Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
MIRNet is a neural network architecture designed for image restoration, focusing on high-resolution outputs while capturing multi-scale contextual information. It achieves this by incorporating multi-scale residual blocks with multiple key components: parallel convolution streams to extract features at different resolutions, mechanisms for sharing information between these streams, and attention mechanisms for emphasizing important spatial and channel features. The architecture enables the network to maintain high-resolution representations throughout its depth, ensuring detailed outputs, while also benefiting from the context provided by lower-resolution data. This combination allows MIRNet to produce high-quality restored images across various tasks like denoising, super-resolution, and image enhancement. Extensive testing on five real-world benchmark datasets shows that MIRNet outperforms other state-of-the-art methods in terms of quality and versatility.
"Noise Suppressed Real-Time Image Enhancing Environment using Deep Learning", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 5, page no.168 - 173, May-2024, Available :http://www.ijrti.org/papers/IJRTI2405026.pdf
Downloads:
000205264
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator