RFFNet: Towards Robust and Flexible Fusion for Low-Light Image Denoising

Venue: acmmm_org_ACMMM_2024_Conference
Published: 2024-04-01 05:04:33

Abstract: Low-light environments will introduce high-intensity noise into images. Containing fine details with reduced noise, near-infrared/flash images can serve as guidance to facilitate noise removal. 
However, existing fusion-based methods fail to effectively suppress artifacts caused by inconsistency between guidance/noisy image pairs and do not fully excavate the useful information contained in guidance images. In this paper, we propose a robust and flexible fusion network (RFFNet) for low-light image denoising. Specifically, we present a multi-scale inconsistency calibration module to address inconsistency before fusion by first mapping the guidance features to multi-scale spaces and calibrating them with the aid of pre-denoising features in a coarse-to-fine manner. Furthermore, we develop a dual-domain adaptive fusion module to adaptively extract useful high-/low-frequency signals from the guidance features and then highlight the informative frequencies.
Extensive experimental results demonstrate that our method achieves state-of-the-art performance on NIR-guided RGB image denoising and flash-guided no-flash image denoising.

Main paper: 
 introduction: 
 Images captured in low-light environments typically contain distracting noise. Low-light image denoising aims to eliminate undesired noise, playing essential roles in all-day surveillance, selfdriving techniques, and photography. Image denoisers taking a single image as input often results in insufficient denoising or oversmoothing when dealing with these heavily degraded noisy images.Guided image restoration [11, 18, 22, 32, 34, 36] provides a new solution for image denoising. With low noise, clear structure, and rich details, near-infrared (NIR) images and flash images can serve as guidance for low-light image denoising. Nonetheless, there exists inconsistency between target and guidance image pairs, such as the presence of highlights on objects and hard shadows behind objects under NIR or flash illumination. As illustrated in the bottom part of Fig. 1 (b) , the shadows present in the NIR image do not exist in the RGB image. As a result, directly fusing cross-domain information tends to introduce artifacts, which makes low-light guided denoising a complex problem, as it requires not only removing strong noise and restoring details but also suppressing artifacts.Recently, several works have achieved impressive performance in NIR-guided [21, 35] and flash-guided [11, 31, 39] image denoising based on elaborately designed fusion methods. Nevertheless, they still struggle to eliminate the inconsistency issues. For example, CUNet [11] learns the common and unique features between the noisy image and the guidance image but fails under strong noise, leading to insufficient denoising and the production of artifacts, as illustrated in Fig. 1 (d) . SANet [35] guides denoising by aggregating structural signals from the guidance image, but introduces inconsistent structures, resulting in artifacts in the output (see Fig. 1 (e) ). Moreover, kernel prediction-based methods [31, 39] are highly susceptible to noise, failing to effectively suppress the artifacts in the presence of strong noise.Additionally, existing methods do not fully excavate the useful information contained in the guidance images in terms of frequency. For instance, DVN [21] leverages an additional branch to incorporate only the structure of guidance images, disregarding the potential beneficial information in low-frequency, such as smooth transition. SANet aggregates structure without considering the roles of individual frequency components, causing low-frequency colors to be equally integrated into the structure map, resulting in unexpected color deviations. We posit that high-frequency information can guide the restoration of texture details, while lowfrequency signals are useful for noise removal by prompting spatial smoothness in flat areas.To alleviate the above-mentioned issues, this paper proposes a Multi-Scale Inconsistency Calibration Module (MICM) to handle the inconsistency between the input image pairs. Specifically, we downsample the features from the input image pair into different scales to decouple noise and structure, and then modulate the crossspectral features with the aid of pre-denoised features from the noisy input using spatial attention. As a