没有合适的资源?快使用搜索试试~ 我知道了~
Noise reduction ggggggg
需积分: 5 0 下载量 193 浏览量
2023-12-12
21:55:40
上传
评论
收藏 904KB PDF 举报
温馨提示
试读
23页
Noise reduction ggggggg
资源推荐
资源详情
资源评论
Sensors 2009, 9, 1692-1713; doi:10.3390/s90301692
sensors
ISSN 1424-8220
www.mdpi.com/journal/sensors
Article
Noise Reduction for CFA Image Sensors Exploiting HVS
Behaviour
Angelo Bosco
1,
*, Sebastiano Battiato
2
, Arcangelo Bruna
1
and Rosetta Rizzo
2
1
STMicroelectronics, Stradale Primosole 50, 95121 Catania, Italy;
E-Mail: arcangelo.bruna@st.com
2
Università di Catania, Dipartimento di Matematica ed Informatica, Viale A. Doria 6, 95125
Catania, Italy; E-Mails: battiato@dmi.unict.it; rosetta.rizzo@dmi.unict.it
* Author to whom correspondence should be addressed; E-Mail: angelo.bosco@st.com
Received: 18 November 2008; in revised form: 4 March 2009 / Accepted: 9 March 2009/
Published: 10 March 2009
Abstract: This paper presents a spatial noise reduction technique designed to work on
CFA (Color Filtering Array) data acquired by CCD/CMOS image sensors. The overall
processing preserves image details using some heuristics related to the HVS (Human
Visual System); estimates of local texture degree and noise levels are computed to regulate
the filter smoothing capability. Experimental results confirm the effectiveness of the
proposed technique. The method is also suitable for implementation in low power mobile
devices with imaging capabilities such as camera phones and PDAs.
Keywords: Noise Reduction, Color Filter Array, HVS, Texture Detection.
1. Introduction
The image formation process through consumer imaging devices is intrinsically noisy. This is
especially true using low-cost devices such as mobile-phones, PDAs, etc., mainly in low-light
conditions and the absence of flash-guns [1].
The final perceived quality of images acquired by digital sensors can be optimized through multi-
shot acquisitions (e.g., extending dynamic range [2], increasing resolution [3]) and/or using ad-hoc
post-processing techniques [4,5] taking into account the raw data acquired by Bayer matrixed image
OPEN ACCESS
Sensors 2009, 9
1693
sensors [6]. These are grayscale sensors covered by CFA (Color Filter Array) to enable color
sensitivity, such that each cell of the sensor array is receptive to only one color component. The final
color image is obtained by means of a color reconstruction (demosaicing) algorithm that combines the
color information of neighboring pixels [7-9] and [10]. A useful review of technology and methods in
the field can be found in [1] and [11].
In this paper we propose a novel spatial noise reduction method that directly processes the raw CFA
data, combining together HVS (Human Visual System) heuristics, texture/edges preservation
techniques and sensor noise statistics, in order to obtain an effective adaptive denoising.
The proposed algorithm introduces the concept of the usage of HVS peculiarities directly on the
CFA raw data from the sensor. In addition, the complexity of the algorithm is kept low by using only
spatial information and a small fixed-size filter processing window, allowing real-time performance on
low cost imaging devices (e.g., mobile phones, PDAs).
The HVS properties, able to characterize or isolate unpleasant artifacts, are complex (highly
nonlinear) phenomena not yet completely understood involving a lot of complex parameters [12,13].
Several studies in the literature have tried to simulate and code some known aspects in order to find
reliable image metrics [14-16] and heuristics to also be applied for demosaicing [17].
Sophisticated denoising methods such as [18-20] perform multiresolution analysis and processing in
the wavelet domain. Other techniques, as suggested in [21], use anisotropic non-linear diffusion
equations, but work iteratively. Spatial denoising approaches having texture discrimination capabilities
can be found in [1,23,24], whereas methods implementing texture discrimination using fuzzy logic are
described in [25,26]. Other kinds of noise, such as fixed pattern noise (FPN) can be treated ad-hoc, in
[27] a method suitable is presented.
The proposed filtering method is a trade-off between real time implementation with very low
hardware logic and the usage of some HVS peculiarities, texture and noise level estimation. The filter
adapts its smoothing capability to local image characteristics yielding effective results in terms of
visual quality.
The paper is structured as follows: in the next section some details about the CFA and HVS
characteristics are briefly discussed; in Section 3 the overall details of the proposed method are
presented. An experimental section reports the results and some comparisons with other related
techniques. The final section tracks directions for future works.
2. Background
2.1. Bayer Data
In typical imaging devices a color filter is placed on top of the imager making each pixel sensitive
to only one color component. A color reconstruction algorithm interpolates the missing information at
each location and reconstructs the full RGB image [9-11]. The color filter selects the red, green or blue
component for each pixel; this arrangement is known as Bayer pattern [6]; other arrangements of CFA
data take into account CMY complementary colors, but the RGB color space is the most common.
