Image Quality Enhancement of Scanned Photos:Comparison of Deep Learning Techniques (original) (raw)

ENHANCING IMAGE QUALITY WITH DEEP LEARNING: TECHNIQUES AND APPLICATIONS

Jurnal ELTIKOM, 2024

The emergence of deep learning has transformed numerous fields, particularly image processing, where it has substantially enhanced image quality. This paper provides a structured overview of the objectives, methods, re-sults, and conclusions of deep learning techniques for image enhancement. It examines deep learning methodolo-gies and their applications in improving image quality across diverse domains. The discussion includes state-of-the-art algorithms such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Autoencoders, highlighting their applications in medical imaging, photography, and remote sensing. These methods have demonstrated notable impacts, including noise reduction, resolution enhancement, and contrast improvement. Despite its significant promise, deep learning faces challenges such as computational complexity and the need for large annotated datasets. outlines future research directions to overcome these limitations and further advance deep learning's potential in image enhancement.

Image Restoration and Enhancement Using Deep Learning

International Journal of Engineering Applied Sciences and Technology

During the process of image acquisition, sometimes images are degraded because of various reasons like low resolution of camera, motion blur, noise etc. This paper presents the work associated with the Image Restoration & Enhancement techniques. The process of recovering degraded image is known as Image Restoration. Image restoration includes denoising image, image inpainting, etc. Here we proposed Convolution Neural Network (CNN) with Median Filter for removing noise, Region filling Exemplar Based Inpainting Algorithm for image inpainting. Image enhancement is one amongst the problem in image processing. Haze, low lighting etc. are the various problems in images. The aim of Image enhancement is to process an image such that result is more suitable than original image for specific application. Here for haze removal we implement dark channel prior algorithm and for lightning low-light image we proposed functions. Image enhancement improves the appearance of the image.

On the use of deep learning for blind image quality assessment

Signal, Image and Video Processing, 2017

In this work we investigate the use of deep learning for distortion-generic blind image quality assessment. We report on different design choices, ranging from the use of features extracted from pre-trained Convolutional Neural Networks (CNNs) as a generic image description, to the use of features extracted from a CNN fine-tuned for the image quality task. Our best proposal, named DeepBIQ, estimates the image quality by average-pooling the scores predicted on multiple sub-regions of the original image. Experimental results on the LIVE In the Wild Image Quality Challenge Database show that DeepBIQ outperforms the state-ofthe-art methods compared, having a Linear Correlation Coefficient (LCC) with human subjective scores of almost 0.91. These results are further confirmed also on four benchmark databases of synthetically distorted images: LIVE, CSIQ, TID2008 and TID2013.

Image restoration using deep learning

2016

We propose a new image restoration method that reduces noise and blur in degraded images. In contrast to many state of the art methods, our method does not rely on intensive iterative approaches, instead it uses a pre-trained convolutional neural network.

Image noise reduction by deep learning methods

International Journal of Electrical and Computer Engineering (IJECE), 2023

Image noise reduction is an important task in the field of computer vision and image processing. Traditional noise filtering methods may be limited by their ability to preserve image details. The purpose of this work is to study and apply deep learning methods to reduce noise in images. The main tasks of noise reduction in images are the removal of Gaussian noise, salt and pepper noise, noise of lines and stripes, noise caused by compression, and noise caused by equipment defects. In this paper, such noises as the removal of raindrops, dust, and traces of snow on the images were considered. In the work, complex patterns and high noise density were studied. A deep learning algorithm, such as the decomposition method with and without preprocessing, and their effectiveness in applying noise reduction are considered. It is expected that the results of the study will confirm the effectiveness of deep learning methods in reducing noise in images. This may lead to the development of more accurate and versatile image processing methods capable of preserving details and improving the visual quality of images in various fields, including medicine, photography, and video.

Noisy image enhancements using deep learning techniques

International Journal of Electrical and Computer Engineering (IJECE), 2024

This article explores the application of deep learning techniques to improve the accuracy of feature enhancements in noisy images. A multitasking convolutional neural network (CNN) learning model architecture has been proposed that is trained on a large set of annotated images. Various techniques have been used to process noisy images, including the use of data augmentation, the application of filters, and the use of image reconstruction techniques. As a result of the experiments, it was shown that the proposed model using deep learning methods significantly improves the accuracy of object recognition in noisy images. Compared to single-tasking models, the multi-tasking model showed the superiority of this approach in performing multiple tasks simultaneously and saving training time. This study confirms the effectiveness of using multitasking models using deep learning for object recognition in noisy images. The results obtained can be applied in various fields, including computer vision, robotics, automatic driving, and others, where accurate object recognition in noisy images is a critical component.

