In order to solve the problem of difficult identification of hot spot areas due to the low resolution of infrared images affected by noise during hot spot detection of photovoltaic panels an adaptive denoising method of mixed noise in infrared images based on principal component analysis was proposed. The method used adaptive window pre-processing algorithms to initially denoise the acquired hot spot infrared images filtering out the low-density salt and pepper noise in the images and reducing the impact of the noise signal on the subsequent selection of the noise reduction training set. Then principal component analysis based on block matching was used to reduce the dimensionality of the pre-processed image information to extract the main features of the signal and reduce the computational complexity in noise filtering. Finally the image was denoised for the second time using the linear minimum mean square error estimation to filter out the residual noise. In addition the image noise level was recalculated before the secondary denoising so that the final denoised image obtained a better visual effect. The experimental results showed that this method effectively removed the mixed noise in the photovoltaic hot spot infrared image. The objective evaluation index showed that the structural similarity of the image was maintained at 0. 9 when the noise was low. The peak signal-to-noise ratio of the image under the influence of high-density noise was increased by 2 dB on average compared with the modified Alpha mean filter algorithm. The image details were retained in the actual visual effect and the hot spot area could be observed significantly.