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Rectification of illumination is vital in enhancing the visibility of images acquired in suboptimal lighting conditions. Still, it remains a challenging yet intriguing process as many existing algorithms fail to meet expectations. Hence, a latent light manifestation (LLM) algorithm is introduced, beginning with a conversion to the HSV space, processing the V channel using the Naka-Rushton and exponential equations. Next, the two output images are blended using a two-step range-expansion blending approach. After that, a statistics-based tonality adjustment method and a dynamic range extension process are applied. Finally, a transfer to the RGB space is performed to produce the output image. The LLM is tested using three datasets, compared with eight contemporary algorithms, and the quality evaluations are carried out via five performance measures. The results denote that the developed LLM consistently outperforms existing algorithms, scoring the best according to the used measures and yielding visually pleasing results. [ABSTRACT FROM AUTHOR]

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Various available imaging systems are capturing images with deficient sharpness due to numerous unavoidable shortcomings. Perceiving and extracting information from such images is uneasy. Hence, it is required to process these images properly to produce sharper and clearer details. Many methods exist that can be used to increase the sharpness of digital images. Among such, the unsharp mask has gained high popularity due to its rapidness and simplicity. Still, this filter usually degrades the processed images by an overshoot effect, which appears around the edges as white shades. In this study, an anisotropic diffusion-based unsharp mask filter, so-called ADUSM, is proposed, in that the degraded image is filtered using an amended anisotropic diffusion filter rather than processing it only by a low-pass Gaussian filter. This modification permitted the attenuation of the overshoot artefact which yielded to obtain better quality results. The ADUSM is tested with several types of images and assessed with two adequate quality metrics. Many experiments indicated that the proposed filter can outperform different existing methods and produce satisfactory results with reasonable application time. [ABSTRACT FROM AUTHOR]

It is known that computed tomography (CT) images are corrupted by many degradations including: blurring, low contrast or noise due to different real-world limitations. Thus, it is necessary to filter these images before starting the diagnostic process. In recent years, extensive research has been carried out to reduce such undesirable degradations, in which substantial achievements have been attained from skillful researchers by providing various innovative methods. Such methods contributed significantly in improving the poor visual quality of CT images. In this article, a review about six contemporary methods for each of image enhancement, denoising and deblurring is provided due to the high-profile of CT images in the medical field. Hence, after the prevalent causes of the degradations are highlighted, adequate elucidations about the literature methods are delivered. Finally, an inclusive summary is provided. [ABSTRACT FROM AUTHOR]