Abstract
Smartphones are now the most popular medium for photography. Smartphones can capture better images than the hardware they use thanks to Machine Learning-based computational photography. Even though modern phones have sophisticated image enhancement applications, older and aged phones lag behind for a variety of reasons. Lens quality degradation, which results in washed out, soft-looking images, is one of the most common issues seen in older devices. We propose a method for improving such images by using an attention-based multi-scale residual neural network trained on a synthetic dataset. We chose two smartphones: an old device that captures degraded images and a modern flagship that provides reference enhanced images. Then we used the bloom filter, contrast, and highlight adjustment to make the reference images appear degraded. Later, we trained the model using synthetically degraded images and tested it on a variety of older devices. We achieved a maximum Peak Signal to Noise Ratio (PSNR) score of 74 throughout our experiments. To evaluate the model's images, we used Blind Image Quality Assessment (BIQA) methods such as HyperIQA. Aside from correcting the lens issue, the model achieves comparatively better sharpness, contrast, and color processing. Our proposed method generalizes well and achieves up to 11.45% improvement on novel devices by utilizing a very limited amount of data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
How Many Photos Are There? 50+ Photos Statistics (2023). https://photutorial.com/photos-statistics/. Accessed 14 Jan 2023
Lee, J., et al.: On-device neural net inference with mobile GPUs. arXiv preprint arXiv:1907.01989 (2019)
Guo, C., et al.: Zero-reference deep curve estimation for low-light image enhancement. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1780–1789 (2020)
Kinoshita, Y., Kiya, H.: Hue-correction scheme based on constant-hue plane for deep-learning-based color-image enhancement. IEEE Access 8, 9540–9550 (2020)
Hui, Z., Wang, X., Deng, L., Gao, X.: Perception-preserving convolutional networks for image enhancement on smartphones. In: Leal-Taixé, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11133, pp. 197–213. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11021-5_13
Ignatov, A., et al.: PIRM challenge on perceptual image enhancement on smartphones: Report. In: European Conference on Computer Vision (ECCV) Workshops (2018)
Engin, D., Genc, A., Kemal Ekenel, H.: Cycle-dehaze: enhanced CycleGAN for single image dehazing. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 825–833 (2018)
Kuanar, S., Mahapatra, D., Bilas, M., Rao, K.R.: Multi-path dilated convolution network for haze and glow removal in nighttime images. In: Visual Computer, pp. 1–14 (2022)
Burger, H.C., Schuler, C.J., Harmeling, S.: Image denoising: can plain neural networks compete with BM3D? In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2392–2399 (2012)
Shen, L., Yue, Z., Feng, F., Chen, Q., Liu, S., Ma, J.: MSR-net: low-light image enhancement using deep convolutional network. arXiv preprint arXiv:1711.02488 (2017)
Lore, K.G., Akintayo, A., Sarkar, S.: LLNet: a deep autoencoder approach to natural low-light image enhancement. Pattern Recogn. 61, 650–662 (2017)
Yan, L., Fu, J., Wang, C., Ye, Z., Chen, H., Ling, H.: Enhanced network optimized generative adversarial network for image enhancement. Multimedia Tools Appl. 80, 14363–14381 (2021)
Zamir, S.W., et al.: Learning enriched features for real image restoration and enhancement. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12370, pp. 492–511. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58595-2_30
Charbonnier, P., Blanc-Féraud, L., Aubert, G., Barlaud, M.: Two deterministic half-quadratic regularization algorithms for computed imaging. In: 1st International Conference on Image Processing, vol. 2, pp. 168–172 (1994)
GIMP - GNU Image Manipulation Program. https://www.gimp.org/. Accessed 24 Feb 2023
Kumar, J., Chen, F., Doermann, D.: Sharpness estimation for document and scene images. In: 21st International Conference on Pattern Recognition (ICPR 2012), pp. 3292–3295 (2012)
Su, S., et al.: Blindly assess image quality in the wild guided by a self-adaptive hyper network. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3667–3676 (2020)
Hosu, V., Lin, H., Sziranyi, T., Saupe, D.: KonIQ-10k: an ecologically valid database for deep learning of blind image quality assessment. IEEE Trans. Image Process. 29, 4041–4056 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Hossain, M., Rakib, M.H., Nijhum, I.R., Rahman, T. (2024). Enhancing Image Quality of Aging Smartphones Using Multi-scale Selective Kernel Feature Fusion Network. In: Bennour, A., Bouridane, A., Chaari, L. (eds) Intelligent Systems and Pattern Recognition. ISPR 2023. Communications in Computer and Information Science, vol 1940. Springer, Cham. https://doi.org/10.1007/978-3-031-46335-8_4
Download citation
DOI: https://doi.org/10.1007/978-3-031-46335-8_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-46334-1
Online ISBN: 978-3-031-46335-8
eBook Packages: Computer ScienceComputer Science (R0)