Research on Infrared and Visible Image Fusion Based on Tetrolet Transform and Convolution Sparse Representation

Abstract

Image fusion is a visual enhancement technique that combines source images from different sensors to produce a more robust and informative fused image for subsequent processing or decision making. Infrared and visible light images share complementary properties that enable the production of robust and informative fused images. This paper proposed an infrared and visible image fusion method that improved the tetrolet framework to improve infrared and visible image fusion quality. First, the source image is enhanced by bicubic interpolation. The improved tetrolet transform then decomposes the enhanced source image; the high-frequency components are fused by convolutional sparse representation theory and combined with corresponding rules, and the low-frequency components are fused by defining ISER descriptors. Finally, we use the inverse transform to reconstruct the fused image. Qualitative and quantitative experimental results on five groups of typical infrared and visible image datasets demonstrate the proposed method's effectiveness. The proposed method exhibits better performances on subjective vision and objective indexes compared with the other state-of-the-art methods.INDEX TERMS Image fusion, improved tetrolet transform, convolutional sparse representation, ISER descriptor.

Night Vision Anti-Halation Algorithm of Different-Source Image Fusion Based on Low-Frequency Sequence Generation

“…The proposed algorithm is compared with IGFF [12], AP-SPCNN [13], FPDE [14], RE-IFS [15], TT-CSR [16], YUVWT [17], IHSDCT [18], and the experimental results are objectively evaluated by the adaptive partition quality evaluation method of night vision anti-halation fusion image [27].…”

“…In reference [15], the designed fusion rules based on regional energy (RE) and intuitionistic fuzzy sets (IFS) preserve the important target and texture information in the resulting image, respectively. Reference [16] adopts tetrolet transform (TT) to decompose the visible and infrared image and use convolutional sparse representation (CSR) to fuse the high-frequency components, which effectively improves the visual effect of the fused image.…”

FSADFuse: A Novel Fusion Approach to Infrared and Visible Images

“…For example, Li et al [22] proposed an image fusion algorithm based on lifting stationary wavelet transform and non-subsampled shearlet transform(LSWT-NSST), and the obtained fusion image has clear texture information and good visual perception. Feng et al [23] used tetrolet transform and convolution sparse representation, the complete target and background information in the original image can be retained. Bavirisetti et al [24]applied guided filtering to the proposed method, which could manipulate image features at various scales.…”