The number of green elements is twice the number of red and blue pixels due to the higher
sensitivity of the human eye to the green light, which, in fact, has a higher weight when computing the
Sensors 2009, 9
1694
luminance. The proposed filter processes raw Bayer data, providing the best performance if executed
as the first algorithm of the IGP (Image Generation Pipeline). A typical image reconstruction pipeline
is shown in Figure 1.
Figure 1. Image Generation Pipeline.
2.2. Basic Concepts about the Human Visual System
It is well known that the HVS has a different sensitivity at different spatial frequencies [28]. In areas
containing mean frequencies the eye has a higher sensitivity. Furthermore, chrominance sensitivity is
weaker than the luminance one.
HVS response does not entirely depend on the luminance value itself, rather, it depends on the
luminance local variations with respect to the background; this effect is described by the Weber-
Fechner’s law [13,29], which determines the minimum difference DY needed to distinguish between Y
(background) and Y+DY. Different values of Y yield to different values of DY.
The aforementioned properties of the HVS have been used as a starting point to devise a CFA
filtering algorithm. Luminance from CFA data can be extracted as explained in [30], but for our
purposes it can be roughly approximated by the green channel values before gamma correction.
The filter changes its smoothing capability depending on the CFA color of the current pixel and its
similarity with the neighborhood pixels.
More specifically, in relation to image content, the following assumptions are considered:
- if the local area is homogeneous, then it can be heavily filtered because pixel variations are
basically caused by random noise.
- if the local area is textured, then it must be lightly filtered because pixel variations are mainly
caused by texture and by noise to a lesser extent; hence only the little differences can be safely
filtered, as they are masked by the local texture.
Sensors 2009, 9
1695
3. The Proposed Technique
3.1. Overall filter block diagram
A block diagram describing the overall filtering process is illustrated in Figure 2. Each block will
be separately described in detail in the following sections.
Figure 2. Overall Filter Block Diagram.
The fundamental blocks of the algorithm are:
Signal Analyzer Block: computes a filter parameter incorporating the effects of human
visual system response and signal intensity in the filter mask.
Texture Degree Analyzer: determines the amount of texture in the filter mask using
information from the Signal Analyzer Block.
Noise Level Estimator: estimates the noise level in the filter mask taking into account the
texture degree.
Similarity Thresholds Block: computes the fuzzy thresholds that are used to determine the
weighting coefficients for the neighborhood of the central pixel.
Weights Computation Block: uses the coefficients computed by the Similarity Thresholds
Block and assigns a weight to each neighborhood pixel, representing the degree of similarity
between pixel pairs.
Filter Block: actually computes the filter output.
The data in the filter mask passes through the Signal Analyzer block that influences the filter
strength in dark and bright regions (Section 3.2 for further details). The HVS value is used in
combination with the output of the Texture Degree Analyzer (Section 3.4) and Noise Level Estimator
(Section 3.5) to produce the similarity thresholds used to finally compute the weights assigned to the
Sensors 2009, 9
1696
neighborhood of the central pixel (Section 3.6). The final filtered value is obtained by a weighted
averaging process (Section 3.7).
3.2. Signal Analyzer Block
As noted [31-33], it is possible to approximate the minimum intensity gap that is necessary for the
eye to perceive a change in pixel values. The base sensitivity thresholds measure the contrast
sensitivity in function of frequency while fixing the background intensity level. In general, the
detection threshold varies also with the background intensity. This phenomenon is known as
luminance masking or light adaptation. Higher gap in intensity is needed to perceive a visual
difference in very dark areas, whereas for mid and high pixel intensities a small difference in value
between adjacent pixels is more easily perceived by the eye [32].
It also crucial to observe that in data from real image sensors, the constant AWGN (Additive White
Gaussian Noise) model does not fit well the noise distribution for all pixel values. In particular, as
discussed in [34], the noise level in raw data is predominantly signal-dependent and increases as the
signal intensity raises; hence, the noise level is higher in very bright areas. In [34] and [35] it is also
illustrated how clipping in data is the cause of noise level underestimation; e.g., noise level for pixels
close to saturation cannot be robustly tracked because the signal reaches the upper limit of the allowed
bitdepth encoding.
We decided to incorporate the above considerations of luminance masking and sensor noise
statistics into a single curve as shown in Figure 3. The shape of this curve allows compensating for
lower eye sensitivity and increased noise power in the proper areas of the image, allowing adaptive
filter smoothing capability in relation to the pixel values.
Figure 3. HVS curve used in the proposed approach.
Pixel
Value
HVS
weight
2
bitdepth
-1
HVS
max
HVS
min
(2
bitdepth
-1)/2
A high HVS value (HVSmax) is set for both low and high pixel values: in dark areas the human eye
is less sensitive to variations of pixel intensities, whereas in bright areas noise standard deviation is
higher. HVS value is set low (HVSmin) at mid pixel intensities.
剩余22页未读,继续阅读
资源评论
weixin_55031823
- 粉丝: 0
- 资源: 13
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功