Upscaling Image using Fast Super Resolution Convolution Natural Network

Small Image can Upscale using FSRCNN

1. 1 /24 OUTLINES Abstract 1 Introduction 2 Motivation 3 Flowchart of Model Training 8 Proposed Our Dataset 7 Proposed Architecture of CFSRCNN 6 Background Theory 5 Flowchart of Model Testing 9 Conclusion 11 Aim 4 Experiment and Experimental Results 10

2. ABSTRACT The Custom Fast Super Resolution Convolutional Neural Network (CFSRCNN) model is suggested in this paper. Unlike other FSRCNNs, our model upscales images fourfold while maintaining quality and processing speed. In this paper, the researcher utilized the proposed architecture and our own dataset for four times the original size upscaling of tiny images. According to the experimental findings, CFSRCNN is a useful tool for a variety of image improvement applications because it can upscale small images to four times their original size. 2 /24

3. INTRODUCTION The FSRCNN model's CNN convolutional neural network architecture is designed to efficiently upscale images with minimal computational effort. The model's lightweight design enables real-time performance on various platforms, making it suitable for real-world applications, including image restoration, video upscaling, and other related tasks. In this research, the FSRCNN model is trained on a diverse dataset of low-resolution images and corresponding high-resolution actual data. Experimental results demonstrate its superiority over traditional interpolation-based methods and competitive performance compared to more complex super-resolution models. The findings highlight FSRCNN's effectiveness as an efficient solution for upscaling small images, rendering it a valuable tool for enhancing image quality across various applications. 3 /24

4. MOTIVATION With the increase in crimes around the world, when looking for suspects from CCTV, the images taken from a distance are small images, so an Upscaling Algorithm is needed to make the small images into large images in order to see those small suspects clearly. In the field of deep learning, there are many algorithms that can upscaling images like this, but we need an algorithm that can quickly and real time upscaling without reducing the quality of the original image. 4 /24

5. AIM To enhance image quality by increasing resolution, specifically improving the quality of low-resolution inputs by fourfold using the proposed Custom Super-Resolution Convolutional Neural Network architecture (CFSRCNN). 5 /24

6. Background Theory 6 /24

7. Upscaling Image Super Resolution Using Deep Learning There are several techniques for image super-resolution for deep learning, including FSRCNN, EDSR, ESPCN, and LapSRN. The FSRCNN (Fast Super Resolution Convolutional Neural Network) is a deep learning model designed for image super-resolution tasks, particularly upscaling low-resolution images. EDSR stands out for its high accuracy, yet it suffers from slow processing speed and large file sizes. ESPCN, while tiny and fast, lags behind in visual performance compared to newer models. LapSRN offers multi-scale super-resolution with a single pass, accommodating various scaling factors efficiently. However, it's slower than ESPCN and FSRCNN, and its accuracy falls short of EDSR's standards. Among these models, FSRCNN emerges as a compelling choice for upscaling images. While EDSR, LapSRN, and ESPCN possess their respective strengths, FSRCNN's combined advantages make it a valuable option for image upscaling tasks that require both efficiency and reasonable visual quality. 7 /24

8. Network Architecture Comparison of SRCNN and FSRCNN Image super-resolution is the process of enhancing the quality of low-resolution images to higher resolutions. Dong et al. first introduced the SRCNN in 2014, marking the use of convolutional neural networks in the field of super-resolution of images, which resulted in significant improvements in image upscaling. SRCNN's reliance on bicubic interpolated low-resolution images limits its effectiveness and capacity for learning. To overcome SRCNN's limitations, researchers like Dong et al. and Kim et al. proposed approaches like the Extended SRCNN and FSRCNN. FSRCNN offers a more efficient and compact model with a sub-pixel convolutional structure, striking a balance between efficiency and image quality. Comparing FSRCNN and SRCNN reveals the advantages of FSRCNN in terms of computational efficiency and image restoration. Fig. Network Architecture Comparison of SRCNN and FSRCNN 8 /24

9. Proposed Our Architecture of CFSRCNN ထို ဇယားကွက ် ကို ဂရစ်ပုံတွေ နဲ ့ Step by step ပြန်ပြရမယ ်။ 9 /24

10. Flowchart of Model Training Fig. Model Training Diagram 10 /24

11. Model Training Methods 11 /24

12. Adam Optimizer Adam Optimizer : The optimization algorithm Adam (Adaptive Moment Estimation) commonly utilized during training to update the neural network weights. It combines the ideas of both the RMSprop and AdaGrad algorithms and introduces momentum to speed up convergence. In deep learning applications, the Adam optimizer is frequently utilized because it works well with complex, large-scale models. The Adam optimizer's update rule is as follows:   Where, is the vector of weights at iteration t. is the loss's gradient with respect to at iteration t η is the learning rate, which controls the step size during weight updates and are hyperparameters that determine the first and second moments' exponential decay rates and are the gradients' first and second moments, respectively and are bias-corrected estimates of and ϵ is a small constant (usually a very small value) to avoid division by zero   12 /24

13. Mean Square Error (MSE) Mean Squared Error (MSE) Loss : For regression tasks, Mean Squared Error is a widely used loss function. The difference between actual (ground truth) and anticipated values is calculated using the average squared difference. With regard to deep learning, MSE is often used as a measure of how well a neural network is performing on a given task, and the goal during training is to minimize this loss. The MSE loss is calculated as follows:   Where, n is data points number (samples), is the true value (ground truth) of the i-th sample, is the predicted value for the i-th sample.   13 /24

14. Checkpoint and Evaluation Checkpoints : Model checkpoints are saved during training, and the best model (based on validation loss) is saved with a unique filename containing the epoch number and validation loss. After training 1500 epochs using CFSRCNN, the best result is achieved at epoch 1388. Dataset Epoch Validation Loss Validation Accuracy Own Dataset 1388/1500 19.9093 0.9747 Fig. The best result of trained model Evaluation : After training, the model is evaluated on the test set using the evaluation method. The evaluation result includes validation loss and validation accuracy. 14 /24

15. Flowchart of Model Testing Fig. Model Testing Diagram 15 /24

16. Experimental Results 16 /24

17. Comparison of Upscaling Image and Original Image Input Image Super Resolution (SR) Ground Truth (GT) Scale Comparison SR and GT Processing Time MSE SSIM PSNR 50x50 200x200 200x200 4x 55.28 0.99 30.70 dB 0.1s 200x200 800x800 800x800 4x 4.32 1.00 41.78 dB 0.4s Fig. Shows the graph of PSNR, SSIM and MSE the comparison results and procession time. 17 /24

18. Conclusion In summary, the researcher's own Custom Fast-Super- Resolution Convolutional Neural Network (CFSRCNN) architecture has proven to be a highly efficient and effective solution for 4x image upscaling from low-resolution inputs. The network's fast processing speed and computational efficiency make it appropriate for real-time and resource-constrained applications. So, this architecture can be used in surveillance areas, home security, and restricted areas for the detection of expected people and thieves by using CCTV. The success of this FSRCNN model represents a significant development in image super-resolution, enhancing both computer vision and image processing technology. 18 /24

19. Comments a n d Su gge s t i ons Thanks For Your Kind Attention! 19