Noisy image enhancements using deep learning techniques

ABSTRACT


INTRODUCTION
Recognition of objects in images is an important task in the field of computer vision.However, real images often contain noise caused by various factors such as poor lighting, distortion, atmospheric effects, and other external noise.Noise can significantly complicate the process [1] of object recognition [2] and reduce model accuracy.In recent years, deep learning methods based on convolutional neural networks (CNNs) have received a lot of attention in the field of object recognition.These methods have shown high accuracy and the ability to extract complex features from images.However, the performance of deep learning on noisy images remains a challenge requiring further research.This paper proposes the use of multitasking models for object recognition in noisy images using deep learning methods.Multitasking models offer the solution to several related problems at the same time, including object recognition and noise filtering.This allows the model to learn and use noise information for more accurate object recognition.The paper considers various aspects of multitasking learning in the context of object recognition in noisy images.Including the choice of model architecture, optimization of the loss function, adaptation of training data, and selection of suitable methods for noise filtering.This work aims to study the effectiveness of multitasking models using deep learning methods for object recognition in noisy images.It is assumed that this approach can significantly improve the process of object recognition, even in conditions of strong noise.Techniques that have been considered include the use of data augmentation, the use of filters to eliminate noise, and the development of custom CNN architectures that can account for noise and learn from noisy data.Also, the influence of various types of noise on the process of object recognition and optimal approaches for their elimination were studied.In the following, experiments and analysis of the results are presented that confirm or refute the hypothesis about the advantages of multitasking models in recognizing objects in noisy images.The results obtained can have significant practical applications, since the development of efficient models for object recognition in noisy images can be useful in many areas, including computer vision, automatic control, medical diagnostics, and industry.
Sanqian et al. [3] proposes a method for translating noisy images into clean images using generative-adversarial networks.The method allows for improving the quality of noisy images before their further recognition.Li et al. [4] proposes a new convolutional neural network architecture for removing noise from images.The authors show that their method can be successfully used for preprocessing noisy images before recognizing objects.Shen et al. [5] propose a method for teaching object detection on noisy images.The method takes noise into account and applies augmentation to train the model to consistently recognize objects in noise conditions.
In study [6], a method is proposed to improve the recognition of objects in images with a low level of illumination.The method uses convolutional neural networks to improve image quality and increase the accuracy of object detection.In study [7], the problem of vascular segmentation on noisy retinal images is investigated.A method is proposed that takes into account noise and applies adaptive filters to improve the quality of vascular segmentation.The study [8] is an overview of deep learning methods for denoising images.The review covers various approaches used to train deep learning models for denoising and discusses their application in the context of object recognition.Soylu et al. [9] explores the problem of semantic object segmentation in noisy images.A method is proposed that uses fully convolutional networks that consider noise during training to improve the quality of segmentation.Zhang et al. [10] proposes a method for robust object detection in noisy images.The method uses deep neural networks with progressive feature extraction to account for noise and improve object detection accuracy.Zou et al. [11] investigates the problem of object tracking on noisy videos.A method is proposed that uses deep neural networks to understand and diagnose visual tracking systems under noisy conditions.Chen et al. [12] explores the problem of face recognition in noisy images.A method using generative adversarial networks (GANs) is proposed to improve image quality and increase the accuracy of face recognition in noisy conditions

