- Research Article
- Open Access
Video Enhancement Using Adaptive Spatio-Temporal Connective Filter and Piecewise Mapping
© ChaoWang et al. 2008
- Received: 28 August 2007
- Accepted: 3 April 2008
- Published: 5 May 2008
This paper presents a novel video enhancement system based on an adaptive spatio-temporal connective (ASTC) noise filter and an adaptive piecewise mapping function (APMF). For ill-exposed videos or those with much noise, we first introduce a novel local image statistic to identify impulse noise pixels, and then incorporate it into the classical bilateral filter to form ASTC, aiming to reduce the mixture of the most two common types of noises—Gaussian and impulse noises in spatial and temporal directions. After noise removal, we enhance the video contrast with APMF based on the statistical information of frame segmentation results. The experiment results demonstrate that, for diverse low-quality videos corrupted by mixed noise, underexposure, overexposure, or any mixture of the above, the proposed system can automatically produce satisfactory results.
- Impulse Noise
- Bilateral Filter
- Noise Filter
- Temporal Filter
- Neighborhood Window
Driven by rapid development of digital devices, camcorders and cameras are no longer used only for professional work, but step into a variety of application areas such as surveillance and home video making. While capturing videos become much easier, video defects, such as blocking, blur, noises, and contrast distortions, are often introduced by many uncontrollable factors: unprofessional video recording behaviors, information loss in video transmissions, undesirable environmental lighting, device defects, and so forth. As a result, there is an increasing demand for the technique—video enhancement, which aims at improving videos' visual qualities, while endeavoring to repress different kinds of artifacts. In this paper, we focus on two most common defects: noises and contrast distortions. While some existing software have already provided noise removal and contrast enhancement functions, it is likely that most of them introduce artifacts and could not produce desirable results for a broad variety of videos. Until now, video enhancement still remains a challenging research problem in filtering noises as well as enhancing contrast.
The natural noises in videos are quite complex; yet, fortunately, most noises can be represented using two models: additive Gaussian noise and impulse noise [1, 2]. Additive Gaussian noise generally assumes zero-mean Gaussian distribution and is usually introduced during video acquisition, while impulse noise assumes uniform or discrete distribution and is often caused by transmission errors. Thus, filters can be designed targeting the two kinds of noises. Gaussian noise can be well suppressed by bilateral filter , anisotropic diffusion , wavelet-based approaches , or fields of experts  while maintaining edges. Impulse noise filters lie on robust image statistics to distinguish noise pixels and fine features (i.e., small high-gradient regions) and often need an iterative process to reduce false detection [7–9]. Building filters for removing mixture of Gaussian and impulse noise is more practical than that for one specific type of noise with respect to natural images. The essence of mixed noise filter is to incorporate the pertinent techniques into a uniform framework that can effectively smooth the mixed noise while avoiding blurring the edges and fine features.
As to video noise removal, in addition to the above issues, temporal information should also be taken into consideration because it is more valuable than spatial information in the case of stationary scene . But straightly averaging temporal corresponding pixels to smooth noise may introduce "ghosting" artifacts in the presence of camera and object motion. Such artifacts can be removed by motion compensation and a number of algorithms have been proposed with different computational complexity . However, severe impulse noise will introduce abrupt pixel changes like motions and greatly decrease the accuracy of motion compensation. Moreover, there are often not enough similar pixels for smoothing in temporal directions, owing to imperfect motion compensation or transitions between shots. Thus, a desirable video noise filter should distinguish impulse pixels and motional pixels as well as collect enough similar pixels adaptively from temporal to spatial directions.
As to contrast enhancement after noise filtering, it is quite difficult to find a universal approach for all videos owing to their diverse characteristics such as underexposed, overexposed with many fine features or with large black background. Although numerous contrast enhancement methods have been proposed, most of them are unable to automatically produce satisfactory results for different kinds of low-contrast videos, and may generate ringing artifacts in the vicinity of the edges "washed-out" artifacts  when having monochromic background or noise over enhancement artifacts.