result, the inconsistency can be handled in a coarse-to-fine manner. Furthermore, we develop a Dual-Domain Adaptive Fusion Module (DAFM) to flexibly inject the modulated guidance features into the main path of the network by considering different roles of low-/high-frequency signals.More concretely, we employ multi-kernel depthwise convolutions and average pooling techniques to extract high-frequency and lowfrequency components of cross-modal images. Then, squeeze-andexcitation techniques are utilized to extract relevant local contexts from the high-frequencies of the guidance image, while useful global contexts in low-frequencies of the guidance image are captured through channel-wise cross-attention.As illustrated in Fig. 1 , our RFFNet strikes a better balance in noise removal, details recovery, and artifact suppression based on  
related works 2.1 single-image denoising: 
 Traditional single image denoising algorithms mainly include filtering [3, 10] , sparse-coding [14, 41] and low-rank factorization [13, 17] , which often rely on handcrafted filters or prior knowledge and lack flexibility in handling complex noise distributions.In recent years, learning-based methods have outperformed traditional methods. For example, DnCNN [45] uses batch normalization and residual learning for denoising. RIDNet [2] introduces feature attention in the residual structure to remove real noise. SADNet [4] employs deformable convolutions for spatially adaptive denoising. NBNet [7] removes noise by learning a set of reconstruction bases in the feature space. MPRNet [44] builds a multi-stage network for progressive restoration. NAFNet [6] designs a novel attention block to build a hierarchical network. Transformer-based denoising networks, such as Uformer [38] and Restormer [43] , are developed to better use local contexts and long-range dependencies. However, single-image denoising approaches tend to over-smooth texture details, especially at high noise levels.Several early works on guided image restoration [15, 25, 33] utilize flash images to reduce noise and blurring artifacts in non-flash images. Afterwards, He et al. [18] propose guided filters to handle various low-level tasks, including flash-guided no-flash image denoising. Yan et al. [42] generate a scale map to capture smooth transitions and usable edges in guidance images.Recently, deep learning methods have been applied to guided image restoration. DJFR [28] constructs a general deep fusion network for multiple image processing tasks, including image denoising. SVLRM [32] proposes a spatially variant linear representation model with learnable coefficients. Subsequently, CUNet [11] uses sparse encoding to separate the common and unique information of cross-modal images, which can perform both guided restoration and guided fusion. DKN [22] learns explicitly sparse and spatially variant kernels to guide filtering. DeepFnF [39] and RIDFnF [31] predict the kernel to combine the pixel colors of flash/no-flash images. DVN [21] addresses the issue of structure inconsistency in NIR images by using deep inconsistency prior. SANet [35] aggregates structure from the guidance image to estimate a clean structure map for guided denoising. However, when dealing with dense noise in low-light environments, most existing guided denoising methods fail to strike a balance between detail preservation, noise removal, and artifact suppression.Frequency analyses are widely adopted in image restoration [8] . Traditional algorithms for denoising [10, 20] or deblurring [26, 27] design adaptive filters in the frequency domain. Many learningbased image restoration methods are approached from a frequency perspective [8] . MWCNN [29] replaces the conventional pooling operation with the wavelet transform for a better tradeoff between receptive field sizes and efficiency. In [16] , stochastic frequency masking is proposed to address overfitting issues in blind superresolution and blind denoising. SDWNet [47] designs a wavelet transformation module to recover clear high-frequency texture details. FADN [40] decouples input images into multiple components using discrete cosine transform for individual processing, enabling differential super-resolution. SFNet [9] proposes a decoupling and modulation module that can adaptively separate frequency information and selectively aggregate them. This paper emphasizes the diverse roles of frequencies in guidance images and strives to adaptively aggregate useful frequencies. 
