Contrast is an important feature in an image because the low-contrast effect causes poor details visibility, and the high-contrast effect leads to better-perceived details. The low-contrast effect happens due to many unavoidable reasons, and the demand for high-quality images entails the production of improved quality images. This obligates processing the low-contrast effect properly with an algorithm that works rapidly and preserves the essential image information. Many of the existing algorithms do not provide desirable results as they may amplify the brightness, deliver deficient colors, introduce insufficient contrast, or generate processing artifacts. Hence, a new HLIPSCS algorithm is introduced to avoid the aforesaid drawbacks and involves many processing concepts related to hyperbolic, logarithmic, and statistical approaches. It starts by applying two different hyperbolic methods, then their results are merged via an altered logarithmic approach. Next, statistical and contrast stretching approaches are applied to provide the anticipated outcome. The HLIPSCS algorithm is applied on different real contrast-degraded grayscale and color images, compared with eight different algorithms and the comparison outputs are evaluated using three different image evaluation methods. For the made comparisons and experiments, the HLIPSCS showed processing superiority as it delivered top performances in respect of image evaluation scores. Finally, the results of HLIPSCS have a way better appearance in the matters of brightness, contrast, and color representations.