Blind Image Decomposition

Han, Junlin; Li, Weihao; Fang, Pengfei; Sun, Chunyi; Hong, Jie; Armin, Mohammad Ali; Li, Hongdong

doi:10.1007/978-3-031-19797-0_13

Cited by 13 publications

(8 citation statements)

References 83 publications

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“…For , and α is set to 0.002. In addition to the default settings, we have also developed a data augmentation strategy to introduce multiple degradation types for prompt learning, which is similar to CutMix [22,67]. This is not involved by default for fair comparison.…”

Section: Methodsmentioning

confidence: 99%

“…Despite the challenges of the task, we explore the potential of training the restoration model using multiple prompts with data augmentation. Inspired by augmentation techniques that blend multiple images into one [67], or apply various enhancements to a single image [22], and can improve the classification performance, we design an augmentation strategy for training called Degradation-Mix (DMIX), as shown in Fig. S2.…”

Section: C2 Training With Multiple Promptsmentioning

confidence: 99%

“…S2. Similar to Cut-Mix [22], we first split all images in a mini-batch into two halves, either horizontally or vertically. These halves are then randomly shuffled to create new images with combined degradations.…”

Section: C2 Training With Multiple Promptsmentioning

confidence: 99%

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Transfer Learning in Motor Imagery Brain Computer Interface: A Review

2022

J. Shanghai Jiaotong Univ. (Sci.)

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Sparse-view computed tomography (CT)-using a small number of projections for tomographic reconstructionenables much lower radiation dose to patients and accelerated data acquisition. The reconstructed images, however, suffer from strong artifacts, greatly limiting their diagnostic value. Current trends for sparse-view CT turn to the raw data for better information recovery. The resultant dual-domain methods, nonetheless, suffer from secondary artifacts, especially in ultra-sparse view scenarios, and their generalization to other scanners/protocols is greatly limited. A crucial question arises: have the image post-processing methods reached the limit? Our answer is not yet. In this paper, we stick to image post-processing methods due to great flexibility and propose global representation(GloRe) distillation framework for sparse-view CT, termed GloReDi. First, we propose to learn GloRe with Fourier convolution, so each element in GloRe has an image-wide receptive field. Second, unlike methods that only use the full-view images for supervision, we propose to distill GloRe from intermediate-view reconstructed images that are readily available but not explored in previous literature. The success of GloRe distillation is attributed to two key components: representation directional distillation to align the GloRe directions, and band-pass-specific contrastive distillation to gain clinically important details. Extensive experiments demonstrate the superiority of the proposed GloReDi over the state-of-the-art methods, including dual-domain ones. The source code is available at https: //github.com/longzilicart/GloReDi.

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Section: Methodsmentioning

confidence: 99%

Section: C2 Training With Multiple Promptsmentioning

confidence: 99%

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Transfer Learning in Motor Imagery Brain Computer Interface: A Review

2022

J. Shanghai Jiaotong Univ. (Sci.)

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“…The choice of |τ | = 10 transformations is selected based on the domain knowledge of possible transformations that occur in endoscopic videos. Thus, we have excluded all the augmentations that, when applied, result in drastically different looking images that are highly unlikely to arise in surgical videos, such as posterize, solarize and equalize used in [24] and other novel augmentations proposed in the literature: YOCO [29], MixUp [30], CutMix [31] or AugMix [32].…”

Section: Trandaugmentmentioning

confidence: 99%

TRandAugment: temporal random augmentation strategy for surgical activity recognition from videos

et al. 2023

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Purpose Automatic recognition of surgical activities from intraoperative surgical videos is crucial for developing intelligent support systems for computer-assisted interventions. Current state-of-the-art recognition methods are based on deep learning where data augmentation has shown the potential to improve the generalization of these methods. This has spurred work on automated and simplified augmentation strategies for image classification and object detection on datasets of still images. Extending such augmentation methods to videos is not straightforward, as the temporal dimension needs to be considered. Furthermore, surgical videos pose additional challenges as they are composed of multiple, interconnected, and long-duration activities. Methods This work proposes a new simplified augmentation method, called TRandAugment, specifically designed for long surgical videos, that treats each video as an assemble of temporal segments and applies consistent but random transformations to each segment. The proposed augmentation method is used to train an end-to-end spatiotemporal model consisting of a CNN (ResNet50) followed by a TCN. Results The effectiveness of the proposed method is demonstrated on two surgical video datasets, namely Bypass40 and CATARACTS, and two tasks, surgical phase and step recognition. TRandAugment adds a performance boost of 1–6% over previous state-of-the-art methods, that uses manually designed augmentations. Conclusion This work presents a simplified and automated augmentation method for long surgical videos. The proposed method has been validated on different datasets and tasks indicating the importance of devising temporal augmentation methods for long surgical videos.

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“…All images in the training dataset go through image augmentation. In addition to increasing the dataset, the augmentation encourages the model to recognize pointing in diverse and noisy environments, and also given partial information [42]. Hence, the model will be able to perform well in changing environments which include lighting variations and occlusions.…”

Section: Data Collectionmentioning

confidence: 99%

Robust Multi-User In-Hand Object Recognition in Human-Robot Collaboration Using a Wearable Force-Myography Device

Bamani

Kahanowich

Ben-David

et al. 2022

IEEE Robot. Autom. Lett.

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In communication between humans, gestures are often preferred or complementary to verbal expression since the former offers better spatial referral. Finger pointing gesture conveys vital information regarding some point of interest in the environment. In human-robot interaction, a user can easily direct a robot to a target location, for example, in search and rescue or factory assistance. State-of-the-art approaches for visual pointing estimation often rely on depth cameras, are limited to indoor environments and provide discrete predictions between limited targets. In this paper, we explore the learning of models for robots to understand pointing directives in various indoor and outdoor environments solely based on a single RGB camera. A novel framework is proposed which includes a designated model termed PointingNet. PointingNet recognizes the occurrence of pointing followed by approximating the position and direction of the index finger. The model relies on a novel segmentation model for masking any lifted arm. While state-of-the-art human pose estimation models provide poor pointing angle estimation accuracy of 28 • , PointingNet exhibits mean accuracy of less than 2 • . With the pointing information, the target is computed followed by planning and motion of the robot. The framework is evaluated on two robotic systems yielding accurate target reaching.

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Blind Image Decomposition

Cited by 13 publications

References 83 publications

Transfer Learning in Motor Imagery Brain Computer Interface: A Review

Transfer Learning in Motor Imagery Brain Computer Interface: A Review

TRandAugment: temporal random augmentation strategy for surgical activity recognition from videos

Robust Multi-User In-Hand Object Recognition in Human-Robot Collaboration Using a Wearable Force-Myography Device

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