29,052 research outputs found
Single image super-resolution using self-optimizing mask via fractional-order gradient interpolation and reconstruction
Image super-resolution using self-optimizing mask via fractional-order
gradient interpolation and reconstruction aims to recover detailed information
from low-resolution images and reconstruct them into high-resolution images.
Due to the limited amount of data and information retrieved from low-resolution
images, it is difficult to restore clear, artifact-free images, while still
preserving enough structure of the image such as the texture. This paper
presents a new single image super-resolution method which is based on adaptive
fractional-order gradient interpolation and reconstruction. The interpolated
image gradient via optimal fractional-order gradient is first constructed
according to the image similarity and afterwards the minimum energy function is
employed to reconstruct the final high-resolution image. Fractional-order
gradient based interpolation methods provide an additional degree of freedom
which helps optimize the implementation quality due to the fact that an extra
free parameter -order is being used. The proposed method is able to
produce a rich texture detail while still being able to maintain structural
similarity even under large zoom conditions. Experimental results show that the
proposed method performs better than current single image super-resolution
techniques.Comment: 24 pages, 13 figures, it is to appear in ISA Transaction
Deep Artifact-Free Residual Network for Single Image Super-Resolution
Recently, convolutional neural networks have shown promising performance for
single-image super-resolution. In this paper, we propose Deep Artifact-Free
Residual (DAFR) network which uses the merits of both residual learning and
usage of ground-truth image as target. Our framework uses a deep model to
extract the high-frequency information which is necessary for high-quality
image reconstruction. We use a skip-connection to feed the low-resolution image
to the network before the image reconstruction. In this way, we are able to use
the ground-truth images as target and avoid misleading the network due to
artifacts in difference image. In order to extract clean high-frequency
information, we train the network in two steps. The first step is a traditional
residual learning which uses the difference image as target. Then, the trained
parameters of this step are transferred to the main training in the second
step. Our experimental results show that the proposed method achieves better
quantitative and qualitative image quality compared to the existing methods.Comment: 8 page
Boosting High-Level Vision with Joint Compression Artifacts Reduction and Super-Resolution
Due to the limits of bandwidth and storage space, digital images are usually
down-scaled and compressed when transmitted over networks, resulting in loss of
details and jarring artifacts that can lower the performance of high-level
visual tasks. In this paper, we aim to generate an artifact-free
high-resolution image from a low-resolution one compressed with an arbitrary
quality factor by exploring joint compression artifacts reduction (CAR) and
super-resolution (SR) tasks. First, we propose a context-aware joint CAR and SR
neural network (CAJNN) that integrates both local and non-local features to
solve CAR and SR in one-stage. Finally, a deep reconstruction network is
adopted to predict high quality and high-resolution images. Evaluation on CAR
and SR benchmark datasets shows that our CAJNN model outperforms previous
methods and also takes 26.2% shorter runtime. Based on this model, we explore
addressing two critical challenges in high-level computer vision: optical
character recognition of low-resolution texts, and extremely tiny face
detection. We demonstrate that CAJNN can serve as an effective image
preprocessing method and improve the accuracy for real-scene text recognition
(from 85.30% to 85.75%) and the average precision for tiny face detection (from
0.317 to 0.611).Comment: 8 pages, 6 figures, 5 tables. Accepted by the 25th ICPR (2020
Image Reconstruction with Predictive Filter Flow
We propose a simple, interpretable framework for solving a wide range of
image reconstruction problems such as denoising and deconvolution. Given a
corrupted input image, the model synthesizes a spatially varying linear filter
which, when applied to the input image, reconstructs the desired output. The
model parameters are learned using supervised or self-supervised training. We
test this model on three tasks: non-uniform motion blur removal,
lossy-compression artifact reduction and single image super resolution. We
demonstrate that our model substantially outperforms state-of-the-art methods
on all these tasks and is significantly faster than optimization-based
approaches to deconvolution. Unlike models that directly predict output pixel
values, the predicted filter flow is controllable and interpretable, which we
demonstrate by visualizing the space of predicted filters for different tasks.Comment: https://www.ics.uci.edu/~skong2/pff.htm
IEGAN: Multi-purpose Perceptual Quality Image Enhancement Using Generative Adversarial Network
Despite the breakthroughs in quality of image enhancement, an end-to-end
solution for simultaneous recovery of the finer texture details and sharpness
for degraded images with low resolution is still unsolved. Some existing
approaches focus on minimizing the pixel-wise reconstruction error which
results in a high peak signal-to-noise ratio. The enhanced images fail to
provide high-frequency details and are perceptually unsatisfying, i.e., they
fail to match the quality expected in a photo-realistic image. In this paper,
we present Image Enhancement Generative Adversarial Network (IEGAN), a
versatile framework capable of inferring photo-realistic natural images for
both artifact removal and super-resolution simultaneously. Moreover, we propose
a new loss function consisting of a combination of reconstruction loss, feature
loss and an edge loss counterpart. The feature loss helps to push the output
image to the natural image manifold and the edge loss preserves the sharpness
of the output image. The reconstruction loss provides low-level semantic
information to the generator regarding the quality of the generated images
compared to the original. Our approach has been experimentally proven to
recover photo-realistic textures from heavily compressed low-resolution images
on public benchmarks and our proposed high-resolution World100 dataset.Comment: Accepted at IEEE WACV 201
Deep Generative Adversarial Compression Artifact Removal
Compression artifacts arise in images whenever a lossy compression algorithm
is applied. These artifacts eliminate details present in the original image, or
add noise and small structures; because of these effects they make images less
pleasant for the human eye, and may also lead to decreased performance of
computer vision algorithms such as object detectors. To eliminate such
artifacts, when decompressing an image, it is required to recover the original
image from a disturbed version. To this end, we present a feed-forward fully
convolutional residual network model trained using a generative adversarial
framework. To provide a baseline, we show that our model can be also trained
optimizing the Structural Similarity (SSIM), which is a better loss with
respect to the simpler Mean Squared Error (MSE). Our GAN is able to produce
images with more photorealistic details than MSE or SSIM based networks.
