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Found 5 papers, 4 papers with code
NTIRE 2023 Image Shadow Removal Challenge Technical Report: Team IIM_TTI
In this paper, we analyze and discuss ShadowFormer in preparation for the NTIRE2023 Shadow Removal Challenge [1], implementing five key improvements: image alignment, the introduction of a perceptual quality loss function, the semi-automatic annotation for shadow detection, joint learning of shadow detection and removal, and the introduction of new data augmentation technique "CutShadow" for shadow removal.
MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results
Small Object Detection (SOD) is an important machine vision topic because (i) a variety of real-world applications require object detection for distant objects and (ii) SOD is a challenging task due to the noisy, blurred, and less-informative image appearances of small objects.
Ranked #2 on
Small Object Detection
on SOD4SB Public Test
(using extra training data)
Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded Images
This paper proposes crack segmentation augmented by super resolution (SR) with deep neural networks.
Kernelized Back-Projection Networks for Blind Super Resolution
In our second model, the Kernelized BackProjection Network (KBPN), a raw kernel is estimated and directly employed for modeling the image degradation.
Crack Segmentation for Low-Resolution Images using Joint Learning with Super-Resolution
This paper proposes a method for crack segmentation on low-resolution images.
