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Found 17 papers, 11 papers with code
JDEC: JPEG Decoding via Enhanced Continuous Cosine Coefficients
We propose a practical approach to JPEG image decoding, utilizing a local implicit neural representation with continuous cosine formulation.
Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidance
These techniques are often not applicable in unconditional generation or in various downstream tasks such as image restoration.
BroadBEV: Collaborative LiDAR-camera Fusion for Broad-sighted Bird's Eye View Map Construction
The method boosts the learning of depth estimation of the camera branch and induces accurate location of dense camera features in BEV space.
Implicit Neural Image Stitching
Existing frameworks for image stitching often provide visually reasonable stitchings.
Learning Residual Elastic Warps for Image Stitching under Dirichlet Boundary Condition
Trendy suggestions for learning-based elastic warps enable the deep image stitchings to align images exposed to large parallax errors.
ABCD: Arbitrary Bitwise Coefficient for De-Quantization
Modern displays and contents support more than 8bits image and video.
B-Spline Texture Coefficients Estimator for Screen Content Image Super-Resolution
Our network outperforms both a transformer-based reconstruction and an implicit Fourier representation method in almost upscaling factor, thanks to the positive constraint and compact support of the B-spline basis.
Learning Local Implicit Fourier Representation for Image Warping
In this paper, we propose a local texture estimator for image warping (LTEW) followed by an implicit neural representation to deform images into continuous shapes.
Local Texture Estimator for Implicit Representation Function
Recent works with an implicit neural function shed light on representing images in arbitrary resolution.
Ranked #6 on
Image Super-Resolution
on Set5 - 3x upscaling
Reducing the Human Effort in Developing PET-CT Registration
We aim to reduce the tedious nature of developing and evaluating methods for aligning PET-CT scans from multiple patient visits.
Time-Dependent Deep Image Prior for Dynamic MRI
The key ingredients of our method are threefold: 1) a fixed low-dimensional manifold that encodes the temporal variations of images; 2) a network that maps the manifold into a more expressive latent space; and 3) a convolutional neural network that generates a dynamic series of MRI images from the latent variables and that favors their consistency with the measurements in k-space.
Self-Supervised Deep Active Accelerated MRI
The reconstruction network is trained to give the highest reconstruction quality, given the MCTS sampling pattern.
A Review of Convolutional Neural Networks for Inverse Problems in Imaging
In this survey paper, we review recent uses of convolution neural networks (CNNs) to solve inverse problems in imaging.
CNN-Based Projected Gradient Descent for Consistent Image Reconstruction
When the projector is replaced with a CNN, we propose a relaxed PGD, which always converges.
Predicting Cognitive Decline with Deep Learning of Brain Metabolism and Amyloid Imaging
For effective treatment of Alzheimer disease (AD), it is important to identify subjects who are most likely to exhibit rapid cognitive decline.
Deep Convolutional Neural Network for Inverse Problems in Imaging
The starting point of our work is the observation that unrolled iterative methods have the form of a CNN (filtering followed by point-wise non-linearity) when the normal operator (H*H, the adjoint of H times H) of the forward model is a convolution.
Sparse + Low Rank Decomposition of Annihilating Filter-based Hankel Matrix for Impulse Noise Removal
The new approach, what we call the robust ALOHA, is motivated by the observation that an image corrupted with impulse noises has intact pixels; so the impulse noises can be modeled as sparse components, whereas the underlying image can be still modeled using a low-rank Hankel structured matrix.
