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- XVFI: eXtreme Video Frame Interpolation227
An extreme VFI network is proposed, called XVFI-Net, that first handles the VFI for 4K videos with large motion that performs comparably on the previous lower resolution benchmark dataset, which shows a robustness of the algorithm.
- A VOP generation tool: automatic segmentation of moving objects in image sequences based on spatio-temporal information179
An image segmentation method for separating moving objects from the background in image sequences using a combination of the spatial and temporal segmentation masks produces VOPs faithfully.
- PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report161
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones and proposes solutions that significantly improved baseline results defining the state-of-the-art for image enhancement on smartphones.
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- NTIRE 2020 Challenge on NonHomogeneous Dehazing125
This paper reviews the NTIRE 2020 Challenge on Non-Homogeneous Dehazing of images (restoration of rich details in hazy image) and proposed solutions gauge the state-of-the-art in image dehazing.
- Deep SR-ITM: Joint Learning of Super-Resolution and Inverse Tone-Mapping for 4K UHD HDR Applications117
This paper proposes a joint super-resolution (SR) and inverse tone-mapping (ITM) framework, called Deep SR-ITM, which learns the direct mapping from LR SDR video to their HR HDR version, and shows good subjective quality with increased contrast and details, outperforming the previous joint SR and ITM method.
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- JSI-GAN: GAN-Based Joint Super-Resolution and Inverse Tone-Mapping with Pixel-Wise Task-Specific Filters for UHD HDR Video93
This paper takes a divide-and-conquer approach in designing a novel GAN-based joint SR-ITM network, called JSI-GAN, which is composed of three task-specific subnets: an image reconstruction subnet, a detail restoration subnet and a local contrast enhancement (LCE) subnet.
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- KOALAnet: Blind Super-Resolution using Kernel-Oriented Adaptive Local Adjustment88
The proposed KOALAnet produces the most natural results for artistic photographs with intentional blur, which are not over-sharpened, by effectively handling images mixed with in-focus and out-of-focus areas.
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- FISR: Deep Joint Frame Interpolation and Super-Resolution with a Multi-Scale Temporal Loss75
A novel training scheme with a multi-scale temporal loss that imposes temporal regularization on the input video sequence, which can be applied to any general video-related task is proposed and analyzed in depth with extensive experiments.
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- Moving object segmentation in video sequences by user interaction and automatic object tracking64
A user-assisted video segmentation method for separating moving objects from the background in video sequences that exhibits a consistent and faithful segmentation performance for various types of video sequences is addressed.
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- PLADE-Net: Towards Pixel-Level Accuracy for Self-Supervised Single-View Depth Estimation with Neural Positional Encoding and Distilled Matting Loss55
The proposed PLADE-Net, naively extended for stereo inputs, outperforms the most recent self-supervised stereo methods, even without any advanced blocks like 1D correlations, 3D convolutions, or spatial pyramid pooling.
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- SIPSA-Net: Shift-Invariant Pan Sharpening with Moving Object Alignment for Satellite Imagery45
A novel framework called shift-invariant pan-sharpening with moving object alignment (SIPSA-Net) is proposed which is the first method to take into account such large misalignment of moving object regions for PAN sharpening.
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- UPSNet: Unsupervised Pan-Sharpening Network With Registration Learning Between Panchromatic and Multi-Spectral Images42
An unsupervised learning framework with registration learning for pan-sharpening, called UPSNet, which can be effectively trained in the original scales, and implicitly learns the registration between PAN and MS images without any dedicatedly designed registration module involved.
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- Super-Interpolation With Edge-Orientation-Based Mapping Kernels for Low Complex Upscaling37
The experimental results show that the SI method yields the smallest running time and requires relatively small hardware resources, and outperforms the six state-of-the-art methods in average (peak signal-to-noise ratio) PSNR/(structural similarity) SSIM, and exhibits competitive or somewhat lower PSNR/SSIM performance compared with the others.
- Moving Object Tracking in H.264/AVC Bitstream37
This work proposes a method for tracking the target object by using partially decoded texture data and motion vectors extracted directly from H.264/AVC bitstream which achieves low computational complexity and high performance through the dissimilarity energy minimization algorithm.
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- AIM 2020 Challenge on Video Temporal Super-Resolution22
This paper reports the second AIM challenge on Video Temporal Super-Resolution (VTSR), a.k.a. frame interpolation, with a focus on the proposed solutions, results, and analysis, and proposes the enhanced quadratic video interpolation method.
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- AIM 2019 Challenge on Video Temporal Super-Resolution: Methods and Results20
This paper reviews the first AIM challenge on video temporal super-resolution (frame interpolation) with a focus on the proposed solutions and results, and employs the REDS_VTSR dataset derived from diverse videos captured in a hand-held camera for training and evaluation purposes.
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- Fast decision of CU partitioning based on SAO parameter, motion and PU/TU split information for HEVC18
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- ProPaCoL-Net: A Novel Recursive Stereo Image SR Network with Progressive Parallax Coherency Learning–
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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