Abstract

General Background: Deep image matting is a fundamental task in computer vision, enabling precise foreground extraction from complex backgrounds, with applications in augmented reality, computer graphics, and video processing. Specific Background: Despite advancements in deep learning-based methods, preserving fine details such as hair and transparency remains a challenge. Knowledge Gap: Existing approaches struggle with accuracy and efficiency, necessitating novel techniques to enhance matting precision. Aims: This study integrates deep learning with fusion techniques to improve alpha matte estimation, proposing a lightweight U-Net model incorporating color-space fusion and preprocessing. Results: Experiments using the AdobeComposition-1k dataset demonstrate superior performance compared to traditional methods, achieving higher accuracy, faster processing speed, and improved boundary preservation. Novelty: The proposed model effectively combines deep learning with fusion techniques, enhancing matting quality while maintaining robustness across various environmental conditions. Implications: These findings highlight the potential of integrating fusion techniques with deep learning for image matting, offering valuable insights for future research in automated image processing applications, including augmented reality, gaming, and interactive video technologies.

Highlights:

1. Better Precision: Fusion techniques enhance fine detail preservation.
2. Faster Processing: Lightweight U-Net improves speed and accuracy.
3. Wide Applications: Useful for AR, gaming, and video processing.

Keywords: Deep image matting, computer vision, deep learning, fusion techniques, U-Net

Introduction

Image matting is a practical task that requires the estimation of the opacity of each pixel to extract foreground elements from the background. For proper insertion and composition, object matting needs to be highly accurate, but classical methods struggle with low details and fuzzy boundaries in weakly supervised settings. More recently, deep learning methods are well-suited to this task. The aim to improve the performance of deep learning image matting on high-resolution inputs. Deep learning is key for results but fusion techniques can improve the outcomes further. But only a limited methods mix DL with fusion techniques. Recent Method Designs How to Go About [1]. Alpha matting Deep Image Matting Deep image fusion Abstract Deep image matting techniques based on deep neural networks have emerged aiming at accurate alpha matte estimation. Instead of using regular guided filter approaches, these methods capture spatial and structural information which is then used to improve image quality and object representation[2]. This paper is on future works, namely context-aware attributes based on a context-adjusted network learnt from different multi-image visualizations. It allows Fusion Net to work better than current fusion methods in deep learning frameworks. In conjunction with Fusion Net, Net presents powerful designs for multi-image representation systems, achieving photorealistic reconstructions which are robust across two datasets. In addition, the technique deals with difficulties of alpha prediction based on spatial scale, which improves visual quality. In summary, this approach demonstrates powerful performance in simulated multi-task deep matting and 3D digital media tasks[3].

- Image Matting

Matting generally means segmentation of a photo-realistic composition of several objects keeping an identical background and accurately trimming an actual object from a photo-realistic composition of several other objects, especially in a complicated background. This is one of the most crucial step in various fields e.g. visual effects, augmented reality applications and computer graphics[4].The main difficulties that need to be resolved are creating thin lines around the object in alpha matte line. Image matting has been researched for decades and although many classical, machine learning and DL methods have been proposed, high quality image matting with desired transparency treatment or thin structures for hair details, is still a challenging problem[5]. Image matting — a valuable topic in computer vision — has been affected by several traditional techniques such as triangulation based matting, closed-form matting, learning based matting, etc. One common technique is called as alpha matting, where it estimates the parameters which can be given through foreground color and estimates the unknown alpha channel directly from the image[6]. Moreover, even though plenty of alpha matting methods have been proposed, such as image-based, gradient-based, closed-form, preview, and learning-based classics, they often require user intervention to achieve satisfactory performance, suffer from color distortion particularly when similar colors are present in foreground and background, and cannot process occlusion boundary simultaneously. Deep Image Matting, Deep Grab, Context–Aware Image Matting, Deep Semantic Guided Matting and Learning to Extract the Matting Foreground are some of the techniques that accelerate the quality of an image[7]. Fig.1(a, b): show the concept of image matting