Motivated by the above observations, we propose a universal video enhancement system to automatically recover the ideal high-quality signal from noise degraded videos and enlarge their contrast to a subjectively acceptable level. For a given defective video, we introduce an adaptive spatio-temporal connective (ASTC) filter, which adapts from temporal to spatial filters based on noise level and local motion characteristics to remove mixture of Gaussian and impulse noises. Both the temporal and the spatial filters are noniterative trilateral filters, formed by introducing a novel local image statistic—neighborhood connective value (NCV) into the traditional bilateral filter. NCV represents the connective strength of a pixel to all its neighboring pixels and is a good measure for differentiating between impulse noises and fine features. After noise removal, we adopt pyramid segmentation algorithm  to divide a frame into several regions. Based on the areas and standard deviations of these regions, we produce a novel adaptive piecewise mapping function (APMF) to automatically enhance the video contrast. To show effectiveness of our NCV statistic, we conducted a simulation experiment by adding impulse noises into three representative pictures and reported superior noise detection performance compared with other noise filters. In addition, we tested our system on several real defective videos adding mixed noises. These videos cover diverse kinds of defectiveness: underexposure, overexposure, mixture of them, and so forth. Our outputs are much more visually pleasing than those of other state-of-art approaches.
a novel local image statistic for identifying impulse pixels—neighborhood connective value (NCV) (Section 4),
an adaptive spatio-temporal connective (ASTC) filter for reducing mixed noise (Section 5), and
an adaptive piecewise mapping function (APMF) to enhance video contrast (Section 6).
In addition, Section 2 reviews previous work related to video enhancement; the system framework is represented in Section 3; Section 7 gives the experimental results, followed by conclusions in Section 8.
There have been much previous work on image and video noise filter and contrast enhancement. We will make a brief review on this section and describe their essential differences with our work.
2.1. Image and Video Noise Filter
Since most natural noise can be modeled by Gaussian noise and impulse noise , many researchers have put great efforts on removing the two kinds of noises. Most previous Gaussian noise filters are based on anisotropic diffusion  or bilateral filter [3, 14, 15], both of which have similar mathematical models . These methods well suppress Gaussian noise but failed to remove impulse noises owing to treating them as edges. On the other hand, most impulse noise filters are based on rank-order statistics [7, 9, 17], which perform the reordering of pixels of a 2-D neighborhood window into a 1-D sequence. Such approaches weakly exploit spatial relations between pixels. Thus, Kober et al.  introduced a spatially connected neighborhood (CNBH) for noise detection, which describes the connective relations of pixels with their neighborhoods, similar to our NCV statistic. But their solution only considered the pixels of CNBH, unlike ours that utilize all the neighboring pixels to characterize the structures of fine features. Furthermore, it needs to be performed iteratively to correct false detection, unlike our single-step method.
The idea of removing mixture of Gaussian and impulse noise was considered by Peng and Lucke  using a fuzzy filter. Then the median based SD-ROM filter was proposed , but it produced visually disappointing output . Recently, Garnett et al.  brought forward an innovative impulse noise detector—rank-ordered absolute differences (ROAD)—and introduced it into bilateral filter to filter mixed noise. However, unlike our NCV approach, their approach would fail for fine feature pixels, owing to their nonoverall assumption: signal pixels should have similar intensities with at least half of their neighboring pixels.
There is a long history of research on spatio-temporal noise reduction algorithms in signal processing literature . The essence of these methods is to adaptively gather enough information in temporal and spatial directions to smooth pixels while avoiding motion artifacts. Lee and Kang  extended anisotropic diffusion technique to the three dimensions for smoothing video noise. Unlike our approach, they did not employ motion compensation and did not treat temporal and spatial information differently. Instead, we adopt optical flow for motion estimation and use temporal filter more heavily than spatial filter. Jostschulte et al.  developed a video noise reduction system that used spatial and temporal filters separately while preserving edges that match a template set. The separated use of two filters limits their performances on different kinds of videos. Bennett and McMillan  presented the adaptive spatio-temporal accumulation (ASTA) filter that adapts from temporal bilateral filter to spatial bilateral filter based on a tone-mapping objective and local motion characteristics. Owing to bilateral filter's limitation on removing impulse noise, their approach produces disappointing results compared with ours when applied to videos with mixed noise.
2.2. Contrast Enhancement
Numerous contrast enhancement methods have been proposed such as linear or nonlinear mapping function and histogram processing techniques . Most of these methods are based on global statistical information (global image histogram, etc.) or local statistical information (local histogram, pixels of neighborhood window, etc.). Goh et al.  adaptively used four types of fixed mapping function to process video sequences based on histogram analysis. Yet, their results heavily depend on the predefined functions, which restricts the usefulness in diverse videos. Polesel et al.  use unsharp masking techniques to separate image into low-frequency and high-frequency components, then amplify the high-frequency component while leaving the low-frequency component untouched. However, such methods may introduce ringing artifacts due to over enhancement in the vicinity of edges. Durand and Dorsey  use the bilateral filter to separate an image into details and large scale features, then map the large scale features in the log domain and leave the details untouched; thus details are more difficult to distinguish in the processed image. Recently, Chen et al.  brought forward the gray-level grouping technique to spread the histogram as uniformly as possible. They introduce a parameter to prevent one histogram component from occupying too many gray levels, so that their method can avoid "washed-out" artifacts, that is, over enhancing images with homochromous backgrounds. Differently, we suppress "washed-out" artifacts by disregarding the segmented regions with too small standard deviation in our mapping function forming process.