A New Framework for Evaluating Image Quality Including Deep Learning Task Performances as A Proxy

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022

IQUAFLOW is a framework that provides a set of tools to assess image quality. The user can add custom metrics that can be easily integrated and a set of unsupervised methods is offered by default. Furthermore, IQUAFLOW measures quality by using the performance of AI models trained on the images as a proxy. This also helps to easily make studies of performance degradation of several modifications of the original dataset, for instance, with images reconstructed after different levels of lossy compression; satellite images would be a use case example, since they are commonly compressed before downloading to the ground. In this situation, the optimization problem involves finding images that, while being compressed to their smallest possible file size, still maintain sufficient quality to meet the required performance of the deep learning algorithms. Thus, a study with IQUAFLOW is suitable for such case. All this development is wrapped in MLFLOW: an interactive tool used to visualize and summarize the results. This document describes different use cases and provides links to their respective repositories. To ease the creation of new studies, we include a cookiecutter 1 repository. The source code, issue tracker and aforementioned repositories are all hosted on GitHub 2 .

Improve of contrast-distorted image quality assessment based on convolutional neural networks

International Journal of Electrical and Computer Engineering (IJECE)

Many image quality assessment algorithms (IQAs) have been developed during the past decade. However, most of them are designed for images distorted by compression, noise and blurring. There are very few IQAs designed specifically for Contrast Distorted Images (CDI), e.g. Reduced-reference Image Quality Metric for Contrast-changed images (RIQMC) and NR-IQA for Contrast-Distorted Images (NR-IQA-CDI). The existing NR-IQA-CDI relies on features designed by human or handcrafted features because considerable level of skill, domain expertise and efforts are required to design good handcrafted features. Recently, there is great advancement in machine learning with the introduction of deep learning through Convolutional Neural Networks (CNN) which enable machine to learn good features from raw image automatically without any human intervention. Therefore, it is tempting to explore the ways to transform the existing NR-IQA-CDI from using handcrafted features to machine-crafted features using ...

Editorial: Introduction to the Issue on Deep Learning for Image/Video Restoration and Compression

2021

T HE huge success of deep-learning–based approaches in computer vision has inspired research in learned solutions to classic image/video processing problems, such as denoising, deblurring, dehazing, deraining, super-resolution (SR), and compression. Hence, learning-based methods have emerged as a promising nonlinear signal-processing framework for image/video restoration and compression. Recent works have shown that learned models can achieve significant performance gains, especially in terms of perceptual quality measures, over traditional methods. Hence, the state of the art in image restoration and compression is getting redefined. This special issue covers the state of the art in learned image/video restoration and compression to promote further progress in innovative architectures and training methods for effective and efficient networks for image/video restoration and compression. In the following, we provide a short overview of the state of the art in learned image and video ...

Single-Image Reflection Removal Using Deep Learning: A Systematic Review

IEEE Access, 2022

Images captured through the glass often consist of undesirable specular reflections. These reflections detected in front of the glass remarkably reduce the quality and visibility of the scenes behind it. The process of reflection removal from images through the glass has many important applications in computer vision projects. Recently deep learning-based methods are being utilized for reflection removal so widely. In this article, we proposed a systematic literature review on the topic of single-image reflection removal using deep learning methods which were published between the years 2015 to 2021. A total number of 1600 research papers were extracted from five different online databases and digital libraries (IEEE Xplore, Google Scholar, Science Direct, SpringerLink and ACM Digital Library). After following the study selection procedure, 25 research papers were selected for this systematic review. The selected research papers were then analyzed to answer 7 key research questions that we have come up with to comprehensively explore the use of deep learning and neural networks for single-image reflection removal. After reading this article, future researchers will have a solid idea in the research field and will be able to work on their own research. The results provided in this proposed systematic review illustrate the main challenges that are encountered by researchers in this field and recommend encouraging directions for future research work. This review will also be helpful for researchers in discovering accessible datasets that can be used as benchmarks for comparing their proposed deep learning techniques with other studies in this research area.