Although Transformers excel in global feature extraction, they often have limitations in capturing local image details, leading to the loss of some local lighting information and uneven overall lighting distribution. To address this issue, this study proposes a low-light image enhancement algorithm based on light perception enhancement and dense residual denoising. The proposed algorithm leverages the advantages of Transformer and convolutional neural networks and effectively enhances the visual quality and lighting uniformity of low-light images through a detailed light perception mechanism. The network architecture features a codec design that integrates multilevel feature extraction and attention fusion modules internally. The feature extraction modules capture both global and local image features, and the attention fusion modules filter and combine these features to optimize information transmission. Additionally, to address the issue of noise amplification in low-light image enhancement, the enhanced image denoising module effectively reduces the noise of the enhanced image using dense residual connection technology. The performance of the proposed algorithm in processing low-light images is evaluated via comparative experiments. The experimental results show that the proposed algorithm can not only improve the problem of uneven lighting but also significantly reduce image noise and achieve higher quality image output.