Figure 1. Explain the concept of Image Matting

- Deep Learning

Deep learning is a subset of machine learning that has revolutionized image processing and has been applied to many different tasks including matting applications. Since the neural network is the main component of deep learning, even the neural network can be classified in various ways, such as in terms of layer type, data flow, etc. The convolutional neural network (CNN) is one of the most popular types of neural networks as it was proposed for the analysis of visual imagery and is structured and commissioned in layers that are specific for image or feature map processing. Since then, it has been adopted with image pertinent tasks and has driven many forward steps[8]. This model-level automatic feature-selection process based on end-to-end task optimization is a major benefit of deep learning versus traditional machine learning methods and typically results in better accuracy. In contrast to traditional algorithms where specific low-level image features need to be manually identified and extracted as part of the preprocessing stage, deep learning techniques are able to learn this process, along with giving a high-level and abstraction output features[9]. This capability enables the model to process image images in an efficient manner, as well as to process large quantities of image data for high-quality task. This is especially relevant for real-time applications, as is the case with most image matting algorithms. Traditionally these models were handcrafted with some domain expertise but more recent trends have shifted to more complex deep architectures that adapt better to the data leveraging more sophisticated training approaches[10]. Deep learning approaches can be formulated as a set of machine learning techniques designed to automatically extract and understand low- and high-level abstractions in data [11] [12]. CNNs have shown great progress in this area, they are able to learn hierarchical features automatically, which makes them more powerful and adaptable to different conditions [13].

- Fusion Techniques

Fusion technique are algorithms that aggregate information from different non-overlapping information sources. This is also one of the reasons that, although the powerful matting results brought on by pioneering methods such as are witnessed, new diverse matting methods are proposed either for integrating different datasets or methodologies in big effort of helping us tightening the gap toward more accurate and robust results. Fusion may occur at the data or feature level, for instance, pixel-level fusion combines different datasets while feature-level fusion fuse different approaches for the same system.[14]. This approach has been studied in several contexts and has found most success in matting. Using feature-level fusion for recognition most of the studies are dominated for biometric based fusion study with iris based recognition utilizing spatial and frequency domains as its matrices. Neuro-fuzzy and multi-scale strategies are examples of this approach. There are nevertheless some studies which use single-source or multi-sourced datasets, applying traditional approaches. Fusion merges individual data lures into an aggregate. While there have a few cutting-edge works in pixel-level and feature-level matting using fusion technique, the single and multi-source fusion is still very challenging. These realistic challenges must be addressed with advanced fusion measures, feature learning, and optimization due to such problems as color discrepancies, complex object shapes, camera planning, and lighting effects[15]. Multi-modal biometric systems provide better recognition performance than systems that rely on a single biometric pattern. Fusion technology is essential and effective for combing information in a multi-modal biometric system [16]. Fig.2(a, b, c): show the fusion techniques.

Figure 2. illustrating the Fusion Techniques .

Literature Review

Related Work

The related work is brief to fully put our work into perspective and consists of two parts: a literature review to motivate our work, and a representation of the tasks and contributions. The goal of this dedication is to develop a discussion set up according to the approximate procedure for finding a research problem[17]. Explore Image Matting has been researched for years, after which its classic tradition algorithms can be roughly grouped into two categories, i.e., trimap based methods and learning based methods. This classification allows to explain the variability between models. In the past two decades, a variety of both semi-automatic and fully automatic techniques have been developed. Development of deep learning methods that classify the languages and structures of certain datasets with ease, but are data hungry. Deep networks have been proposed to generate alpha mattes that allow for semantic prediction details being guided by those mattes in earlier works[18].

Deep learning-based methods have proved to be far more elegant and robust in addressing complex problems in many computer vision applications than traditional methods due to their high descriptive power. In addition, we have investigated many classical methods based on trimaps for image matting and included them in the appendix. So, instead of presenting the contribution in the Restoring Local Contrast and Details in Image Matting section as its sub-method[19]. They also touch on the angle that their work is not worth less. These details should be included in Related Works section where we will elaborate on a wide overview of various existing techniques and algorithms applied to image matting. This segment reflects their strengthen in designing and developing their proposed MAT-Net in our paper only through this way[20].

Aim of Research

In this paper initial aim is to enhance the precision of extraction of foreground objects from the background (if background is complex) by integrating deep learning and fusion techniques. The goal is to introduce the new lightweight U-Net model in order to improve the alpha matte quality that is more reliable for many image processing works. Besides, it aims to compare the performance of the proposed model with conventional ways and prove its edge over image matting tasks.

Methods

The Proposed Model

Our proposed methodology to solve these challenges: To enhance the performance of the deep image matting methods, we systematically solve these challenges by the following steps. Stage implementations are those that place automated markers directly on the stage. These moves can lessen the demands of deep learning when the territory in the unwound limit is needed. These steps combine the power of the classical technique with recent machine-learning advances. Moreover, a data preprocessing step could potently improve the alpha matte quality for generating the training and validation dataset. Classical algorithms could be more efficient for alpha matte generation. On the other hand, we introduce a lightweight network architecture for generating the alpha matte based on deep learning. Overall, the proposed deep image matting is new and can be used to fill seamlessly with the previous work which would be a good area for future work.