In contrast enhancement procedure, we firstly separate a frame into large scale features and details using rank-ordered absolute difference (ROAD) bilateral filter , which preserves more fine features than other traditional filters do . Then, we enhance the large scale features with APMF to achieve the desired contrast, while mapping the details using a less curved function adjusted by the local intensity standard deviation. This two pipeline method can avoid ringing artifacts even around sharp transition regions. Unlike traditional enhancement methods based on histogram statistics, we produce our adaptive piecewise mapping function (APMF) based on frame segmentation results, which provide more 2-D spatial information. Finally, the mapped large scale features, mapped details, and chrominance are combined to generate the final enhanced video. We next describe the NCV statistic, the ASTC noise filter, as well as the contrast enhancement procedure.
where equals 1 when , and equals 0.5 when is a parameter to penalize highly different intensities and is fixed to 30 in our experiments. The CV of two neighboring pixels assumes values in (0, 1]; the more similar their intensities are, the larger their CV is. CV measures the number of pixels that two neighboring pixels contribute to each other's "connective strength." It is perceptional rational that diagonal neighboring pixels are less closely connected than the neighboring pixels which share one identical edge, so one multiplies a factor (i.e., ) of different values to discriminate the two types of connection relationship.
PCV describes the smoothness of a path; the more similar the intensities of pixels in the path are, the larger the path's PCV is. PCV achieves the maximum 1 when all pixels in the path have identical intensity; thus, . It should be noticed that there are several paths between two pixels. For example, in Figure 3, the path from p 12 to p 33 can be or , which have PCVs of 0.0460 and 0.2497, respectively.
In the above definitions, the neighboring pixels are pixels in a window, denoted by , with p xy as the center. In our experiments, k is fixed to 2. LCV of one specific pixel equals the PCV of the smoothest path from it to the central pixel and reflects the geometric closeness and photometric similarity of it with the central one. Apparently, .
NCV provides a measure of the "connective strength" of a central pixel to all its neighboring pixels. For a neighborhood window, NCV will decrease to about 1 when the intensity of the central pixel far deviates from those of all neighboring pixels and will reach its maximum 25, when all the pixels in the neighborhood window have identical intensity, so .
Since , then one has . Thus, one can make a graph, taking the central pixel and all its neighboring pixels as vertices and taking DIS as the cost of edge between two pixels. Therefore, the calculation of LCV can be converted to the single-source shortest path problem and can be solved by Dijkstra's algorithm .
To test the effectiveness of NCV for impulse noise detection, one conducted a simulation experiments on three representative pictures: "Lena," "Bridge," and "Neon Light" as shown in Figure 4. "Lena" has few sharp transitions, "Bridge" has many edges, and "Neon Light" has lots of impulse-like fine features, that is, small high gradient regions. The diverse characteristics of these pictures assure the effectiveness of our experiments. Figures 5(a), 5(b), and 5(c) display quantitative results from the "Lena," "Bridge," and "Neon Light" images, respectively. The lower dashed lines represent the mean NCV for salt-and-pepper noise pixels—which is a discrete impulse noise model in which the noisy pixels take only the values 0 and 255—as a function of the amount of noise added, and the upper dashed line represents the mean NCV for signal pixels. The signal pixels consistently have higher mean NCVs than the impulse pixels, of which NCVs remain almost constant even with very high noise level. In contrast, the famous ROAD statistic cannot well differentiate between impulse and signal pixels in the "Neon Light" image as shown in Figure 5(d), because it assumes the signal pixels have at least half similar pixels in neighborhood window, which is coincident with the smooth regions but corrupts for fine features.
Video is a compound of image sequences, including both spatial and temporal information. Accordingly, our ASTC video noise filter adapts from temporal to spatial noise filter. We will detail the spatial filter, the temporal filter, and the adaptive fusion strategy in this section.
