Affordable photoacoustic imaging systems employing low-cost light sources such as LEDs, laser diodes, and VCSELs face inherent limitations in optical fluence, bandwidth, and detection sensitivity, resulting in low-SNR signals and characteristic artifacts. This chapter examines computational strategies that compensate for these hardware constraints, emphasizing efficient reconstruction, denoising, and artifact suppression methods tailored for low-fluence operation. Fast GPU- and FPGA-accelerated algorithms, reflection and reverberation correction, and AI-assisted enhancement are discussed as key enablers of high-quality imaging from minimal hardware. The integration of photoacoustic–ultrasound fusion is also highlighted as a straightforward computational means to improve anatomical context and clinical interpretability. Together, these approaches illustrate how optimized computation can bridge the performance gap between affordable and high-end PAI systems.