Image Enhancement Techniques to Modify an Image with Machine Learning Application
Shiplu Das, Sohini Sen, Debarun Joardar, Gargi Chakraborty · 2024
Image enhancement is important in the field of image processing because it improves image quality by showcasing valuable information and trying to suppress unnecessary facts in the figure. The evolution of image improvement algorithms is examined in this paper. Image enhancement is the procedure of processing a picture to construct it more suitable for a particular application than the actual picture. These strategies have a diverse variety of uses in medical image filtering, such as cancer detection, and the tumor detection goal of image enhancement images with low contrast must be improved. These attempts to measure aided in establishing the spectral and spatial integrity of the reconstructed image. It is necessary to improve picture quality in order to improve these images for human viewing or further analysis. Image contrast enhancement is one technique for improving image quality. There are several techniques for enhancing contrast. Low-contrast images are typically captured in either dark or bright environments. As a result, pre-processing of such images is required to make them suitable for other image processing applications. This is where deep learning comes in. In this paper, we propose an ML-based image enhancement model that modifies an image to improve its visual quality while maintaining its semantic content. Our proposed model employs a deep learning framework, specifically a convolutional neural network (CNN), to learn and apply a set of image transformation filters. These filters are learned from a large dataset of images and can be used to perform various image enhancement operations, such as denoising, contrast adjustment, and color correction. To train our model, we use a combination of supervised and unsupervised learning methods. Specifically, we use a dataset of labeled images to train our CNN to perform specific enhancement tasks, such as increasing the brightness or sharpness of an image. Additionally, we use unsupervised learning techniques, such as autoencoders, to learn a representation of the underlying image features and apply them in the enhancement process. Our experimental results demonstrate that our proposed model outperforms existing image enhancement techniques in terms of visual quality and preservation of semantic content. We also show that our model can be used to enhance images for various machine learning applications, such as object detection and recognition, with improved accuracy and efficiency.