Near-infrared image sensors are widely used in fields such as material identification， machine vision， and autonomous driving. Lead sulfide colloidal quantum dot-based infrared photodiodes can be integrated with silicon-based readout circuits in a single step. Based on this， we propose a photodiode based on an n-i-p structure， which removes the buffer layer and further simplifies the manufacturing process of quantum dot image sensors， thus reducing manufacturing costs. Additionally， for the noise complexity in quantum dot image sensors when capturing images， traditional denoising and non-uniformity methods often do not achieve optimal denoising results. For the noise and stripe-type non-uniformity commonly encountered in infrared quantum dot detector images， a network architecture has been developed that incorporates multiple key modules. This network combines channel attention and spatial attention mechanisms， dynamically adjusting the importance of feature maps to enhance the ability to distinguish between noise and details. Meanwhile， the residual dense feature fusion module further improves the network"s ability to process complex image structures through hierarchical feature extraction and fusion. Furthermore， the pyramid pooling module effectively captures information at different scales， improving the network"s multi-scale feature representation ability. Through the collaborative effect of these modules， the network can better handle various mixed noise and image non-uniformity issues. Experimental results show that it outperforms the traditional U-Net network in denoising and image correction tasks.