method 3.1 model formulation: 
 A noise model is given by 𝑌 = 𝑋 + 𝑁 , where 𝑌 , 𝑋 , and 𝑁 (∈ R 𝐻 ×𝑊 ) denote a noisy image, a clean image, and the additive noise component, respectively. The channel dimension is omitted for brevity. Let 𝐺 ∈ R 𝐻 ×𝑊 represent a clean guidance image, due to the high structure similarity between 𝐺 and 𝑋 , we can use 𝐺 to help remove the noise and compensate for the missing details. However, directly fusing 𝐺 and 𝑌 will introduce undesired artifacts due to inconsistency. Therefore, we perform calibration on the crossspectral features before fusion. For the 𝑘-th channel of guidance features, 𝑔 𝑘 , we first obtain a weight matrix by a transformation 𝐴 𝑘 (•, •), which establishes the correlation between 𝑦 𝑘 and 𝑔 𝑘 to eliminate inconsistency by:EQUATIONwhere g𝑘 and 𝑦 𝑘 represent the 𝑘-th channel of calibrated guidance features and noisy features, respectively. We can obtain the calibrated noisy features ỹ𝑘 by applying the same operation. Then, the predicted clean image Ŷ ∈ R 𝐻 ×𝑊 can be produced by:EQUATIONwhere 𝐵 𝑘 (•, •) is another transformation which establishes the correlation between ỹ𝑘 and g𝑘 , focusing on compensating for missing texture details and smoothing the flat areas. The symbol * denotes the convolutional operation, and{𝑓 𝑘 } 𝐾 𝑘=1 ∈ R 𝐻 ×𝑊 ×𝐾 is the feature filters.Considering the different roles of high-frequency edges and lowfrequency smoothing areas in guided denoising, we decouple 𝐵 𝑘 into 𝐵 ℎ 𝑘 and 𝐵 𝑙 𝑘 to selectively capture useful high-/low-frequency signals from the guidance features and highlight the informative frequency components. As a result, Eq. 2 can be rewritten as:EQUATIONwhere 𝐵 ℎ 𝑘 and 𝐵 𝑙 𝑘 are transformations responsible for high-frequency correlation and low-frequency correlation, respectively. Overall, our model is built based on Eq. 1 and Eq. 3, where 𝐴, 𝐵 are implemented by our Multi-Scale Inconsistency Calibration Module (MICM) and Dual-Domain Adaptive Fusion Module (DAFM), respectively.In addition, we introduce a pre-denoising stage before the above operations to reduce the impact of noise on the calibration and fusion processes.As illustrated in Fig. 2 , our RFFNet is an end-to-end two-stage network for progressively restoring noise-free and detail-rich images. The first stage performs pre-denoising to not only facilitate noise removal in the fusion stage but also provide the general outline for easily identifying inconsistency. The guidance features are involved in the network based on our MICM and DAFM in the second stage. A Supervised Attention Module (SAM) [44] is used to bridge the features from the first stage to the second one for further processing.Both stages comprise an encoder-decoder architecture with two downsampling layers and two upsampling layers. A skip connection followed by a 3x3 convolution is employed between the encoder and decoder features. A ResBlock consists of two 3x3 convolution layers, each followed by a PReLU [19] .Our MICM addresses inconsistency by progressively calibrating the cross-spectral features in a coarse-to-fine manner. As illustrated in Fig. 2 , given the guidance feature 𝐺 ∈ R 𝐶 ×𝐻 ×𝑊 and pre-denoised feature 𝑁 ∈ R 𝐶 ×𝐻 ×𝑊 , we leverage average pooling (AP) with different downsampling rates to transform them into different spaces.Here, AP can serve as a low-pass filter, which significantly reduces the impact of noise at lower scales, and achieves effective receptive fields for large-scale inconsistency.In the three branches of the downsampling part, we apply a spatial attention module (SA) to the cross-modal features to calibrate the inconsistency. To be specific, SA leverages a simple convolutional layer to fuse the cross features and distributes them to distinct convolutional layers for generating attention weights adaptively. The process of SA is expressed as:EQUATION[•, •] denotes the concatenation operation, S is the sigmoid operation. Through SA, we can obtain the reweighted features for mitigating the inconsistency. Then, the calibrated features are upsampled to the original size and aggregated with the initial features through skip connections, followed by a convolutional layer for summarizing. The calibration process is applied to both noisy features and guidance features. For brevity, we provide a concise description of the calibration process for the guidance features:EQUATIONwhere, 𝑖 ∈ {1, 2, 3}, Ĝ0 = 0, AP 2 4-𝑖 represents the average pooling operation with a downsampling rate as 2 To extract different frequencies of the features, a straightforward way is to utilize wavelet transform or Fourier transform. However, it is difficult for these tools to distinguish the frequency components to be enhanced or suppressed and they require additional computational overhead to transform features back to the spatial domain [9] . Instead, we use adaptive frequency filters [12] to yield high/low input frequencies. For high-frequency extraction, we divide features into several groups and apply depthwise convolutions with different kernel sizes to each group to simulate the cutoff frequencies in different high-pass filters. For low-frequency extraction, we use average pooling as low-pass filters. We apply the frequency extraction above to both noisy features and guidance features. Then, high/low frequencies are fed into the local and global branches