the parameters of the
distribution from the support patches. Our objective function
composed of a Gaussian fidelity term that imposes category
specific information, and a low-rank term that encourages the
similarity between the noisy and the support patches in a robust
manner.
Finally, we propose to learn a fully-convolutional network model
that consists of a Chain of Identity Mapping Modules (CIMM) for
image denoising. The CIMM structure possesses two distinctive
features that are important for the noise removal task. Firstly,
each residual unit employs identity mappings as the skip
connections and receives pre-activated input to preserve the
gradient magnitude propagated in both the forward and backward
directions. Secondly, by utilizing dilated kernels for the
convolution layers in the residual branch, each neuron in the
last convolution layer of each module can observe the full
receptive field of the first layer
