高速双边滤波：高动态范围图像的展示与细节保留

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本文主要探讨了"Fast Bilateral Filtering for the Display of High-Dynamic-Range Images"这一主题，由弗雷多·杜朗(Frédéric Durand)和朱莉·多尔西(Julie Dorsey)在麻省理工学院计算机科学实验室提出。他们针对高动态范围(High-Dynamic-Range, HDR)图像的显示提出了一个创新技术，该技术旨在降低图像的对比度同时保持细节清晰。这项工作是基于图像的双尺度分解，将图像分为基础层(base layer)和细节层(detail layer)。基础层负责编码大尺度的变化，而细节层保留原始的细微差别。关键的核心部分是使用了一种边缘保持的滤波器，即双边滤波(bilateral filter)。双边滤波是一种非线性滤波器，其像素权重的计算结合了空间域中的高斯核(Gaussian kernel)和强度域中的影响函数(influence function)，该函数会根据像素间强度差异减小权重。这种设计确保了滤波过程在保持图像结构的同时，有效地降低了强度变化较大的区域的对比度。作者还将双边过滤与鲁棒统计学的框架相结合，揭示了它与各向异性扩散(anisotropic diffusion)的关系。这种方法通过在强度域中采用分段线性近似(partially linear approximation)和适当的子采样(subsampling)，实现了双边滤波速度的显著提升，达到了两个数量级的加速效果。这意味着处理HDR图像时，不仅图像质量得到保障，而且可以显著减少计算时间，提高了实时显示的性能。本文提供了对如何利用快速双边滤波技术进行高动态范围图像显示的深入理解，展示了如何在保持图像细节的同时处理高动态范围数据，以及如何通过优化算法实现高效的实时处理。这对于高动态范围图像处理、显示技术和实时应用有着重要的实际意义。

Fast Bilateral Filtering

for the Display of High-Dynamic-Range Images

Fr´edo Durand and Julie Dorsey

Laboratory for Computer Science, Massachusetts Institute of Technology

Abstract

We present a new technique for the display of high-dynamic-range

images, which reduces the contrast while preserving detail. It is

based on a two-scale decomposition of the image into a base layer,

encoding large-scale variations, and a detail layer. Only the base

layer has its contrast reduced, thereby preserving detail. The base

layer is obtained using an edge-preserving ﬁlter called the bilateral

ﬁlter. This is a non-linear ﬁlter, where the weight of each pixel is

computed using a Gaussian in the spatial domain multiplied by an

inﬂuence function in the intensity domain that decreases the weight

of pixels with large intensity differences. We express bilateral ﬁlter-

ing in the framework of robust statistics and show how it relates to

anisotropic diffusion. We then accelerate bilateral ﬁltering by using

a piecewise-linear approximation in the intensity domain and ap-

propriate subsampling. This results in a speed-up of two orders of

magnitude. The method is fast and requires no parameter setting.

CR Categories: I.3.3 [Computer Graphics]: Picture/image

generation—Display algorithms; I.4.1 [Image Processing and Com-

puter Vision]: Enhancement—Digitization and image capture

Keywords: image processing, tone mapping, contrast reduction,

edge-preserving ﬁltering,weird maths

1 Introduction

As the availability of high-dynamic-range images grows due to ad-

vances in lighting simulation, e.g. [Ward 1994], multiple-exposure

photography [Debevec and Malik 1997; Madden 1993] and new

sensor technologies [Mitsunaga and Nayar 2000; Schechner and

Nayar 2001; Yang et al. 1999], there is a growing demand to be

able to display these images on low-dynamic-range media. Our vi-

sual system can cope with such high-contrast scenes because most

of the adaptation mechanisms are local on the retina.

There is a tremendous need for contrast reduction in applica-

tions such as image-processing, medical imaging, realistic render-

ing, and digital photography. Consider photography for example.

A major aspect of the art and craft concerns the management of

contrast via e.g. exposure, lighting, printing, or local dodging and

burning [Adams 1995; Rudman 2001]. In fact, poor management

of light – under- or over-exposed areas, light behind the main char-

acter, etc. – is the single most-commonly-cited reason for rejecting

Figure 1: High-dynamic-range photography. No single global ex-

posure can preserve both the colors of the sky and the details of

the landscape, as shown on the rightmost images. In contrast, our

spatially-varying display operator (large image) can bring out all

details of the scene. Total clock time for this 700x480 image is 1.4

seconds on a 700Mhz PentiumIII. Radiance map courtesy of Paul

Debevec, USC. [Debevec and Malik 1997]

Base Detail Color

Figure 2: Principle of our two-scale decomposition of the input

intensity. Color is treated separately using simple ratios. Only the

base scale has its contrast reduced.

photographs. This is why camera manufacturers have developed

sophisticated exposure-metering systems. Unfortunately, exposure

only operates via global contrast management – that is, it recenters

the intensity window on the most relevant range. If the range of in-

tensity is too large, the photo will contain under- and over-exposed

areas (Fig. 1, rightmost part).

Our work is motivated by the idea that the use of high-dynamic-

range cameras and relevant display operators can address these is-

sues. Digital photography has inherited many of the strengths of

ﬁlm photography. However it also has the potential to overcome

its limitations. Ideally, the photography process should be de-

composed into a measurement phase (with a high-dynamic-range

output), and a post-process phase that, among other things, man-

ages the contrast. This post-process could be automatic or user-

controlled, as part of the camera or on a computer, but it should

take advantage of the wide range of available intensity to perform

appropriate contrast reduction.

In this paper, we introduce a fast and robust operator that takes

a high-dynamic-range image as input, and compresses the contrast

while preserving the details of the original image, as introduced by

Tumblin [1999]. Our operator is based on a two-scale decomposi-

tion of the image into a base layer (large-scale features) and a detail

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