Enhancing Dark Image Exposure
Author(s):
Aishwarya M, Shilpa M, Darshitha K N, Hemanth S Yadav, Indraja P
Keywords:
Frequency-Domain Approach, Convolutional Neural Network, Frequency-Domain Loss Function, Performance Metrics, Integration into Image Processing Pipelines, Robustness and Generalization.
Abstract
This project aims to enhance the visual quality of digital images through deep exposure correction using a frequency domain approach. Leveraging deep learning techniques, a convolutional neural network (CNN) is trained to automatically correct exposure issues in diverse datasets. The novel aspect of this project lies in the incorporation of a frequency-domain loss function, utilizing Fourier transforms to address specific characteristics of image frequencies. The model is trained on a carefully curated dataset, and hyperparameter tuning ensures optimal performance. The exposure correction tool is seamlessly integrated into image processing pipelines, providing a userfriendly interface for individual or batch image correction. Performance metrics, including mean squared error, PSNR, and SSI, are employed to quantify the improvement in exposure correction. The robustness and generalization of the model are evaluated across varying scenes and lighting conditions. Extensive documentation details the entire process, from data collection to model architecture, enabling users to effectively utilize and understand the exposure correction tool. Iterative refinement based on user feedback ensures the adaptability and continual improvement of the proposed solution in real-world applications. This project successfully implemented a deep exposure correction model with a unique frequency-domain loss function. The integration of Fourier transforms and careful dataset curation contributed to the model's robustness. Continuous refinement based on user feedback ensures adaptability. This project contributes to improving image quality and holds potential for diverse applications in digital imagery
Article Details
Unique Paper ID: 162138

Publication Volume & Issue: Volume 10, Issue 8

Page(s): 42 - 46
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