Image Super-Resolution with Deep Learning: Enhancing Visual Quality using SRCNN


  • Sesha Bhargavi Velagaleti, Shailaja Sanjay Mohite, Ravindra Sadashivrao Apare, Vipashi Kansal, A L N Rao, Amit Srivastava, Saloni Bansal, Anurag Shrivastava


Deep Learning, Image Super-Resolution, Quantitative Evaluation, SRCNN, Visual Quality Assessment.


This research digs into the space of Image Super-Resolution, particularly centring on the assessment and comparison of four noticeable calculations: SRCNN, FSRCNN, ESPCN, and VDSR. The study utilizes a different dataset enveloping different spaces, from common scenes to restorative imaging and satellite applications. Quantitative measurements, counting Top Signal-to-Noise Proportion (PSNR) and Structural Similarity Index (SSIM), and nearby visual quality evaluations were utilized to gauge the execution of each algorithm. SRCNN developed as the frontrunner, showing the most elevated PSNR of 28.7 and a commendable SSIM of 0.89. FSRCNN and ESPCN were closely taken after with PSNR values of 27.9 and 28.3, and SSIM scores of 0.88 and 0.87, separately. VDSR illustrated competitive execution with a PSNR of 27.5 and an SSIM of 0.86. These quantitative results were complemented by visual quality appraisals, where SRCNN got the most elevated rating of 9.2, followed by FSRCNN (8.8), ESPCN (8.7), and VDSR (8.5). This research contributes to the continuous exchange of computer vision, emphasizing the qualities and trade-offs of each calculation and giving important bits of knowledge into their pertinence over differing picture super-resolution scenarios.


Download data is not yet available.


CHANG, Y., CHEN, G. and CHEN, J., 2023. Pixel-Wise Attention Residual Network for Super-Resolution of Optical Remote Sensing Images. Remote Sensing, 15(12), pp. 3139.

CHUNG, M., JUNG, M. and KIM, Y., 2023. Enhancing Remote Sensing Image Super-Resolution Guided by Bicubic-Downsampled Low-Resolution Image. Remote Sensing, 15(13), pp. 3309.

GELADO, S.H., QUILODRÁN-CASAS, C. and CHAGOT, L., 2023. Enhancing Microdroplet Image Analysis with Deep Learning. Micromachines, 14(10), pp. 1964.

HAJIAN, A. and ARAMVITH, S., 2023. Fusion Objective Function on Progressive Super-Resolution Network. Journal of Sensor and Actuator Networks, 12(2), pp. 26.

HAN, L., ZHAO, Y., LV, H., ZHANG, Y., LIU, H., BI, G. and HAN, Q., 2023. Enhancing Remote Sensing Image Super-Resolution with Efficient Hybrid Conditional Diffusion Model. Remote Sensing, 15(13), pp. 3452.

KALLUVILA, A., 2023. Super-Resolution of Brain MRI via U-Net Architecture. International Journal of Advanced Computer Science and Applications, 14(5),.

KONG, Y. and LIU, S., 2024. DMSC-GAN: A c-GAN-Based Framework for Super-Resolution Reconstruction of SAR Images. Remote Sensing, 16(1), pp. 50.

KUMAR, L. and JAIN, M., 2022. A Novel Image Super-Resolution Reconstruction Framework Using the AI Technique of Dual Generator Generative Adversarial Network (GAN). Journal of Universal Computer Science, 28(9), pp. 967-983.

LAN, W. and CHANG, C., 2023. Research on Image Sharpness Enhancement Technology based on Depth Learning. International Journal of Advanced Computer Science and Applications, 14(2),.

MOHAMMAD-RAHIMI, H., VINAYAHALINGAM, S., MAHMOUDINIA, E., SOLTANI, P., BERGÉ, S.,J., KROIS, J. and SCHWENDICKE, F., 2023. Super-Resolution of Dental Panoramic Radiographs Using Deep Learning: A Pilot Study. Diagnostics, 13(5), pp. 996.

REN, Z., ZHAO, J., CHEN, C., YAN, L. and MA, X., 2023. Dual-Path Adversarial Generation Network for Super-Resolution Reconstruction of Remote Sensing Images. Applied Sciences, 13(3), pp. 1245.

SEYDI, S.T. and AREFI, H., 2023. A COMPARISON OF DEEP LEARNING-BASED SUPER-RESOLUTION FRAMEWORKS FOR SENTINEL-2 IMAGERY IN URBAN AREAS. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, X-1/W1-2023, pp. 1021-1026.

TEMIZ, H., 2023. Enhancing the Resolution of Historical Ottoman Texts Using Deep Learning-Based Super-Resolution Techniques. Traitement du Signal, 40(3), pp. 1075-1082.

UMIRZAKOVA, S., MARDIEVA, S., MUKSIMOVA, S., AHMAD, S. and WHANGBO, T., 2023. Enhancing the Super-Resolution of Medical Images: Introducing the Deep Residual Feature Distillation Channel Attention Network for Optimized Performance and Efficiency. Bioengineering, 10(11), pp. 1332.

WEI, Z., BAI, Y., CHENG, R., HU, H., WANG, P., ZHANG, W. and ZHANG, G., 2023. Improved sparse domain super-resolution reconstruction algorithm based on CMUT. PLoS One, 18(8),.

