Non-Iterative Tone Mapping With High Efficiency and Robustness

This paper proposes an efficient approach for tone mapping, which provides a high perceptual image quality for diverse scenes. Most existing methods, optimizing images for the perceptual model, use an iterative process and this process is time consuming. To solve this problem, we proposed a new layer-based non-iterative approach to finding an optimal detail layer for generating a tone-mapped image. The proposed method consists of the following three steps. First, an image is decomposed into a base layer and a detail layer to separate the illumination and detail components. Next, the base layer is globally compressed by applying the statistical naturalness model based on the statistics of the luminance and contrast in the natural scenes. The detail layer is locally optimized based on the structure fidelity measure, representing the degree of local structural detail preservation. Finally, the proposed method constructs the final tone-mapped image by combining the resultant layers. The performance evaluation reveals that the proposed method outperforms the benchmarking methods for almost all the benchmarking test images. Specifically, the proposed method improves an average tone mapping quality index-II (TMQI-II), a feature similarity index for tone-mapped images (FSITM), and a high-dynamic range-visible difference predictor (HDR-VDP)-2.2 by up to 0.651 (223.4%), 0.088 (11.5%), and 10.371 (25.2%), respectively, compared with the benchmarking methods, whereas it improves the processing speed by over 2611 times. Furthermore, the proposed method decreases the standard deviations of TMQI-II, FSITM, and HDR-VDP-2.2, and processing time by up to 81.4%, 18.9%, 12.6%, and 99.9%, respectively, when compared with the benchmarking methods.11Ysciescopu

Segmentation-based disparity refinement

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BlockNet: A Deep Neural Network for Block-Based Motion Estimation Using Representative Matching

Owing to the limitations of practical realizations, block-based motion is widely used as an alternative for pixel-based motion in video applications such as global motion estimation and frame rate up-conversion. We hereby present BlockNet, a compact but effective deep neural architecture for block-based motion estimation. First, BlockNet extracts rich features for a pair of input images. Then, it estimates coarse-to-fine block motion using a pyramidal structure. In each level, block-based motion is estimated using the proposed representative matching with a simple average operator. The experimental results show that BlockNet achieved a similar average end-point error with and without representative matching, whereas the proposed matching incurred 18% lower computational cost than full matching.11Ysciescopu

Biochemical characterization of type I-E anti-CRISPR proteins, AcrIE2 and AcrIE4

Abstract In bacteria and archaea, CRISPRs and Cas proteins constitute an adaptive immune system against invading foreign genetic materials, such as bacteriophages and plasmids. To counteract CRISPR-mediated immunity, bacteriophages encode anti-CRISPR (Acr) proteins that neutralize the host CRISPR–Cas systems. Several Acr proteins that act against type I-E CRISPR–Cas systems have been identified. Here, we describe the biochemical characterization of two type I-E Acr proteins, AcrIE2 and AcrIE4. We determined the crystal structure of AcrIE2 using single-wavelength anomalous diffraction and performed a structural comparison with the previously reported AcrIE2 structures solved by different techniques. Binding assays with type I-E Cas proteins were carried out for the target identification of AcrIE2. We also analyzed the interaction between AcrIE4 and its target Cas component using biochemical methods. Our findings corroborate and expand the knowledge on type I-E Acr proteins, illuminating diverse molecular mechanisms of inhibiting CRISPR-mediated prokaryotic anti-phage defense

The structure of AcrIE4-F7 reveals a common strategy for dual CRISPR inhibition by targeting PAM recognition sites

© 2022 The Author(s). Published by Oxford University Press on behalf of Nucleic Acids Research.Bacteria and archaea use the CRISPR-Cas system to fend off invasions of bacteriophages and foreign plasmids. In response, bacteriophages encode anti-CRISPR (Acr) proteins that potently inhibit host Cas proteins to suppress CRISPR-mediated immunity. AcrIE4-F7, which was isolated from Pseudomonas citronellolis, is a fused form of AcrIE4 and AcrIF7 that inhibits both type I-E and type I-F CRISPR-Cas systems. Here, we determined the structure of AcrIE4-F7 and identified its Cas target proteins. The N-terminal AcrIE4 domain adopts a novel α-helical fold that targets the PAM interaction site of the type I-E Cas8e subunit. The C-terminal AcrIF7 domain exhibits an αβ fold like native AcrIF7, which disables target DNA recognition by the PAM interaction site in the type I-F Cas8f subunit. The two Acr domains are connected by a flexible linker that allows prompt docking onto their cognate Cas8 targets. Conserved negative charges in each Acr domain are required for interaction with their Cas8 targets. Our results illustrate a common mechanism by which AcrIE4-F7 inhibits divergent CRISPR-Cas types.Y