5.1. The Spatial Connective Trilateral Filter
As mentioned in Section 4, NCV is a good statistic for impulse noise detection, whereas the bilateral filter  well suppresses Gaussian noise. Thus, we incorporate NCV into the bilateral filter to form a trilateral filter in order to remove mixed noise.
where and represent spatial and radiometric weights, respectively . In our experiments, and are fixed to 2 and 30, respectively. The formula is based on the assumption that pixels locating nearer and having more similar intensities should have larger weights.
According to the new weighting function, for impulse noise pixels, is almost "shut off" by the switch J, while and work to remove the large outliers; for other pixels, is almost "shut off" by the switch J, and only and work to smooth small amplitude noise without blurring edges. Consequently, we build the spatial trilateral connective (SCT) filter by merging (9) and (13).
5.2. Trilateral Filtering in Time
As to videos, temporal filtering is more important than spatial filtering , but irregular camera and object motions often degrade the performance. Thus, robust motion compensation is quite necessary. Optical flow is a classical approach for this problem; however, it depends on robust gradient estimation and will fail for noisy, underexposed, or overexposed images. Therefore, we pre-enhance the frames with SCT filter and our adaptive piecewise mapping function, which will be detailed in Section 6. Then, we adopt the cvCalcOpticalFlowLK() function of the intel open source computer vision library (Opencv) to compute dense optical flows for robust motion estimation. Too small and too large motions are deleted; also, half-wave rectification and Gaussian smoothing are applied to eliminate noises in optical flow field .
where and and J are defined the same as (11) and (12), respectively.
The TCT filter can well differentiate impulse noise pixels from motional pixels and smooth the former while leaving the later almost untouched. For impulse noise pixels, the switch function J in TCT filter will "shut off" the radiometric component and the spatial weight is used to smooth them; for motional pixels, J will "shut off" the impulsive component and TCT filter reverts to bilateral filter, which takes the motional pixels as "temporal edges" and leaves them unchanged.
5.3. Implementing ASTC
In the above formula, presents the local noise level and is computed in a spatial neighborhood window. reaches its maximum 1 in good frames and decreases with the increase of noise level. is the gain factor of current pixel and equals the tone mapping scales in our adaptive piecewise mapping function, which will be detailed in Section 6. Thus, the more mapping scale is and less noises exist, the larger becomes; the less mapping scale is and more noises exist, the smaller becomes. Such characteristics assure the threshold working well for different kinds of videos.
If a sufficient number of temporal pixels are gathered, only temporal filter is used.
On the other hand, even if temporal pixels are insufficient, the temporal filter should still more dominant over the spatial one in the fused spatio-temporal filter.
which represents the sum of pixel weights in temporal direction. If (i.e., sufficient temporal pixels), , then ASTC filter regresses to temporal connective trilateral filter; if (i.e., insufficient temporal pixels), , ASTC filter will use the temporal connective trilateral filter to gather pixels in temporal direction first, and then use the spatial connective trilateral filter to gather the remaining number of pixels in spatial direction.
We have described the process of filtering mixture of Gaussian and impulse noises from defective videos. However, contrast enhancement is another key issue. In this section, we will show how to build the tone mapping function as well as how to automatically adjust important parameters and smooth the function in time.
6.1. Generating AMPF
If is larger than Bright, then it is assigned to 1, and the low-segment curve will occupy the whole dynamic range; if is lower than Dark, then it is assigned to 0, and the high-segment curve will occupy the whole dynamic range. If there are no regions with average intensities falling into either dark or bright range, then is assigned to the default value 0.5.
where and are parameters controlling the curvatures of low and high segments, respectively. and are gain factors of intensities Dark and Bright, respectively, which is defined the same as in (15), that is, the proportion between the new intensity and the original one. and are precomputed before getting the mapping function and control the selection of curves between the red and the green in Figure 7. This mapping function avoids sharp slope near the origin, and thus well preserves details .
6.2. Automatic Parameters Selection
Although we designed the APMF as (19) to deal with different situations, how to choose appropriate parameters in the function determines the tone mapping performance. Thus, we will detail the process of choosing these important parameters— , and .
where N is the normalization operator (divided by the maximum), and I is the maximum range which can be stretched to. In other words, denotes the maximum enlarging range, and the exponential factor controls the enlarging scale. It should be noticed that the segmented regions with too small standard deviation should be disregarded in (20) because they probably correspond to the backgrounds or monochromic boards in the image and should not be enhanced anymore.
and can be got similarly. Thus, all the parameters in (19) are determined.
