Keywords : Image enhancement

Adapted Single Scale Retinex Algorithm for Nighttime Image Enhancement

Mohammad Khalil Ismail; Zohair Al-Ameen

AL-Rafidain Journal of Computer Sciences and Mathematics, 2022, Volume 16, Issue 1, Pages 59-69
DOI: 10.33899/csmj.2022.174407

 Images captured at night with low-light conditions frequently have a loss of visible details, inadequate contrast, low brightness, and noise. Therefore, it is difficult to perceive, extract, and analyze important visual information from these images, unless they were properly processed. Different algorithms exist to process nighttime images, yet most of these algorithms are highly complex, generate processing artifacts, over-smooth the images, or do not improve the illumination adequately. Thus, the single scale retinex (SSR) algorithm is adopted in this study to provide better processing for nighttime images. The proposed algorithm starts by converting the color image from the RGB model to the HSV model and enhancing the V channel only while preserving the H and S channels. Then, it determined the image’s illuminated version somewhat like the SSR, computes the logarithms of the illuminated and original images, then subtracts these two images by utilizing an altered procedure. Next, a modified gamma-adjusted Rayleigh distribution function is applied, and its outcome is processed once more by an automatic linear contrast stretching approach to produce the processed V channel that will be utilized with the preserved H and S channels to generate the output RGB image. The developed algorithm is assessed using a real dataset of nighttime images, evaluated using three dedicated image evaluation methods, and compared to ten dissimilar contemporary algorithms. The obtained results demonstrated that the proposed algorithm can significantly improve the perceptual quality of nighttime images and suppress artifact generation rapidly and efficiently, in addition to showing the ability to surpass the performance of different existing algorithms subjectively and objectively.

Rapid Contrast Enhancement Algorithm for Natural Contrast- Distorted Color Images

Asmaa Y. Albakri; Zohair Al-Ameen

AL-Rafidain Journal of Computer Sciences and Mathematics, 2021, Volume 15, Issue 2, Pages 73-90
DOI: 10.33899/csmj.2021.170012

Digital images are often obtained with contrast distortions due to different factors that cannot be avoided on many occasions. Various research works have been introduced on this topic, yet no conclusive findings have been made. Therefore, a low-intricacy multi-step algorithm is developed in this study for rapid contrast enhancement of color images. The developed algorithm consists of four steps, in that the first two steps include separate processing of the input image by the probability density function of the standard normal distribution and the softplus function. In the third step, the output of these two approaches is combined using a modified logarithmic image processing approach. In the fourth step, a gamma-controlled normalization function is applied to fully stretch the image intensities to the standard interval and correct its gamma. The results obtained by the developed algorithm have an improved contrast with preserved brightness and natural colors. The developed algorithm is evaluated with a dataset of various natural contrast degraded color images, compared against six different techniques, and assessed using three specialized image evaluation methods, in that the proposed algorithm performed the best among the comparators according to the used image evaluation methods, processing speed and perceived quality.

Hybrid Genetic Algorithm with Filters to Image Enhancement

Baydaa S Bhnam

AL-Rafidain Journal of Computer Sciences and Mathematics, 2013, Volume 10, Issue 4, Pages 107-119
DOI: 10.33899/csmj.2013.163550

Image enhancement  is a useful and necessary part of image processing and its analysis. The quality of an image could be corrupted by different kinds of noises, added due to the undesired conditions or during the transmission.
In this paper, a Hybrid Genetic Algorithm with Filters (HGAF ) is suggested for the removing of impulse noise from digital images. The new suggested algorithm HGAF uses popular (mean , median and min-max filters) and other proposed filters as fitness function for it in order to design eight proposed genetic filters. These eight proposed genetic filters are applied on several gray images corrupted by two types of noise (salt-and-pepper and gaussian noises) with different levels for comparison and to show the effectiveness of them by using the Peak Signal to Noise Ratio (PSNR) and Root Mean Square Error (RMSE). Also, proposed two methods of parents selection to compare between them and types of crossovers and mutations that are used.

Using Genetic Algorithm to Reduce the Noise Effect on Images

Baydaa S Bhnam

AL-Rafidain Journal of Computer Sciences and Mathematics, 2012, Volume 9, Issue 2, Pages 127-142
DOI: 10.33899/csmj.2012.163723

This paper deals with a problem that concentrates on the noise removal that the images are affected from different resources employing Genetic Algorithm with filters. To achieve the aims of the paper, six types of genetic filters are suggested for noise removal from the images. These suggested genetic filters depending on filters (mean, median, min and max) as an objective function for them.
These suggested genetic filters are applied on several real images contaminated by two types of noise with different levels for comparison and to show the effectiveness of them. The result show that The fifth genetic filter that depends on the median filter as an objective function and heuristic crossover and  adding and subtracting mutation, gives the best results with RMSE=15.7243 and PSNR=24.1646 for Lena.bmp image and with RMSE=8.6197 and PSNR=29.4210 for girl.png image when add 0.05 salt & paper noise.