Data preprocessing for generating alpha matte as label for deep image matting : The generated alpha matte is not an exact replica of the trimap needed in the matting phase. We can nevertheless use the bright regions of the result of the dehazing as markers. The bright region mainly exists inside the opaque foreground that can indicate the potential region of the foreground part. This is a technique that can be defined under the form of 0 < α ≤1, where 1 indicates high emission bright value and low haziness. To put it differently, the process involved in the proposed method consists of three fundamental procedures which include (1) Division of hazy images and their corresponding atmospheric lights using an effective dark channel results for preprocessing of data, (2) The pre-training of the deep network with respect to the supervise net, separately for their iteration and configurations, and (3) Modified deep network image dehazing based on learned model implemented in tejask1999further; tejask199195. Hence we used the estimated cut-point from first stage to generate the foreground and final alpha matte in full-res. So, We built up the pipeline in a second stage of infrastructure which is a lightweight U-net network with a factorized convolution as a color embedding to boost the efficiency. In this first stage, the fusion naturally brings the original color space and the estimated alpha matte. The used conceptual sharpen and RGB value is incorporated into the segmentation network. Using a greyscale factor path will help the network to learn the salient parts. In this way, all the values are merged to feature fusion path to output alpha matte. Fig.3 illustrating the operation of the proposed model.

Figure 3.illustrating the operation of the Proposed Model.

Network Architecture

We then adopt a self-attentive fusion module to explicitly make use of pixel-level fine features for local detail estimation. Concretely, we proposed a multi-resolution self-attention module to improve the deep feature based on the high-level feature map and low-level aware part. We also propose the Dense Context Residual Network to address the sparse mapping problem. Dense Context Residual Network utilizes several Dense Context Blocks for the residual network. The residual connections to predict the coarse alpha matte map is built from convolutional layers in each Dense Context Block We jointly train the Dense Context Residual Network with a composite loss containing the local and global estimates. By retaining details, the local estimator contributes a great deal to the visually pleasing alpha matte, while the global estimator remedies common errors in small and unknown object regions. By training these two estimators jointly, it can circumvent the commonly known self-contradiction problem in the trimap or scribble. We conduct extensive experiments to show the state-of-the-art matting performance of our method, and analyze our designed net-work through detailed ablation studies. On an image of size 2048x1382, run time is 2.3 seconds. Fig.4: show the network architecture used in the proposed model.

Figure 4. illustrating the Network Architecture used in the Proposed Model .

Experiment Setting

Data set

AdobeComposition-1k: 1000 images of 50 different foregrounds. The images are for training and testing the model and consist of multiple objects against complex backgrounds[21]. Generally, each image in the dataset is accompanied by a trimap that is separated into three regions.

1. One is Foreground Region: Part of image region that contains the objects to be extracted.

2. Background Region: Region that represents background.

3. Unknown Region: The area in which the algorithm has to estimate the alpha matte. Fig.5(a, b, c, d, e, f): shows an example of the dataset:

Figure 5. illustrating the example of the dataset [22].

Data Preprocessing

Input data processing the incoming data has to go through many processing steps to be able to utilise them in our framework. A training set is one of the first essentials in the framework of the deep learning model. The inadequate performance of the deep learning basemethod will deliver imperfect image matricing results when the users utilize the trimaps of lower quality. For collecting a suitable training set, a media application based on a 3D model. This is why we can handler every right-indoor scene in together and can release it in one data set. Therefore, in this analysis the shadow removal method is not implemented. Stratified sampling also splits each of the training datasets into four parts: training data covers 70% of sample space; and validation data and testing data cover 15% each. To make our learning model more robust, we have taken a number of steps to reduce environmental variability. Make the scale consistent throughout all datasets and set both sides as the input to the encoder network. Leaving aside that, there is still a lot of data left for the network as input. There are several loading mechanism assigned to these networks such as image horizontally flipped with probability of 0.2, image vertically and horizontally shifted in the predetermined amount for an assignment, random rotation, scale, shifts to the RGB and saturation channel to standardize luminosity values, random crop (0.8 scale). Now currsize = target size For each detail, sort the 9 × 9 pixel trimap. And the trimap value for the four labels is 240.358. The matrices and their corresponding ground truth are subsampled or alpha composited to reduce the resolution. Three different methods are used in this design. To ensure guidance provided does not bias towards certain areas of the material, uniform intervention and trimming rules are now in place. There is a lot of knowledge on how much data quality matters in making sure that we get a great trained network at the end. Our approach leverages semantic difference between overlapping areas, and thus we can undersample informative features. In multi-distraction settings, we attain the highest results and this based on this technique. The background of the network is learned through self-supervised training.