respectively to accentuate the informative signals.High-frequency local branch. Adaptive high-pass filters with different kernel sizes possess distinct receptive fields, enabling the differential extraction of high-frequency components and the provision of varied local information. We employ dynamic highpass filters on both 𝑁 and 𝐺. For the 𝑖-th group of 𝑁 , 𝑁 𝑖 , we can obtain the corresponding high frequencies 𝑁 ℎ 𝑖 viaEQUATIONHere, H 𝑘 represents the depth-wise convolution with kernel size 𝑘 × 𝑘, 𝑘 ∈ {3, 5, 7}. The high-frequency features 𝑁 ℎ and 𝐺 ℎ are yielded by concatenating all groups. Then we multiply 𝑁 ℎ and 𝐺 ℎ and fed the result into a modified squeeze-and-excitation block to obtain a local attention map asEQUATIONWhere 𝑊 1 and 𝑊 2 denote parameters of the fully connected layers. We utilize the Tanh function instead of the sigmoid function, since Tanh projects attention weights into (-1, 1), the negative weights can help suppress the detrimental frequencies. The resulting attention map is subsequently multiplied with the initial high frequencies 𝐺 ℎ . Then we can obtain the high-frequency output.Low-frequency global branch. We use channel-wise cross attention to capture the useful global low frequencies, where the queries (𝑄) come from the noise branch and the keys (𝐾) and values (𝑉 ) come from the guidance branch. The attention map is generated EQUATIONwhere 𝑄 𝑁 ∈ R 𝐶 ×𝐻𝑊 , 𝐾 𝐺 ∈ R 𝐶 ×𝐻𝑊 is generated by applying 1 × 1 convolutions to the 𝑁 and 𝐺 respectively, while 𝑉 𝐺 ∈ R 𝐶 ×𝐻𝑊 is produced from 𝐺 with 1 × 1 convolution. P denotes the average pooling operation with a downsampling rate of 2. The channel-wise CA can also provide lower complexity than the spatial version. In addition, we deploy the multi-head mechanism [37] to further save the complexity and enhance the diversity of feature spaces. Finally, we integrate the initial noisy features with the outputs of the two branches, then feed the result into the DFFN [24] for further frequency refinement.We adopt the Charbonnier loss [5] in both spatial and frequency domains to facilitate dual-domain learning:EQUATIONEQUATIONwhere X1 and X2 are the outputs of the pre-denoising sub-network and the whole network, respectively; 𝑌 is the reference; and F is the fast Fourier transform. The final loss function is given by L = L 𝑠 + 𝜆L 𝑓 , where 𝜆 is set as 0. Tab. 1 shows that our method achieves the best results across three noise levels. The qualitative comparisons in Fig. 3 clearly illustrate the significant advantages of our method over other state-of-the-art approaches on noise removal, artifact suppression, and detail recovery. For single-image denoisers like NAFNet, have achieved satisfactory results on weak-noise datasets. However, as the synthetic noise intensity increases, NAFNet has to sacrifice a large amount of edges and details. For the guided denoising methods, SVLRM and CUNet inevitably yield partially denoised results when confronted with strong noise. Moreover, they fail to handle the inconsistency between RGB and NIR image pairs, resulting in severe artifacts. DVN effectively removes noise but fails to estimate the complete structure of the target image, consequently, the details of the NIR image cannot be fully integrated into the RGB image.By contrast, our RFFNet exhibits strong denoising capabilities and effectively suppresses artifacts. Furthermore, we extract useful frequencies from the NIR image and selectively aggregate edges and textures, yielding results with rich details. Flash-guided no-flash image denoising. We perform validation on the test set of FAID [1] under Gaussian noise with 𝜎 ∈ {25, 50, 75}. For this task, in addition to the methods mentioned above, we compare our RFFNet with DeepFnF [39] and RIDFnF [31] , which are elaborately designed for flash-guided denoising. To further demonstrate the superiority of our method, we additionally provide the flash-guided denoising results of a small version (-s) by changing the channel count of three scales from {64, 96, 128} to {32, 44, 56}, which has only 0.99 M parameters. Tab. 2 shows that our RFFNet, even RFFNet-s, outperforms all comparative methods in terms of PSNR, SSIM, and LPIPS under different noise levels.Fig. 4 shows that Restormer over-smooth the details. Guided denoising methods, including CUNet, DeepFnF, and RIDFnF, produce ghosting artifacts in the bottom two images due to the shadows in the flash image. By contrast, RFFNet completely removes noise and preserves fine structures faithful to the ground truth. Moreover, as shown in Fig. 5 , our method yields fewer artifacts and better details compared to other fusion-based methods in the misaligned case.Besides, as listed in Tab. 3, both RFFNet and RFFNet-s demonstrate computational efficiency and possess a smaller model size compared to the state-of-the-art denoisers.Real-world low-light image denoising. We conduct qualitative experiments on RGB-NIR pairs captured in real low-light environments. As shown in Fig. 6 , the details of the RGB image (brightened for display) are obscured by dense noise, and the NIR image exhibits significant highlights and shadows. Restormer introduces a large amount of artifacts. Both CUNet and DVN struggle to address artifact suppression and denoising. Our method effectively eliminates inconsistency and introduces richer details. 
