Image and Video Processing (eess.IV)

Abstract: Purpose: Obtaining manual annotations to train deep learning (DL) models for auto-segmentation is often time-consuming. Uncertainty-based Bayesian active learning (BAL) is a widely-adopted method to reduce annotation efforts. Based on BAL, this study introduces a hybrid representation-enhanced sampling strategy that integrates density and diversity criteria to save manual annotation costs by efficiently selecting the most informative samples. Methods: The experiments are performed on two lower extremity (LE) datasets of MRI and CT images by a BAL framework based on Bayesian U-net. Our method selects uncertain samples with high density and diversity for manual revision, optimizing for maximal similarity to unlabeled instances and minimal similarity to existing training data. We assess the accuracy and efficiency using Dice and a proposed metric called reduced annotation cost (RAC), respectively. We further evaluate the impact of various acquisition rules on BAL performance and design an ablation study for effectiveness estimation. Results: The proposed method showed superiority or non-inferiority to other methods on both datasets across two acquisition rules, and quantitative results reveal the pros and cons of the acquisition rules. Our ablation study in volume-wise acquisition shows that the combination of density and diversity criteria outperforms solely using either of them in musculoskeletal segmentation. Conclusion: Our sampling method is proven efficient in reducing annotation costs in image segmentation tasks. The combination of the proposed method and our BAL framework provides a semi-automatic way for efficient annotation of medical image datasets.

Abstract: In this paper, we show that processor events like instruction counts or cache misses can be used to accurately estimate the processing energy of software video decoders. Therefore, we perform energy measurements on an ARM-based evaluation platform and count processor level events using a dedicated profiling software. Measurements are performed for various codecs and decoder implementations to prove the general viability of our observations. Using the estimation method proposed in this paper, the true decoding energy for various recent video coding standards including HEVC and VP9 can be estimated with a mean estimation error that is smaller than 6%.

Abstract: The increasing prevalence of mobile devices has led to significant advancements in mobile camera systems and improved image quality. Nonetheless, mobile photography still grapples with challenging issues such as scattering and reflective flare. The absence of a comprehensive real image dataset tailored for mobile phones hinders the development of effective flare mitigation techniques. To address this issue, we present a novel raw image dataset specifically designed for mobile camera systems, focusing on flare removal. Capitalizing on the distinct properties of raw images, this dataset serves as a solid foundation for developing advanced flare removal algorithms. It encompasses a wide variety of real-world scenarios captured with diverse mobile devices and camera settings. The dataset comprises over 2,000 high-quality full-resolution raw image pairs for scattering flare and 1,100 for reflective flare, which can be further segmented into up to 30,000 and 2,200 paired patches, respectively, ensuring broad adaptability across various imaging conditions. Experimental results demonstrate that networks trained with synthesized data struggle to cope with complex lighting settings present in this real image dataset. We also show that processing data through a mobile phone's internal ISP compromises image quality while using raw image data presents significant advantages for addressing the flare removal problem. Our dataset is expected to enable an array of new research in flare removal and contribute to substantial improvements in mobile image quality, benefiting mobile photographers and end-users alike.

Abstract: Histological whole slide images (WSIs) can be usually compromised by artifacts, such as tissue folding and bubbles, which will increase the examination difficulty for both pathologists and Computer-Aided Diagnosis (CAD) systems. Existing approaches to restoring artifact images are confined to Generative Adversarial Networks (GANs), where the restoration process is formulated as an image-to-image transfer. Those methods are prone to suffer from mode collapse and unexpected mistransfer in the stain style, leading to unsatisfied and unrealistic restored images. Innovatively, we make the first attempt at a denoising diffusion probabilistic model for histological artifact restoration, namely ArtiFusion.Specifically, ArtiFusion formulates the artifact region restoration as a gradual denoising process, and its training relies solely on artifact-free images to simplify the training complexity.Furthermore, to capture local-global correlations in the regional artifact restoration, a novel Swin-Transformer denoising architecture is designed, along with a time token scheme. Our extensive evaluations demonstrate the effectiveness of ArtiFusion as a pre-processing method for histology analysis, which can successfully preserve the tissue structures and stain style in artifact-free regions during the restoration. Code is available at https://github.com/zhenqi-he/ArtiFusion.

Abstract: As the world progresses in technology and health, awareness of disease by revealing asymptomatic signs improves. It is important to detect and treat tumors in early stage as it can be life-threatening. Computer-aided technologies are used to overcome lingering limitations facing disease diagnosis, while brain tumor segmentation remains a difficult process, especially when multi-modality data is involved. This is mainly attributed to ineffective training due to lack of data and corresponding labelling. This work investigates the feasibility of employing deep-fake image generation for effective brain tumor segmentation. To this end, a Generative Adversarial Network was used for image-to-image translation for increasing dataset size, followed by image segmentation using a U-Net-based convolutional neural network trained with deepfake images. Performance of the proposed approach is compared with ground truth of four publicly available datasets. Results show improved performance in terms of image segmentation quality metrics, and could potentially assist when training with limited data.