Results and Discussion

Experiments and Results of the Proposed Model

Experimental Setups We discussed the proposed method with the image matting datasets. We divided the datasets into the training set and test set with a ratio of 4:1, as in the previous sections. The parameters in this experiment are optimized based on the training set and tested on the test set. For the progressive training framework, we also conducted these experiments using the matting Laplacian for guiding blurry regions. The training is regularized with the weight of the structure smoothness term, α = 1, using the matting Laplacian provided in [6].

To make a quantitative analysis of the performance of our method, we test our method together with four state-of-the-art methods on the two datasets respectively: MSD-Trimap, DIM100, DIM927 and IndexL0. Additionally, a comprehensive comparison against existing state-of-the-art methods on diverse datasets is performed to assess the superiority of our method. In Table 1 we report a summary of the qualitative and quantitative experiments results and list their processing time.

- Table 1 Description:

1. Method: The method used (our proposed model and other methods).

2. Dataset: The dataset used in the test.

3. MSE: Mean Squared Error.

4. SAD: Sum of Absolute Differences.

5. Gradient Error: Gradient error.

6. Processing Time (s): Processing time in seconds.

Table 2 Shows a comparison of the proposed model with traditional methods with various metrics using the same dataset used in the proposed system.

Details of the Table 2 :

1. MSE (Mean Squared Error):

a. Traditional Methods: 0.025

b. Proposed Model: 0.010

c. MSE — Mean Squared Error: The smaller the value of MSE, the more accurately the Alpha Matte is estimated. As we can see, the introduced model provides significant improvements in MSE because of the new advanced tricks such as Dark Channel Prior and the modified U-Net.

2. PSNR ( Peak Signal-to-Noise Ratio):

a. Traditional Methods: 28 dB

b. Proposed Model: 32 dB

c. Definition: A higher PSNR indicates the better quality of the output image. There is a clear gain in PSNR with the proposed model.

3. SSIM (Structural Similarity Index)

a. Traditional Methods: 0.85

b. Proposed Model: 0.92

c. Reason: SSIM evaluates the structural similarity of two images. The fused version obtains a considerable increase in SSIM over the initial model due to the color and grayscale fusion.

4. Processing Speed:

a. Traditional approaches: 2.5 seconds/image

b. Average inference time per image Proposed Model: 0.8 seconds/image

c. Reason: The speed of the model is significantly increased because of its light-weight nature which is known as Lightweight U-Net.

5. Boundary Region Accuracy:

a. Traditional Methods: 75%

b. Proposed Model: 90%

c. Reason: An earlier outlined model leads to increased precisions at the regions boundary (e.g., hair, or soft edges).

6. Performance on Hazy Images:

a. Traditional approaches: 60% accuracy

b. Proposed Model: 85% Accuracy

c. Use of Dark Channel Prior makes the proposed model more efficient in dealing with hazy images.

Table 3 shows the performance of the proposed model with the correlation matrix, assumptions to Construct the Correlation Matrix:

1. Key Variables:

a. Quality of Input Image – Image quality (clarity, color quality, etc.)

b. Accuracy of generated Alpha Matte

c. Processing Speed: Time for image processing (Seconds)

d. Edge Fidelity: Fidelity in areas near to the boundaries (like hair or soft edges).

e. Hazy image handling: How well the model is performing in the hazy image.

2. Measurements:

Correlated variables are measured using Correlation Coefficient; from − 1 to 1:

a. Perfect positive correlation.

b. 0: No correlation.

c. -1 : Strong negative correlation.

- Understanding the Correlation Matrix:

1. Input Image Quality:

a. Implement a more realistic haze image (0.75) handling, high Precision in Haza boundary (0.80), and Alpha Matte Accuracy(0.85), which very strongly correlated to each other.

b. That is a good input image will effect the Alpha Matte accuracy and boundary accuracy and the models good performance on hazy image.

2. Alpha Matte Accuracy:

a. Highly correlated with Border Precision (0.90) and Blurry Image Management (0.85).

b. This means that a refinement in Alpha Matte translates into a better boundary, and good performance over hazy images.

3. Processing Speed:

a. None correlated with other variables weak (all are lower than 0.15 and even some are slightly negative).

b. That is, input image quality or Alpha Matte accuracy does not affect processing speed.

4. Boundary Precision:

a. Highly correlated with Alpha Matte Accuracy (0.90) and Handling of Hazy Images (0.80).

b. Which implies that boundary refinement is mostly limited by Alpha Matte Quality and the model's result on blurry images.

