The traditional dark channel dehazing algorithm is limited by its inability to adequately remove haze from the distal regions of large-format oblique images and suffers from low processing efficiency. To address these challenges， an improved dark channel prior-based dehazing algorithm for oblique images was proposed in this paper. Initially， a rapid compensation method for the dark channel was employed， utilizing block-level and pixel-level window weighted fusion to enhance transmittance estimation. Subsequently， the dark channel prior was integrated with the radiative transfer equation to establish a haze distribution model and calculate transmittance. The atmospheric light value was then estimated to complete the dehazing process. Furthermore， a parallel computing scheme was designed to enhance dehazing speed. Experimental results indicate that the proposed algorithm effectively mitigates the effects of uneven haze distribution， thus significantly improving the quality of hazy images. In scenes with dense haze， image entropy and average gradient values increase by approximately 22.7% and 30.0%， respectively. Additionally， the dehazing speed during batch processing is approximately 11 times faster than that of traditional dark channel algorithms， demonstrating a substantial enhancement in processing efficiency.