ablation studies: 
 We conduct ablation studies with RFFNet on the FAID dataset [1] under 𝜎 = 75 to investigate the influences of our proposed components. The results of the MICM and DAFM are presented in Tab. 4.Influence of each component. For the network design, we compare the 2-stage architecture, MICM, DAFM, the frequency loss, and the full model with the baseline model. Specifically, for the baseline, we utilize a single U-Net to be the backbone and apply addition operation for fusion in multi-level encoders. By deploying MICM, the model receives a performance gain of 0. and facilitating the calibration process, leading to a 0.27 dB improvement (Tab. 4 (c)). Then, we apply DAFM instead of addition, the model improves by 0.29 dB (Tab. 4 (d)). To facilitate the frequency learning, we introduce the frequency loss L 𝑓 and obtain a gain of 0.09 dB (Tab. 4 (e)). Finally, all our contributions together yield a substantial improvement of 0.94 dB in PSNR over the baseline, setting the new state-of-the-art in low-light image denoising.The Effectiveness of MICM. As shown in the example of Fig. 7 , the full model with MICM handles the inconsistent regions better. To further demonstrate the effectiveness of the coarse-to-fine manner, we do experiments by progressively adding downsampling branches of MICM, leading to higher scores (Tab. 5).w/o MICM Full model Compare with other fusion strategies. We compare DAFM with typical fusion methods such as window multi-head cross attention (W-MCA) and channel-wise multi-head cross attention (C-MCA). DAFM achieves a minimum improvement of 0.12 dB while increasing the parameters by only 2.5% (+0.11 M). 
conclusion: 
 In this paper, we propose a two-stage framework, dubbed RFFNet, for low-light image denoising based on a robust and flexible fusion strategy. Specifically, our Multi-Scale Inconsistency Calibration Module transfers cross-modal features into multi-scale spaces and applies spatial attention in each branch to calibrate inconsistency in a coarse-to-fine manner, ensuring the robustness of the fusion process. Moreover, we develop a Dual-Domain Adaptive Fusion Module to excavate more information from the guidance image by extracting high/low frequencies from the cross-spectral features and adaptively emphasizing the informative frequency components. Extensive experiments on NIR-guided RGB image denoising and flash-guided no-flash image denoising demonstrate that our method outperforms state-of-the-art algorithms in terms of noise removal, artifact suppression, and detail recovery. 
 
 Appendix:  


Decision: All reviewers have decided to accept this work. AC agrees with each reviewer and decides to accept this paper. The authors are suggested to consider all suggestions in the camera ready.

Reviews: review: paper_summary: paper_summary: This paper presents RFFNet, a two-stage framework for low-light image denoising, featuring a robust and flexible fusion strategy. The Multi-Scale Inconsistency Calibration Module (MICM) transfers cross-modal features into multi-scale spaces, employing spatial attention to calibrate inconsistency in a coarse-to-fine manner. Additionally, the Dual-Domain Adaptive Fusion Module (DAFM) extracts high/low frequencies from cross-spectral features, emphasizing informative frequency components adaptively. Experimental results on NIR-guided RGB image denoising and flash-guided no-flash image denoising showcase superior performance in noise removal, artifact suppression, and detail recovery compared to state-of-the-art algorithms.
strength_weakness: strength_weakness: 1. This paper demonstrates good readability, with clear problem analysis and a well-structured, logically coherent solution approach.
2. The paper tackles the fusion of noisy low-light images with guide images in an interesting and novel manner. While the use of well-known attention modules in RFFNet somewhat limits the novelty in module design, the adjustments made to these modules reflect the authors' deep understanding of guided image restoration. I appreciate these adjustments and think that it maches the tone of ACM Multimedia.
