Opportunities, From the Editors

Image processing techniques for enhanced visual quality Collection - Contribution highlights

This Collection focuses on research on adaptive algorithms and learning-based strategies that push the boundaries of visual quality enhancement.
Faija Miah Jul 29, 2026

Collection Overview

Processing techniques play a crucial role in improving the clarity, fidelity, and interpretability of visual content. With advances in deep learning and computational photography, modern methods enable enhancements such as noise reduction, super resolution, contrast optimization, artifact removal, and colour correction. These techniques support fields ranging from medical imaging and remote sensing to entertainment and surveillance. Challenges remain in handling diverse lighting conditions, maintaining natural appearance, or achieving real-time performance. 

This will be a Collection of original research papers and will be open for submissions from all authors – on the condition that the manuscripts fall within the scope of the Collection and of Scientific Reports more generally. Narrative review articles are also welcomed to our sister journal, Scientific Reviews. Submissions are welcomed until 11th November 2026.

Comments from the Guest Editors

"Visual quality is central to applications like medical imaging, auto-driving, photography, and video streaming, making this topic highly relevant. I am thrilled to lead this Collection with other peer editors, which will highlight innovative image processing techniques. It will serve as a go-to resource for the community, driving progress and offering researchers a high-visibility platform for their work." - Dr. Xingbo Dong

Contribution highlights

This study presents a transformer-based super-resolution network for degraded underground coal mine images. By combining local convolution and global attention through adaptive interaction modules, it improves detail reconstruction and image quality. Tests on coal mine and public datasets show consistently superior performance, achieving higher PSNR and SSIM than existing state-of-the-art methods.

A metaheuristic automated framework for quality improvement of CT imagery

This study proposes an automated framework to improve CT image quality by optimising the clip-limit parameter in contrast-limited adaptive histogram equalisation (CLAHE). Using the Whale Optimisation Algorithm and a perception-based image quality evaluator (PIQE), the method enhanced contrast while reducing visual distortion across 315 CT slices, improving diagnostic image quality.

Robust image quality evaluation in optical coherence tomography of skin using global, region-independent metrics

This study introduces an automated framework for objectively evaluating skin optical coherence tomography (OCT) image quality. By segmenting images into air, signal, and noise regions, it defines three global metrics (NFPR, gSNR, and gCN) that eliminate user bias, providing more consistent, reproducible, and reliable quality assessment than conventional ROI-based methods.

Continuous-surface 3D reconstruction from kilometer-range single-photon LiDAR using score-based priors
This study presents a unified framework for high-fidelity 3D reconstruction from long-range single-photon LiDAR data. By combining multimodal sensing with score-based priors and continuous-surface scene modelling, it overcomes sensor limitations and noise, enabling efficient data compression, super-resolution, high-resolution rendering, and robust imaging of complex, real-world environments.

Who is involved? 

Guest Editors:

  • Yasheng Chang, Suzhou City University, China
  • Xingbo Dong, Anhui University, China
  • Kankanala Srinivas, VIT-AP University, India
  • Zhuoyi Yin, Nanjing University of Science and Technology, China
  • Dawei Zhang, Zhejiang Normal University, China

Internal Team:

  • In-House Editor: Dr. Thomas Tischer, Scientific Reports, Germany
  • Commissioning Editor: Faija Miah, Fully OA Brands, Springer Nature, UK
  • Managing Editor: Chantale Davies, Fully OA Brands, Springer Nature, UK

Visit the Collection page to find out more about this Collection.