Moreover we show that our approach can be used as a pre-processing step for
object detection in case images are degraded by compression to a point that
state-of-the art detectors fail. In this task, our GAN method obtains better
performance than MSE or SSIM trained networks.Comment: ICCV 2017 Camera Ready + Acknowledgement
Method for motion artifact reduction using a convolutional neural network for dynamic contrast enhanced MRI of the liver
Purpose: To improve the quality of images obtained via dynamic
contrast-enhanced MRI (DCE-MRI) that include motion artifacts and blurring
using a deep learning approach. Methods: A multi-channel convolutional neural
network (MARC) based method is proposed for reducing the motion artifacts and
blurring caused by respiratory motion in images obtained via DCE-MRI of the
liver. The training datasets for the neural network included images with and
without respiration-induced motion artifacts or blurring, and the distortions
were generated by simulating the phase error in k-space. Patient studies were
conducted using a multi-phase T1-weighted spoiled gradient echo sequence for
the liver containing breath-hold failures during data acquisition. The trained
network was applied to the acquired images to analyze the filtering
performance, and the intensities and contrast ratios before and after denoising
were compared via Bland-Altman plots. Results: The proposed network was found
to significantly reduce the magnitude of the artifacts and blurring induced by
respiratory motion, and the contrast ratios of the images after processing via
the network were consistent with those of the unprocessed images. Conclusion: A
deep learning based method for removing motion artifacts in images obtained via
DCE-MRI in the liver was demonstrated and validated.Comment: 11 pages, 6 figure
Blind Super-Resolution With Iterative Kernel Correction
Deep learning based methods have dominated super-resolution (SR) field due to
their remarkable performance in terms of effectiveness and efficiency. Most of
these methods assume that the blur kernel during downsampling is
predefined/known (e.g., bicubic). However, the blur kernels involved in real
applications are complicated and unknown, resulting in severe performance drop
for the advanced SR methods. In this paper, we propose an Iterative Kernel
Correction (IKC) method for blur kernel estimation in blind SR problem, where
the blur kernels are unknown. We draw the observation that kernel mismatch
could bring regular artifacts (either over-sharpening or over-smoothing), which
can be applied to correct inaccurate blur kernels. Thus we introduce an
iterative correction scheme -- IKC that achieves better results than direct
kernel estimation. We further propose an effective SR network architecture
using spatial feature transform (SFT) layers to handle multiple blur kernels,
named SFTMD. Extensive experiments on synthetic and real-world images show that
the proposed IKC method with SFTMD can provide visually favorable SR results
and the state-of-the-art performance in blind SR problem.Comment: Accepted by CVPR 2019. The Table 1 in the CVF camera ready version is
corrected in this pre-print versio
DeepLens: Shallow Depth Of Field From A Single Image
We aim to generate high resolution shallow depth-of-field (DoF) images from a
single all-in-focus image with controllable focal distance and aperture size.
To achieve this, we propose a novel neural network model comprised of a depth
prediction module, a lens blur module, and a guided upsampling module. All
modules are differentiable and are learned from data. To train our depth
prediction module, we collect a dataset of 2462 RGB-D images captured by mobile
phones with a dual-lens camera, and use existing segmentation datasets to
improve border prediction. We further leverage a synthetic dataset with known
depth to supervise the lens blur and guided upsampling modules. The
effectiveness of our system and training strategies are verified in the
experiments. Our method can generate high-quality shallow DoF images at high
resolution, and produces significantly fewer artifacts than the baselines and
existing solutions for single image shallow DoF synthesis. Compared with the
iPhone portrait mode, which is a state-of-the-art shallow DoF solution based on
a dual-lens depth camera, our method generates comparable results, while
allowing for greater flexibility to choose focal points and aperture size, and
is not limited to one capture setup.Comment: 11 pages, 15 figures, accepted by SIGGRAPH Asia 2018, low-resolution
versio
Demoir\'eing of Camera-Captured Screen Images Using Deep Convolutional Neural Network
Taking photos of optoelectronic displays is a direct and spontaneous way of
transferring data and keeping records, which is widely practiced. However, due
to the analog signal interference between the pixel grids of the display screen
and camera sensor array, objectionable moir\'e (alias) patterns appear in
captured screen images. As the moir\'e patterns are structured and highly
variant, they are difficult to be completely removed without affecting the
underneath latent image. In this paper, we propose an approach of deep
convolutional neural network for demoir\'eing screen photos. The proposed DCNN
consists of a coarse-scale network and a fine-scale network. In the
coarse-scale network, the input image is first downsampled and then processed
by stacked residual blocks to remove the moir\'e artifacts. After that, the
fine-scale network upsamples the demoir\'ed low-resolution image back to the
original resolution. Extensive experimental results have demonstrated that the
proposed technique can efficiently remove the moir\'e patterns for camera
acquired screen images; the new technique outperforms the existing ones
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