I. Introduction
Negative imaging is a fundamental operation in image editing, involving the reversal of pixel values by subtracting their intensity from a maximum value, creating new intensity values [1]. However, traditional precise subtractor methods demand a lot of computing power, making them unsuitable for systems with limited resources or real-time requirements. Approximate computing is an approach that emphasises energy efficiency over accuracy in computational results. This involves introducing controlled errors into the computation to reduce power consumption and increase processing speed. This method is particularly relevant in applications where minor impurities can be tolerated, such as image and signal processing, enabling more efficient and faster operation while conserving energy resources. In response, approximate subtractor circuits offer an alternative approach. This trade-off between precision and complexity makes them well-suited for tasks like generating negative images, where small deviations from absolute accuracy are acceptable [2–4]. Approximate subtractors improve the efficiency of producing negative images by taking advantage of this precision-complexity trade-off. This is especially important in situations where conserving resources and achieving real-time processing are crucial [5].