The technology development, combined with chip size reductions, has significantly increased power density. Furthermore, the power limitation in portable devices has increased the importance of power consumption considerably. Approximate computing as an emerging paradigm leverages the error resiliency of applications to decrease power consumption. In this paper, we propose four approximate 4:2 compressors. We utilize the gate diffusion input to achieve significant area reduction in the proposed compressors. Afterward, we employ these compressors in the structure of Dadda multipliers. Compared to the state-of-the-art works, the proposed compressors result in, on average, 45% and 42% higher accuracy and lower area, respectively. Circuit simulations were carried out at a 32-nm carbon nanotube field-effect transistor technology node by HSPICE. The comparisons between our proposed compressors and exact compressor indicate that there has been an average improvement of 55%, 60% and 46% in the number of transistors, power consumption, and delay, respectively. In addition, the proposed multipliers were used in three image processing applications, including image multiplication, sharpening, and smoothing. The results show up to 46% and 70% improvement in the mean structure similarity index metric and peak signal-to-noise ratio compared to the state-of-the-art.