3. The experimental results presented in this paper are impressive. There are significant advantages in terms of quantitative metrics, visual effects, and computational overhead.
limitations: limitations: I found the ablation study in Table 4 to be quite confusing. Is it reasonable for Unet to achieve a PSNR of 33.97dB as a baseline? This seems somewhat high and deviates from common expectations. Would retraining other methods using the training schedule proposed in this paper lead to different conclusions?
This observation undermines my trust in the fairness of comparisons in this paper. If the authors could address my concerns, I would be inclined to give a higher rating.

By the way, the template of this paper is strange. I'm not sure if it should be considered when rating.
paper_summary: paper_summary: This paper proposes a fusion network for low-light image denoising, and a multi-scale inconsistency calibration module is introduced to guide the feature calibration in a coarse-to-fine manner. The work also develops a dual-domain adaptive fusion module to extract useful information from the guidance features. experimental results demonstrate the effectiveness on NIR-guided RGB image denoising and flash-guided no flash image denoising.
strength_weakness: strength_weakness: 1. The writing of this paper is easy to understand.
2. The experiments are sufficient and the visual results and quantitative metrics verify the effectiveness of this method.
3. The description of the methodology is detailed.
limitations: limitations: 1. The motivation of the paper requires more detailed argumentation, such as whether the approach for extracting high and low frequencies is reasonable in DAFM.
paper_summary: paper_summary: The RFFNet paper addresses a crucial challenge in low-light image denoising by introducing a novel fusion network that leverages guidance images to enhance noise reduction and detail preservation.
strength_weakness: strength_weakness: 1.  The paper proposes a unique approach that integrates a Multi-Scale Inconsistency Calibration Module (MICM) and a Dual-Domain Adaptive Fusion Module (DAFM) to tackle inconsistencies in guided image denoising.
2. The paper reports superior performance over current state-of-the-art methods on both NIR-guided and flash-guided denoising tasks.
3. The architecture cleverly addresses both high and low-frequency components in the image denoising process, potentially setting a new benchmark in the field.
limitations: limitations: 1. There are many grammar problems, typos, and format errors.
2. The contributions of this work seem incremental.
3. It’s better to add some visual results to verify the contribution of each loss function term more intuitively and clearly.
4. Discuss the limitations of the proposed method more thoroughly, including potential failure cases, and suggest future research directions to overcome these limitations.
paper_summary: paper_summary: The authors of this paper propose a robust and flexible vision network (RFFNet) for low light image denoising. Specifically, a multi-scale inconsistency calibration module is proposed to process the inconsistent features into the multi-scale space before fusion by first mapping guidance, and to pre-denoise the features in a coarse-to-fine manner. In addition, by designing a dual-domain adaptive fusion module, adaptive extraction of useful high/low frequency signals from guidance characteristics and then highlighting information frequencies is realized. A large number of experimental results verify the effectiveness of the method.
strength_weakness: strength_weakness: The main contributions of the paper include the proposed multi-scale inconsistencies calibration module to deal with local inconsistencies, and the design of a two-domain adaptive fusion module to adaptively fuse information. 
The experimental part shows the advantages of RFFNet in visual effects and performance indicators , as well as in computational efficiency.
limitations: limitations: 1. The formula modeling in this paper is incomplete. In formula 1, only additive noise is considered without multiplicative noise, which is not consistent with the noise distribution in the real low-light scene.

2. The setting of this paper is simple and does not conform to the real situation. In the setup of this paper, it is considered that the guided NIR or flash image is perfectly aligned with the GT image, which is a very simple setup. However, in the real world, most of the guide images should not be perfectly aligned. For this more complex setup, the authors present only a visual comparison result that lacks a GT image and does not show any metric results, which is unconvincing.

3. The experimental exposition of this paper is not clear. For the experiments on FAID dataset, the author did not fully explain whether the data used was RAW or RGB, and it seems that the adding  noise method on FAID dataset is not same with that on DVD dataset or other compared methods such as deepFnF, which does not meet the setting of dark light image denoising. Authors are advised to fully explain the practices and the reasons for the differences.

4. This paper does not seem to have strong relevance to the MM conference. Specifically, there is no MM application or context, no actual MM results, no MM-related references, and even no mention of multimedia in the full text.