任务导向流：提升视频超分与去噪的艺术

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"《基于任务导向流的视频增强：深度学习新进阶》本文介绍了一项关于视频处理领域的创新工作，特别是在低帧率视频的提升和去噪方面。视频超分（VideoEnhancement）是通过深度学习技术增强低分辨率视频质量的重要应用，而任务导向流（Task-OrientedFlow）则是解决这一问题的关键突破。传统的光学流估计方法，如EpicFlow[28]，虽然能精确预测物体运动，但其在特定任务上的性能可能并不理想，如帧插值时可能会导致诸如模糊边缘或失真物体细节等问题，如图1中的(I-c)所示。任务导向流方法旨在针对不同任务定制更精准的流估计。相比于EpicFlow，它能够减少因估计误差产生的视觉干扰，例如在帧插值过程中，(I-d)所示的任务导向流能够消除像手指那样模糊不清的细节，如图1的(I-e)所示。这种方法的优势在于它考虑了具体任务的需求，从而提供更高质量的视频插值效果。对于视频去噪任务，任务导向流同样展现出了优势。如图1的(II-b)和(II-d)所示，当用EpicFlow进行去噪时，尽管整体运动估计准确，但可能存在残留的噪声。然而，使用任务导向流进行去噪后，(II-e)的结果表明，图像的清晰度得到了显著提升，即使在输入是噪声视频的情况下也能获得更纯净的输出，如(II-a)所示。总结来说，本文的核心内容是深度学习在视频处理中的一个新进展——任务导向流技术，它通过优化流估计以适应特定任务需求，有效改善了视频帧插值和去噪的质量，使得视频处理的性能迈上了新的台阶。这是一项具有广泛应用潜力的技术，对于提高视频内容的质量和用户体验具有重要意义。"

super-resolution.

Temporal Frame Interpolation

Given a low frame rate

video, a temporal frame interpolation algorithm generates

a high frame rate video by synthesizing additional frames

between two temporally neighboring frames. Speciﬁcally, let

and

be two consecutive frames in an input video, the

task is to estimate the missing middle frame

. Temporal

frame interpolation doubles the video frame rate, and can be

recursively applied for even higher frame rates.

Video Denoising/Deblocking

Given a degraded video with

artifacts from either the sensor or compression, video denois-

ing/deblocking aims to remove the noise or compression arti-

facts to recover the original video. This is typically done by

aggregating information from neighboring frames. Speciﬁ-

cally, Let

, ...,

be N-consecutive frames in an input

video, the task of video denoising is to estimate the middle

frame

∗

ref

given degraded frames

, ...,

as input. For

the ease of description, in the rest of paper, we simply call

both tasks as video denoising.

Video Super-Resolution (SR)

Similar to video denoising,

given

consecutive low-resolution frames as input, the task

of video super-resolution is to recover the high-resolution

middle frame. In this work, we ﬁrst upsample all the input

frames to the same resolution of the output using bicubic

interpolation, and our algorithm only needs to recover the

high-frequency component in the output image.

4. Task-Oriented Flow for Video Processing

Most motion-based video processing algorithms has two

steps: motion estimation and image processing. For example,

in temporal frame interpolation, most algorithms ﬁrst estimate

how pixels move between input frames (frame 1 and 3), and

then move pixels to the estimated location in the output frame

(frame 2) [

]. Similarly, in video denoising, algorithms ﬁrst

between them, and then remove noises by aggregating infor-

mation from registered frames.

In this paper, we propose to use task-oriented ﬂow

(TOFlow) to integrate the two steps. To learn the task-oriented

ﬂow, we design an end-to-end trainable network with three

parts (Figure 3): a ﬂow estimation module that estimates the

movement of pixels between input frames; an image transfor-

mation module that warps all the frames to a reference frame;

and a task-speciﬁc image processing module that performs

video interpolation, denoising, or super-resolution on regis-

tered frames. Because the ﬂow estimation module is jointly

trained with the rest of the network, it learns to predict a ﬂow

ﬁeld that ﬁts to a particular task.

4.1. Toy Example

Before discussing the details of network structure, we ﬁrst

start with two synthetic sequences to demonstrate why our

TOFlow can outperform traditional optical ﬂows. The left

Case I: With Ground Truth Flows

Case II: With Task-Oriented Flows

Input frames

TOFlow

Warped by

TOFlow

Denoised

frame

Video Denoising

Case I: With Ground Truth Flows

Case II: With Task-Oriented Flows

Input frames

TOFlow

Warped by

TOFlow

Interpolated

frame

Frame Interpolation

Warped by

TOFlow

Denoised

frame

GT flow Warped by

GT flow

Interpolated

frame

GT flow

Figure 2: A toy example that demonstrates the effectiveness

of task oriented ﬂow over the traditional optical ﬂow. See

Section 4.1 for details.

of Figure 2 shows an example of frame interpolation, where

a green triangle is moving to the bottom in front of a black

background. If we warp both the ﬁrst and third frames to the

second, even using the ground truth ﬂow (Case I, left column),

there is an obvious doubling artifact in the warped frames due

to occlusion (Case I, middle column), which is a well-known

problem in the optical ﬂow literature [

]. The ﬁnal interpola-

tion result based on these two warp frames still contains this

artifact (Case I, right column). In contrast, TOFlow does not

stick to object motion: the background should be static, but

it has non-zero motion (Case II, left column). With TOFlow,

however, there is barely any artifact in the warped frames

(Case II, middle column) and the interpolated frame looks

clean (Case II, right column). The hallucinated background

motion actually helps to reduce the doubling artifacts. This

shows that TOFlow can reduce errors and synthesize frames

better than the ground truth ﬂow.

Similarly, on the right of Figure 2, we show an example of

video denoising. The random small boxes in the input frames

are synthetic noises. If we warp the ﬁrst and the third frames

to the second using the ground truth ﬂow, the noisy patterns

(random squares) remain, and the denoised frame still contains

some noise (Case I, right column. There are some shadows of

boxes on the bottom). But if we warp these two frames using

TOFlow (Case II, left column), those noisy patterns are also

reduced or eliminated (Case II, middle column), and the ﬁnal

denoised frame base on them contains almost no noise. This

also shows that TOFlow learns to reduce the noise in input

frames by inpainting them with neighboring pixels, which

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