Discrete cosine transform network for guided depth map super-resolution
Guided depth super-resolution (GDSR) is an essential topic in multi-modal image
processing, which reconstructs high-resolution (HR) depth maps from low-resolution ones …
processing, which reconstructs high-resolution (HR) depth maps from low-resolution ones …
Spherical space feature decomposition for guided depth map super-resolution
Guided depth map super-resolution (GDSR), as a hot topic in multi-modal image processing,
aims to upsample low-resolution (LR) depth maps with additional information involved in …
aims to upsample low-resolution (LR) depth maps with additional information involved in …
Deep convolutional neural network for multi-modal image restoration and fusion
X Deng, PL Dragotti - IEEE transactions on pattern analysis …, 2020 - ieeexplore.ieee.org
In this paper, we propose a novel deep convolutional neural network to solve the general
multi-modal image restoration (MIR) and multi-modal image fusion (MIF) problems. Different …
multi-modal image restoration (MIR) and multi-modal image fusion (MIF) problems. Different …
Towards fast and accurate real-world depth super-resolution: Benchmark dataset and baseline
Depth maps obtained by commercial depth sensors are always in low-resolution, making it
difficult to be used in various computer vision tasks. Thus, depth map super-resolution (SR) …
difficult to be used in various computer vision tasks. Thus, depth map super-resolution (SR) …
PMBANet: Progressive multi-branch aggregation network for scene depth super-resolution
Depth map super-resolution is an ill-posed inverse problem with many challenges. First,
depth boundaries are generally hard to reconstruct particularly at large magnification factors …
depth boundaries are generally hard to reconstruct particularly at large magnification factors …
Learning graph regularisation for guided super-resolution
We introduce a novel formulation for guided super-resolution. Its core is a differentiable
optimisation layer that operates on a learned affinity graph. The learned graph potentials …
optimisation layer that operates on a learned affinity graph. The learned graph potentials …
A novel complex-valued convolutional neural network for medical image denoising
Several applications of complex-valued networks have been reported for computer vision
tasks like image processing and classification. However, complex-valued convolutional …
tasks like image processing and classification. However, complex-valued convolutional …
Bridgenet: A joint learning network of depth map super-resolution and monocular depth estimation
Depth map super-resolution is a task with high practical application requirements in the
industry. Existing color-guided depth map super-resolution methods usually necessitate an …
industry. Existing color-guided depth map super-resolution methods usually necessitate an …
Learning complementary correlations for depth super-resolution with incomplete data in real world
Depth information is a significant ingredient to visually perceive the physical world.
However, mainstream depth sensors, eg, time-of-flight (ToF) cameras, often measure …
However, mainstream depth sensors, eg, time-of-flight (ToF) cameras, often measure …
Learning scene structure guidance via cross-task knowledge transfer for single depth super-resolution
Existing color-guided depth super-resolution (DSR) approaches require paired RGB-D data
as training examples where the RGB image is used as structural guidance to recover the …
as training examples where the RGB image is used as structural guidance to recover the …