As mentioned in Section 2, in order to better deal with details as well as avoiding ringing artifacts, we first separate an image into large scale parts and details using ROAD bilateral filter owing to its ability of well preserving fine features , and then enhance the large scale parts with function , while enhancing details with a less curved function . and correspond to the intensity standard deviations of all regions falling into and , respectively. The larger the standard deviation is, the more linear the mapping function for the details is.
6.3. Temporal Filtering of AMPF
where is the difference operator. If Diff of successive frames is lower than a threshold, then we smooth , and in the APMF of current frame by averaging corresponding values in neighboring ( ) frames. Otherwise, we just adopt the new APMF. In our experiments, m is fixed to 5 and the threshold is 30.
To demonstrate the effectiveness of the proposed video enhancement framework, we have applied it to a broad variety of low-quality videos, including corrupted by mixed Gaussian and impulse noise, underexposed and overexposed video sequences. Although it is difficult to obtain the ground truth comparison for video enhancement, it can be clearly seen from the processed results that our framework is superior to the other existing methods.
From all picture (b), (c) of Figures 9, 10, and 11, all of which are enhanced by the popular contrast enhancement method-histogram equalization, we can see that no matter whether the noises are filtered in advance (all Figure (c)) or not (all Figure (b)), the output videos are always unacceptable, since the noises are over-enhanced in the equalization process. While our APMF considers the intensity standard deviations and treat large scale parts and details differently. From Figures 10(c), 10(d), 11(c), and 11(d), we can see that our APMF produces much better outputs than histogram equalization after the same filtering process. Our APMF great enhances the video as well as suppressing mixed noises. In addition, our APMF produces desirable outputs in all underexposed, overexposed, and mixed ill-exposed videos, owing to its ability of adaptively adjusting the mapping functions according to different videos.
As to noise filtering, our ASTC filter also outperforms other approaches. Although the ASTA system work well on videos with Gaussian noises , it fails to deal with videos with mixed noises as shown in Figure 9(d). We can see great impulse noise pixels allover the image. This is because ASTA is formed by combining the spatial and temporal bilateral filters, which take the impulse noise pixels as "temporal edges" and leave them untouched. In addition, AML3D filter and P3D filter, which are two kinds of improved spatio-temporal median filters, produce grainy results as shown in the bright wall regions in Figure 10(d) as well as the dark regions in Figure 11(d). In contrast, our system produces more pleasing outputs as shown in Figure 10(e) and well preserves details that are hardly visible in the original videos such as the car in Figure 9(e) and the telephone in Figure 11(e). The reason is that our noise filter is based on the combination of a good impulse detector and the classical bilateral filter; the former well deals with large outliers, and the latter effectively smoothes small amplitude noises. In general, the results indicate the robustness and effectiveness of our video enhancement system in different kinds of videos with mixed noises.
In this paper, we have presented a universal video enhancement system, which is able to greatly suppress the most two common noises—Gaussian and impulse noises as well as significantly enhance video contrast. We introduce a novel local image statistic—neighborhood connective value (NCV) to improve impulse noise detection performance to a great extent. Then, we incorporate it into the bilateral filter framework to form an adaptive spatio-temporal connective (ASTC) filter to reduce mixed noises. ASTC filter adapts from a temporal filter to a spatial one based on noise level and local motion characteristics, and thus assure its robustness for different videos. Furthermore, we build an adaptive piecewise mapping function (APMF) to automatically enhance video contrast using statistical information of frame segmentation results, which provide more 2-D spatial information than the histogram statistics. We conducted a simulation experiment on three representative images, and an extensive experiment on several videos, which are underexposed, overexposed, or both of them. Both the objective and subjective evaluations indicated the effectiveness of our system.
Limitations remain in our system, however. First, our system assumes that impulse noise pixels are always closely connected with fewer neighboring pixels than signal pixels, so it will fail to remove large blotches (i.e., distorted region larger than four pixels) for film restoration. Secondly, our implementation is very slow since it includes multiple nonlinear filtering steps and computation of NCVs. The current processing of one frame takes about one minute. Extending our approach to detect large blotches and improving its performance are our future work. Furthermore, we will pay attention to enhance video regions differently according to human's attention model.
This work was supported by the National High-Tech Research and Development Plan (863) of China under Grant no. 2006AA01Z118, National Basic Research Program (973) of China under Grant no. 2006CB303103, and National Natural Science Foundation of China under Grant no. 60573167.
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