We introduce DRHDR, a Dual branch Residual Convolutional Neural Network for
Multi-Bracket HDR Imaging. To address the challenges of fusing multiple
brackets from dynamic scenes, we propose an efficient dual branch network that
operates on two different resolutions. The full resolution branch uses a
Deformable Convolutional Block to align features and retain high-frequency
details. A low resolution branch with a Spatial Attention Block aims to attend
wanted areas from the non-reference brackets, and suppress displaced features
that could incur on ghosting artifacts. By using a dual branch approach we are
able to achieve high quality results while constraining the computational
resources required to estimate the HDR results.

本文介绍了 DRHDR，一种用于多支架 HDR 成像的双分支残差卷积神经网络，可以在保留高频细节的同时，通过使用变形卷积块对特征进行对齐，并通过空间注意力块来抑制跟踪失误的造成的鬼影区域，同时能够保持计算效率。

DRHDR：用于多层括号高动态范围成像的双分支残差网络

DRHDR: A Dual branch Residual Network for Multi-Bracket High Dynamic  Range Imaging

Although wireless capsule endoscopy is the preferred modality for diagnosis
and assessment of small bowel diseases, the poor camera resolution is a
substantial limitation for both subjective and automated diagnostics.
Enhanced-resolution endoscopy has shown to improve adenoma detection rate for
conventional endoscopy and is likely to do the same for capsule endoscopy. In
this work, we propose and quantitatively validate a novel framework to learn a
mapping from low-to-high resolution endoscopic images. We combine conditional
adversarial networks with a spatial attention block to improve the resolution
by up to factors of 8x, 10x, 12x, respectively. Quantitative and qualitative
studies performed demonstrate the superiority of EndoL2H over state-of-the-art
deep super-resolution methods DBPN, RCAN and SRGAN. MOS tests performed by 30
gastroenterologists qualitatively assess and confirm the clinical relevance of
the approach. EndoL2H is generally applicable to any endoscopic capsule system
and has the potential to improve diagnosis and better harness computational
approaches for polyp detection and characterization. Our code and trained
models are available at this https URL

本研究提出并定量验证了从低分辨率到高分辨率内窥镜图像的映射的新框架，并结合条件对抗网络和空间注意力块将分辨率提高了 8 倍至 12 倍，该方法被 30 名胃肠病专家定性评估并确认具有临床重要性，其具有潜力提高诊断精度并更好地利用计算方法进行息肉检测和表征。

EndoL2H：胶囊内窥镜深度超分辨率

EndoL2H: Deep Super-Resolution for Capsule Endoscopy

Deep learning based image Super-Resolution (SR) has shown rapid development
due to its ability of big data digestion. Generally, deeper and wider networks
can extract richer feature maps and generate SR images with remarkable quality.
However, the more complex network we have, the more time consumption is
required for practical applications. It is important to have a simplified
network for efficient image SR. In this paper, we propose an Attention based
Back Projection Network (ABPN) for image super-resolution. Similar to some
recent works, we believe that the back projection mechanism can be further
developed for SR. Enhanced back projection blocks are suggested to iteratively
update low- and high-resolution feature residues. Inspired by recent studies on
attention models, we propose a Spatial Attention Block (SAB) to learn the
cross-correlation across features at different layers. Based on the assumption
that a good SR image should be close to the original LR image after
down-sampling. We propose a Refined Back Projection Block (RBPB) for final
reconstruction. Extensive experiments on some public and AIM2019 Image
Super-Resolution Challenge datasets show that the proposed ABPN can provide
state-of-the-art or even better performance in both quantitative and
qualitative measurements.

本研究提出一种基于注意力机制的反向投影网络（ABPN）用于图像超分辨率，使用增强的反向投影块提高低分辨率特征残差，结合空间注意力块（SAB）学习不同层次中特征之间的交叉相关性，并加入完善后的反向投影块（RBPB）进行重建，实验结果表明该方法在定量和定性指标上均取得了最新的优秀结果。