5. Hazy Image Handling:

a. Highly correlated to Alpha Matte Accuracy (α=0.85) and Boundary Precision (α=0.80).

b. alpha matte accuracy and boundary accuracy, thus a hazy image is also a test for the model.

Figure 6.Show Confusion Matrix Heatmap.

Conclusions

This paper investigates a novel deep image dressing model based on fusion techniques to accurately extract foreground objects from complex natural backgrounds. The proposed model combines deep learning and fusion methods and outperforms conventional techniques and the resulting images have better quality. This demonstration reveals that the model not only maintains its high accuracy over fine details but also over a wider range of environmental conditions, which can be incorporated into a number of image processing applications.

References

1. J. Li, J. Zhang, and D. Tao, "Deep image matting: A comprehensive survey," arXiv preprint arXiv:2304.04672, 2023.
2. H. Yu, N. Xu, Z. Huang, Y. Zhou, and H. Shi, "High-resolution deep image matting," in Proc. AAAI Conf. Artif. Intell., 2021.
3. S. Iqbal, A. N. Qureshi, and M. Alhussein, "Hybrid deep spatial and statistical feature fusion for accurate MRI brain tumor classification," Frontiers in AI, 2024.
4. Y. Liang, Q. Fu, Z. Kun, and G. Liu, "Enhancing transparent object matting using predicted definite foreground and background," IEEE Trans. Image Process., 2024.
5. J. Yao, X. Wang, L. Ye, and W. Liu, "Matte anything: Interactive natural image matting with segment anything model," Image Vis. Comput., 2024.
6. S. Lutz and A. Smolic, "Foreground color prediction through inverse compositing," in Proc. IEEE/CVF Winter Conf. Appl. Comput. Vis., 2021.
7. M. H. Mahmood, L. T. Lim, and D. A. Awang Mat, "Deep learning in the grading of diabetic retinopathy: A review," IET Comput. Vis., 2022.
8. L. Wang et al., "Medical matting: Medical image segmentation with uncertainty from the matting perspective," Comput. Biol. Med., Elsevier, 2023.
9. A. Saber, M. Sakr, O. M. Abo-Seida, and A. Keshk, "A novel deep-learning model for automatic detection and classification of breast cancer using the transfer-learning technique," IEEE Access, vol. 9, pp. 1-14, 2021.
10. S. Lin, L. Yang, and I. Saleemi, "Robust high-resolution video matting with temporal guidance," in Proc. IEEE Int. Conf. Comput. Vis., 2022.
11. A. S. Qaddoori, J. H. Saud, and F. A. Hamad, "A classifier design for micro bubble generators based on deep learning technique," 5 Nano, Exp. Concern, Part 5, Proc., vol. 80, no. 3, p. 1705, 2023.
12. S. Liqaa M. and J. H. Saud, "Deep video understanding based on language generation," Int. J. Cloud Comput. Database Manage., vol. 6, no. 1, pp. 9-15, 2025.
13. M. Talib and J. H. Saud, "A multi-weapon detection using deep learning," Iraq J. Inf. Commun. Technol. (IJICT), vol. 7, no. 1, Apr. 2024.
14. X. Zhang, H. He, and J. X. Zhang, "Multi-focus image fusion based on fractional order differentiation and closed image matting," ISA Trans., 2022.
15. J. Wang, X. Jiang, Y. Zhang, J. Yin, and Z. Yang, "TG Matting: Automatic image matting based on trimap generation," J. Comput. Sci., 2024.
16. S. A. Tuama, J. H. Saud, and Z. A. Alobaidy, "Using an accurate multimodal biometric for human identification system via deep learning," Al-Mansour J., Special Issue HICNAS2022.
17. B. Mauer and J. Venecek, "Writing the literature review," in Strategies for Conducting Literary Research, 2022.
18. S. Kazi, "Fruit grading, disease detection, and an image processing strategy," J. Image Process. Artif. Intell., 2023.
19. P. C. Su and M. T. Yang, "Integrating depth-based and deep learning techniques for real-time video matting without green screens," Electronics, 2024.
20. Y. Liu, J. Xie, X. Shi, Y. Qiao, and Y. Huang, "Tripartite information mining and integration for image matting," in Proc. IEEE Int. Conf. Comput. Vis., 2021.
21. H. Gu, H. Dong, N. Konz, and M. A. Mazurowski, "A systematic study of the foreground-background imbalance problem in deep learning for object detection," arXiv preprint arXiv:2306.16539, 2023.
22. N. Xu, B. Price, S. Cohen, and T. Huang, "Deep image matting," arXiv preprint arXiv:1703.03872v3, 2017.