XINYE, L. and ZEQIAN, C., 2022. Single image super-resolution reconstruction based on fusion of internal and external features. Multimedia Tools and Applications, 81(2), pp. 1589-1605.

Bani Ahmad, A. Y. A. ., Kumari, D. K. ., Shukla, A. ., Deepak, A. ., Chandnani, M. ., Pundir, S. ., & Shrivastava, A. . (2023). Framework for Cloud Based Document Management System with Institutional Schema of Database. International Journal of Intelligent Systems and Applications in Engineering, 12(3s), 672–678.

P. William, Anurag Shrivastava, Upendra Singh Aswal, Indradeep Kumar, Framework for Implementation of Android Automation Tool in Agro Business Sector, 2023 4th International Conference on Intelligent Engineering and Management (ICIEM), 10.1109/ICIEM59379.2023.10167328

P. William, Anurag Shrivastava, Venkata Narasimha Rao Inukollu, Viswanathan Ramasamy, Parul Madan, Implementation of Machine Learning Classification Techniques for Intrusion Detection System, 2023 4th International Conference on Intelligent Engineering and Management (ICIEM), 10.1109/ICIEM59379.2023.10167390

N Sharma, M Soni, S Kumar, R Kumar, N Deb, A Shrivastava, Supervised Machine Learning Method for Ontology-based Financial Decisions in the Stock Market, ACM Transactions on Asian and Low-Resource Language Information Processing.

Ajay Reddy Yeruva, Esraa Saleh Alomari, S Rashmi, Anurag Shrivastava, Routing in Ad Hoc Networks for Classifying and Predicting Vulnerabilities, Cybernetics and Systems, Taylor & Francis, 2023

P William, OJ Oyebode, G Ramu, M Gupta, D Bordoloi, A Shrivastava, Artificial intelligence based models to support water quality prediction using machine learning approach, 2023 International Conference on Circuit Power and Computing Technologie

J Jose, A Shrivastava, PK Soni, N Hemalatha, S Alshahrani, CA Saleel, An analysis of the effects of nanofluid-based serpentine tube cooling enhancement in solar photovoltaic cells for green cities, Journal of Nanomaterials 2023

K Murali Krishna, Amit Jain, Hardeep Singh Kang, Mithra Venkatesan, Anurag Shrivastava, Sitesh Kumar Singh, Muhammad Arif, Deelopment of the Broadband Multilayer Absorption Materials with Genetic Algorithm up to 8 GHz Frequency, Security and Communication Networks

P Bagane, SG Joseph, A Singh, A Shrivastava, B Prabha, A Shrivastava, Classification of malware using Deep Learning Techniques, 2021 9th International Conference on Cyber and IT Service Management (CITSM).

A Shrivastava, SK Sharma,Various arbitration algorithm for onchip (AMBA) shared bus multi-processor SoC, 2016 IEEE Students' Conference on Electrical, Electronics and Computer Science, SCEECS 509330

Gandomi, M. Haider, “Beyond the hype: Big data concepts, methods, and analytics”, International Journal of Information Management, vol. 35, no. 2, pp. 137-144, 2015.

Shrivastava, A., Chakkaravarthy, M., Shah, M.A..A Novel Approach Using Learning Algorithm for Parkinson’s Disease Detection with Handwritten Sketches. In Cybernetics and Systems, 2022

Shrivastava, A., Chakkaravarthy, M., Shah, M.A., A new machine learning method for predicting systolic and diastolic blood pressure using clinical characteristics. In Healthcare Analytics, 2023, 4, 100219

Shrivastava, A., Chakkaravarthy, M., Shah, M.A.,Health Monitoring based Cognitive IoT using Fast Machine Learning Technique. In International Journal of Intelligent Systems and Applications in Engineering, 2023, 11(6s), pp. 720–729

Shrivastava, A., Rajput, N., Rajesh, P., Swarnalatha, S.R., IoT-Based Label Distribution Learning Mechanism for Autism Spectrum Disorder for Healthcare Application. In Practical Artificial Intelligence for Internet of Medical Things: Emerging Trends, Issues, and Challenges, 2023, pp. 305–321

Boina, R., Ganage, D., Chincholkar, Y.D., .Chinthamu, N., Shrivastava, A., Enhancing Intelligence Diagnostic Accuracy Based on Machine Learning Disease Classification. In International Journal of Intelligent Systems and Applications in Engineering, 2023, 11(6s), pp. 765–774

Shrivastava, A., Pundir, S., Sharma, A., ...Kumar, R., Khan, A.K. Control of A Virtual System with Hand Gestures. In Proceedings - 2023 3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023, 2023, pp. 1716–1721

P. Srivastava, P. Choudhary, S. A. Yadav, A. Singh and S. Sharma, A System for Remote Monitoring of Patient Body Parameters, International Conference on Technological Advancements and Innovations (ICTAI), 2021, pp. 238-243,




How to Cite

Sesha Bhargavi Velagaleti, Shailaja Sanjay Mohite, Ravindra Sadashivrao Apare, Vipashi Kansal, A L N Rao, Amit Srivastava, Saloni Bansal, Anurag Shrivastava. (2024). Image Super-Resolution with Deep Learning: Enhancing Visual Quality using SRCNN. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 479–486. Retrieved from



Research Article