Magnetic Resonance Imaging (MRI) is a valuable medical imaging technology, while it suffers from a long acquisition time. Various methods have been proposed to reconstruct sharp images from undersampled k-space data to reduce imaging time. ...

• Leverage high-frequency priors to boost the MRI reconstruction.
• Develop an effective block to utilize high-frequency information.
• Design a novel HFGN with complex convolution for complex-valued MRI data.

Knowledge distillation (KD), known for its ability to transfer knowledge from a cumbersome network (teacher) to a lightweight one (student) without altering the architecture, has been garnering increasing attention. Two primary categories emerge ...

Stereo matching is a fundamental research area in the field of computer vision. In recent years, iterative methods based on Gated Recurrent Units (GRUs) have showcased remarkable achievements in this domain. Despite their high accuracy, these ...

• We propose an innovative uncertainty-based framework with coarse-to-fine manner that produces more accurate disparity with less iterations.
• We introduce a high-resolution Pixel Difference Convolution (PDC)-based refinement module to ...

Deep learning-based image interpolation methods are confronted with various challenges in its application to anisotropic medical volumetric data (i.e., CT and MR images) out of the complex nonlinear deformation and the scarcity of high-quality ...

Multi-Contrast Magnetic Resonance Imaging (MCMRI) utilizes the short-time reference image to facilitate the reconstruction of the long-time target one, providing a new solution for fast MRI. Although various methods have been proposed, they still have ...

Pansharpening aims to sharpen low resolution (LR) multispectral (MS) images with the help of corresponding high resolution (HR) panchromatic (PAN) images to obtain HRMS images. Model-based pansharpening methods manually design objective functions via ...

The prior domain knowledge, i.e., the regularity property of continuous multiobjective optimization problems (MOPs), could be learned to guide the search for evolutionary multiobjective optimization. This paper proposes a learning-to-guide strategy (LGS) ...

Images taken under the low-light condition often contain blur and saturated pixels at the same time. Deblurring images with saturated pixels is quite challenging. Because of the limited dynamic range, the saturated pixels are usually clipped in ...

Magnetic resonance imaging (MRI) tasks often involve multiple contrasts. Recently, numerous deep learning-based multi-contrast MRI super-resolution (SR) and reconstruction methods have been proposed to explore the complementary information from the multi-...

Flow-based and deformable convolution (DConv)-based methods are two mainstream approaches for solving the video frame interpolation (VFI) problem, which have made remarkable progress with the development of deep convolutional networks over the past years. ...

Developing and integrating advanced image sensors with novel algorithms in camera systems is prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research ...

With the use of convolutional neural networks, Single-Image Super-Resolution (SISR) has advanced dramatically in recent years. However, we notice a phenomenon that the structure of all these models must be consistent during training ...

• We propose a new patch-based model via non-convex weighted Smoothly Clipped Absolute Deviation (SCAD) prior for compressive sensing.

The nuclear norm-based convex surrogate of the rank function has been widely used in compressive sensing (CS) to exploit the sparsity of nonlocal similar patches in an image. However, this method treats different singular values ...

Cervical cancer is one of the leading causes of female-specific cancer death. Tumor region segmentation plays a pivotal role in both the clinical analysis and treatment planning of cervical cancer. Due to the heterogeneity and low ...

• We propose the augmented multiscale network (AugMS-Net) to address the small tumors recognition problem.

Single image dehazing is a challenging ill-posed problem that has drawn significant attention in the last few years. Recently, convolutional neural networks have achieved great success in image dehazing. However, it is still difficult for these ...

It has been demonstrated that the blurring process reduces the high-frequency information of the original sharp image, so the main challenge for image deblurring is to reconstruct high-frequency information from the blurry image. In this paper, we propose ...

Under stereo settings, the problem of image super-resolution (SR) and disparity estimation are interrelated that the result of each problem could help to solve the other. The effective exploitation of correspondence between different views facilitates ...

Existing arts have shown promising efforts to deal with the blind deblurring task. However, most of the recent works assume the additive noise involved in the blurring process to be simple-distributed (i.e. Gaussian or Laplacian), while the real-...${}_{\mathrm{}}$${}_{}$${}_{\mathrm{}}$${}_{\mathrm{}}$${}_{}$${}_{\mathrm{}}$${}_{}$

Blind deblurring methods are sensitive to outliers, such as saturated pixels and non-Gaussian noise. Even a small amount of outliers can dramatically degrade the quality of the estimated blur kernel, because the outliers are not conforming to the ...

Interference between the grids of the camera sensor and the screen cause moiré patterns to always appear on photographs captured from a screen, significantly affecting people’s ability to review images. We propose a novel method to ...