Defocus blur detection (DBD) separates in-focus and out-of-focus regions in
an image. Previous approaches mistakenly mistook homogeneous areas in focus for
defocus blur regions, likely due to not considering the internal factors that
cause defocus blur. Inspired by the law of depth, depth of field (DOF), and
defocus, we propose an approach called D-DFFNet, which incorporates depth and
DOF cues in an implicit manner. This allows the model to understand the defocus
phenomenon in a more natural way. Our method proposes a depth feature
distillation strategy to obtain depth knowledge from a pre-trained monocular
depth estimation model and uses a DOF-edge loss to understand the relationship
between DOF and depth. Our approach outperforms state-of-the-art methods on
public benchmarks and a newly collected large benchmark dataset, EBD. Source
codes and EBD dataset are available at: https:github.com/yuxinjin-whu/D-DFFNet.

本文提出了一种名为 D-DFFNet 的方法来检测图片中的 defocus 模糊区域。该方法通过隐式地结合深度信息和 DOF-edge 损失函数来理解 defocus 现象，并通过从预训练的单眼深度估计模型中提取深度特征。在公开基准数据集和新的大型数据集 EBD 上，该方法优于现有的各种方法。

深度和景深线索让去焦模糊检测更好

Depth and DOF Cues Make A Better Defocus Blur Detector

Smartphone cameras today are increasingly approaching the versatility and
quality of professional cameras through a combination of hardware and software
advancements. However, fixed aperture remains a key limitation, preventing
users from controlling the depth of field (DoF) of captured images. At the same
time, many smartphones now have multiple cameras with different fixed apertures
-- specifically, an ultra-wide camera with wider field of view and deeper DoF
and a higher resolution primary camera with shallower DoF. In this work, we
propose $\text{DC}^2$, a system for defocus control for synthetically varying
camera aperture, focus distance and arbitrary defocus effects by fusing
information from such a dual-camera system. Our key insight is to leverage
real-world smartphone camera dataset by using image refocus as a proxy task for
learning to control defocus. Quantitative and qualitative evaluations on
real-world data demonstrate our system's efficacy where we outperform
state-of-the-art on defocus deblurring, bokeh rendering, and image refocus.
Finally, we demonstrate creative post-capture defocus control enabled by our
method, including tilt-shift and content-based defocus effects.

提出了一种利用双摄像头系统中的虚焦效应，并通过图像重构来了解虚焦控制的 $	ext {DC}^2$ 系统，能够优于现有的虚焦去模糊，背景虚化和图像重构技术，支持倾斜移位和基于内容的虚化效果。

DC²：通过学习重新聚焦的双摄像头散焦控制

$\text{DC}^2$: Dual-Camera Defocus Control by Learning to Refocus

Camera arrays provide spatial and angular information within a single
snapshot. With refocusing methods, focal planes can be altered after exposure.
In this letter, we propose a light field refocusing method to improve the
imaging quality of camera arrays. In our method, the disparity is first
estimated. Then, the unfocused region (bokeh) is rendered by using a
depth-based anisotropic filter. Finally, the refocused image is produced by a
reconstruction-based superresolution approach where the bokeh image is used as
a regularization term. Our method can selectively refocus images with focused
region being superresolved and bokeh being aesthetically rendered. Our method
also enables postadjustment of depth of field. We conduct experiments on both
public and self-developed datasets. Our method achieves superior visual
performance with acceptable computational cost as compared to other
state-of-the-art methods. Code is available at
this https URL

提出了一种基于光场的方法来改善相机阵列的图像质量，使用视差进行对焦区域的估计，使用基于深度的各向异性滤波器渲染非对焦区域，再使用基于重建的超分辨率方法产生重点区域被超分辨率处理而非对焦区域审美化呈现的图像，使得景深可以进行后期调整，并表明该方法在公开及自建数据集上取得了优越的视觉表现。