This paper introduces a novel, unconventional method to enhance the visual clarity of monochrome images derived from non-visual data sources. The approach involves a two-step process: image pseudo-colorization followed by decolorization. Surprisingly, this counterintuitive technique can significantly improve discernibility of image features, irrespective of their size, shape, or original visual prominence. The paper delves into the algorithmic details of this method and presents experimental results on a representative dataset of IR, X-ray, MRI, and ultrasound images. When disregarding factors related to natural image appearance (which are irrelevant in non-visual domains), this method outperforms conventional image enhancement techniques, including sophisticated ones, in terms of standard image quality criteria, i.e., sharpness, contrast, and overall detail perceptibility. This superiority is substantiated by both subjective evaluations and objective metrics. The success of this technique hinges on the careful selection of color maps and the application of a specific, recently proposed decolorization scheme. The technique is well-suited for various visual data analysis tasks in non-visual domains, primarily in AI-based solutions.