Model for enhancing low-light kiwifruit flower images based on improved GAN

Uneven brightness and noise images have posed a great challenge on the recognition rate and accuracy of visual tasks, due mainly to the mutual occlusion of kiwifruit flowers in natural environments. In this article, the image enhancement was proposed to detect the kiwifruit flower under low light using improved generative adversarial network (GAN). The research object was taken as the flowers at the Meixian Kiwi Experimental Station of Northwest Agriculture and Forestry University, Shaanxi Province, China. Digital cameras and mobile phones were used to capture the images of flowers in different open states at some periods. All uniform pixel images were selected to create a dataset. Three steps were divided in the improved model of image enhancement. Firstly, GAN was used to optimize and improve the model. The channel and spatial attention were combined to perform the residual connections in the generator section. Among them, ECANet was used to more accurately extract the image features under the brightness distribution of specific areas in the channel attention. SAM was used to focus the specific regions in the image using lightweight computational operations in the spatial attention, in order to achieve the local attention. As such, the generator was obtained to extract the brightness distribution of kiwifruit low-light flower images. Secondly, a Swin Transformer block was added at the connection between the upsampling and downsampling of the generator. The Swin Transformer block was used as the standard multi-head self-attention evolution of the original Transformer. Therefore, the global modelling of the Transformer was also utilized to split the input image. The dependency relationship among each module of the image was then calculated to improve the global modelling and feature extraction of image details in the generator. Finally, the pattern collapse that encountered by single discriminators was reduced to adaptively enhance the local regions and global lighting. The generator was then provided with the required adaptive adjustment. A dual discriminator mechanism was utilized to enhance the perception of image details. The accuracy of image evaluation was improved to generate the clearer images in the generator network. On the self-built dataset, the PSNR and NIQE of the improved model were 7.09 and 10.36, respectively. Meanwhile, a comparison was then made on the five models, such as RetinexNet, Enlighten GAN, Zero DEC, Cycle GAN, and Diffusion Low Light. The peak signal-to-noise ratio of the improved model was 0.11db higher than that of EnlightenGan. The quality assessment of natural image was 12.41% higher than that of Zero DCE. Therefore, the low-light image enhancement model was performed the better quality, lower distortion, better naturalness, and smaller color deviation after enhancement. This improved model can also be applied in the practical production to develop the computer vision in smart agriculture.