METHOD
This paper proposes a multitasking learning architecture that solves two problems: image classification by context and image enhancement.Classification is performed for three classes: road camera, space, and medical images.The classification uses the ResNet 152 architecture [13]- [15] multitasking models [16]- [18] based on the ResNet-152 architecture can have multiple output layers corresponding to each task.Each output layer can be associated with a corresponding class or label for a classification problem.Shared layers and parameters between tasks can be shared, allowing the model to use common information to improve performance across all tasks.After image classification, additional classification for traffic images occurs.She divides them into subclasses: road images with raindrops, snowfall, and fog.After classification into road image subclasses, the AttentiveGAN submodel [19] is passed through, which detects and cleans images of noise such as snow marks, raindrops, and fog.Image classification [20]  Other medical and space imaging classes go through the super-resolution generative adversarial network (SRGAN) submodel [21]- [23].It performs image enhancement using generative adversarial networks (GANs).Upon completion of training, the model combines both tasks: classification and image enhancement using GAN [24], [25].The result is a single model that can perform both tasks: image context classification and image enhancement.Multi-tasking learning architecture Figure 1 performs two tasks, the first task is image classification by context, which contains three classes, such as road camera images, space images, and medical images.
ResNet 152 architecture is used for classification, and image enhancement is performed after classification.Also, after classifying the road image, it goes through the classification again, dividing it into sub-classes, such as road image with raindrops, road image with snowfall, and road image with fog.After  In this research work, a multi-task model was selected and trained on a joint dataset containing a variety of images affected by various types of noise and degradation.This choice allowed the model to share information and knowledge between different tasks, which in turn greatly improved its ability to analyze and reconstruct images.One of the key factors to improve the accuracy of object recognition in noisy images is the selection of the optimal convolutional neural network (CNN) architecture.The architectural design of CNNs has been carefully considered to accommodate noisy images, resulting in significant improvements in analysis and reconstruction performance.In particular, the use of convolutional layers specifically tailored to deal with noise has proven critical to achieving outstanding results.In addition, the study explored and applied models based on the concept of attention, such as attention network models (AttentiveGAN).These models have proven to be particularly effective at removing noise and restoring detail in noisy images.Attention mechanisms in the models made it possible to focus more accurately on important parts of the image, which contributed to improving the reconstruction quality.The study also included a thorough analysis and comparison of various quality metrics used to evaluate the results to ensure the effectiveness of the selected architectures and methods.These results confirmed that the choice of a multi-task model optimized for the characteristics of noisy images, as well as the use of attention network architectures, made a significant contribution to improving the accuracy of object recognition and restoration in noisy images.In summary, this study highlights the importance of selecting optimal architectural solutions and deep learning methods when working with noisy images and confirms their significant impact on the quality of analysis and reconstruction in the context of multi-task models.

CONCLUSION
This work explored the application of multitasking models using deep learning methods to improve noisy images.The goal was to solve the problem of distortion and reduction in image quality due to the presence of noise.Research has shown that multitasking models can solve these problems.By solving several related problems at the same time, the model can then be trained and use information about noise for more accurate object recognition.Multitasking models allow information to be shared between tasks, resulting in better generalization and improved performance.During the experiments and analysis of the results, it was confirmed that the use of multitasking models by deep learning methods on noisy images leads to an increase in accuracy in image enhancement.This opens up prospects for the application of such models in various fields, including computer vision, automatic control, medical diagnostics, and industry.However, it should be noted that the performance of multitasking models may depend on the nature of the noise, the algorithms used, and the architecture of the model.Further research may be aimed at optimizing these aspects to achieve even more accurate results.In general, the use of multitasking models by deep learning methods to improve noisy images is a promising area of research.This opens up new opportunities for developing more efficient recognition systems and creating high-precision solutions in noisy environments.

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ISSN: 2088-8708 Int J Elec & Comp Eng, Vol.14, No. 1, February 2024: 811-818 812 using the AttentiveGAN model requires two separate steps: training the AttentiveGAN model for image generation and feature extraction, and then image classification using a separate classifier model such as CNN.

ISSN: 2088- 8708 
Noisy image enhancements using deep learning techniques (Kuanysh Daurenbekov) 813 classifying the road image into subclasses, it goes through the AttentiveGAN submodels, identifying and removing noise from the images, such as snow marks, raindrops, and fog.The rest of the classes related to medical and satellite imagery go through the SRGANs submodel, and after training, a single model is derived that performs two tasks, such as classifying and image enhancement using GANs.