Cone-beam CT (CBCT) is widely used in adaptive radiotherapy (ART) but often suffers from image artifacts and poor soft tissue contrast, limiting its wider application in ART workflows including segmentation and dose calculation. In this work, we propose a conditional Iterative α-(de)Blending (cIADB) for CBCT image quality improvement. cIADB employs a deterministic blending-deblending mechanism that reduces sampling randomness, enabling more stable and efficient image generation compared to conventional conditional denoising diffusion probabilistic model (cDDPM), which relies on stochastic sampling. We comprehensively evaluate the proposed method on head-and-neck CBCTs across different training approaches and anatomical planes. Quantitative results demonstrate that cIADB achieves better performance compared to cDDPM in terms of PSNR, SSIM, and SSE, while qualitative assessments further confirm improved denoising effect and structural fidelity. Moreover, the lightweight inference process of cIADB facilitates its potential integration into ART workflows. Our study highlights the promise of deterministic IADB model as a robust solution for clinical CBCT enhancement.