Insight Enhancement of Distortion Effects on the Quality in Imagery Environments

Despite advances in technology and processing, image enhancement remains an open research area. The quality of the images has a great impact on the processing, analysis, and recognition. Images are a kind of multimedia data that demand huge resources. The image quality affects further processing, and if carefully utilized, will result in a reduction in the resources required for a specific task. Despite advancements in imagery technologies, the acquisition and transmission of digital images and other factors possess some kind of distortion. This may be attributed to transmission noise, environmental effects, and poor-quality acquiring devices and cameras, to name a few. This study compares ten image enhancement techniques for three types of imagery. The images investigated represent three classes. Micro is represented by microscopic images, Macro is represented by Macroscopic satellite images, and Photography for ordinary images. The study considers compressed images, both lossy and lossless compression, and ordinary uncompressed images. Three distortion categories, mainly Noise, Blurring, and Contrast effects, are extensively experimented with and studied. Noise types investigated are Gaussian, Poisson, Salt and Pepper, and Speckle noises. Two blurring distortions, Average Blur and Gaussian Blur, are investigated. In addition to low contrast. Distortions are applied to each image, and ten enhancement filters are applied. These include Negative, De-blur Regularized, De-blur blind deconvolution, De-blur Lucy-Richardson, Contrast Stretching, Adaptive, Gray Level Slicing, Median, Digital, and Histogram Equalization. Enhancement key performance indicators, Peak Signal to Noise Ratio, and Structural Similarity Index were measured to judge the performance of the filters. The three classes of imagery experimented with a developed tool that facilitates the experimentation and gives the KPIs for each image with a friendly User Interface. Insight is gained, thus facilitating the choice for the enhancement method.

@article{Adlan2026,
  title     = {Insight Enhancement of Distortion Effects on the Quality in Imagery Environments},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {5},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Hanan Hassan Ali Adlan and Mona Alanazi and Kolood Alenezi and Muneera Aldhabbah and Muneera Alsubai and Rafa Bahobail and Reem Alharbi},
  doi       = {10.14569/IJACSA.2026.0170515},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170515}
}